Review of In situ observed relationships between skin temperatures and 2 m air temperatures in the Arctic

Abstract. To facilitate the combined use of traditional 2 m air temperature (T2m) observations from weather stations in the Arctic and skin temperature (Tskin) observations from satellites the relationship between high latitude snow and ice Tskin and T2m is quantified. Multiyear data records of simultaneous Tskin and T2m from 20 different in situ sites have been analysed, covering the Greenland Ice Sheet (GrIS), sea ice in the Arctic Ocean, and coastal snow covered land in North Alaska. The diurnal and seasonal temperature variabilities and the impacts from clouds and wind on the T2m-Tskin differences are quantified. Considering all stations, T2m is on average 1.37 °C warmer than Tskin, with the largest differences at the GrIS stations (mean of diff. of 1.64 °C). Tskin and T2m are often highly correlated, and the two temperatures are almost identical (

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

10.5194/tc-2021-38 ◽

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.

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

10.5194/tc-2019-126 ◽

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.

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Warm Arctic Proglacial Lakes in the ASTER Surface Temperature Product

Remote Sensing ◽

10.3390/rs13152987 ◽

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.

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Erroneous Arctic Temperature Trends in the ERA-40 Reanalysis: A Closer Look

Journal of Climate ◽

10.1175/2010jcli4054.1 ◽

2011 ◽

Vol 24 (10) ◽

pp. 2620-2627 ◽

Cited By ~ 92

Author(s):

James A. Screen ◽

Ian Simmonds

Keyword(s):

Lower Troposphere ◽

The Arctic ◽

Extreme Caution ◽

Scientific Debate ◽

Weather Forecasts ◽

Temperature Trends ◽

Air Temperatures ◽

Arctic Temperature ◽

Arctic Climate Change

Abstract Atmospheric reanalyses can be useful tools for examining climate variability and change; however, they must be used cautiously because of time-varying biases that can induce artificial trends. This study explicitly documents a discontinuity in the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) that leads to significantly exaggerated warming in the Arctic mid- to lower troposphere, and demonstrates that the continuing use of ERA-40 to study Arctic temperature trends is problematic. The discontinuity occurs in 1997 in response to refined processing of satellite radiances prior to their assimilation into the reanalysis model. It is clearly apparent in comparisons of ERA-40 output against satellite-derived air temperatures, in situ observations, and alternative reanalyses. Decadal or multidecadal Arctic temperature trends calculated over periods that include 1997 are highly inaccurate, particularly below 600 hPa. It is shown that ERA-40 is poorly suited to studying Arctic temperature trends and their vertical profile, and conclusions based upon them must be viewed with extreme caution. Consequently, its future use for this purpose is discouraged. In the context of the wider scientific debate on the suitability of reanalyses for trend analyses, the results show that a series of alternative reanalyses are in broad-scale agreement with observations. Thus, the authors encourage their discerning use instead of ERA-40 for examining Arctic climate change while also reaffirming the importance of verifying reanalyses with observations whenever possible.

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STORM ICE OIL WIND WAVE WATCH SYSTEM (SIOWS): WEB GIS APPLICATION FOR MONITORING THE ARCTIC

Proceedings of International Conference "Managinag risks to coastal regions and communities in a changinag world" (EMECS'11 - SeaCoasts XXVI) ◽

10.31519/conferencearticle_5b1b93852689d4.28131112 ◽

2017 ◽

Author(s):

Alexander Myasoedov ◽

Sergey Azarov ◽

Ekaterina Balashova ◽

...

Keyword(s):

Satellite Data ◽

Wind Wave ◽

Arctic Region ◽

Web Gis ◽

The Arctic ◽

Flexible Tool ◽

In Situ Data ◽

Current State ◽

Watch System

Working with satellite data, has long been an issue for users which has often prevented from a wider use of these data because of Volume, Access, Format and Data Combination. The purpose of the Storm Ice Oil Wind Wave Watch System (SIOWS) developed at Satellite Oceanography Laboratory (SOLab) is to solve the main issues encountered with satellite data and to provide users with a fast and flexible tool to select and extract data within massive archives that match exactly its needs or interest improving the efficiency of the monitoring system of geophysical conditions in the Arctic. SIOWS - is a Web GIS, designed to display various satellite, model and in situ data, it uses developed at SOLab storing, processing and visualization technologies for operational and archived data. It allows synergistic analysis of both historical data and monitoring of the current state and dynamics of the "ocean-atmosphere-cryosphere" system in the Arctic region, as well as Arctic system forecasting based on thermodynamic models with satellite data assimilation.

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Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

Remote Sensing ◽

10.3390/rs13122283 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2283

Author(s):

Hyangsun Han ◽

Sungjae Lee ◽

Hyun-Cheol Kim ◽

Miae Kim

Keyword(s):

Random Forest ◽

Sea Ice ◽

Arctic Ocean ◽

Open Water ◽

The Arctic ◽

Atmospheric Effects ◽

Sea Ice Concentration ◽

Air Temperatures ◽

The Pacific ◽

Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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International Arctic Systems for Observing the Atmosphere: An International Polar Year Legacy Consortium

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-14-00145.1 ◽

2016 ◽

Vol 97 (6) ◽

pp. 1033-1056 ◽

Cited By ~ 30

Author(s):

Taneil Uttal ◽

Sandra Starkweather ◽

James R. Drummond ◽

Timo Vihma ◽

Alexander P. Makshtas ◽

...

Keyword(s):

United States ◽

The United States ◽

Satellite Observations ◽

The Arctic ◽

International Polar Year ◽

Network Measurements ◽

International Polar ◽

In Situ Observations ◽

Arctic Atmosphere

Abstract International Arctic Systems for Observing the Atmosphere (IASOA) activities and partnerships were initiated as a part of the 2007–09 International Polar Year (IPY) and are expected to continue for many decades as a legacy program. The IASOA focus is on coordinating intensive measurements of the Arctic atmosphere collected in the United States, Canada, Russia, Norway, Finland, and Greenland to create synthesis science that leads to an understanding of why and not just how the Arctic atmosphere is evolving. The IASOA premise is that there are limitations with Arctic modeling and satellite observations that can only be addressed with boots-on-the-ground, in situ observations and that the potential of combining individual station and network measurements into an integrated observing system is tremendous. The IASOA vision is that by further integrating with other network observing programs focusing on hydrology, glaciology, oceanography, terrestrial, and biological systems it will be possible to understand the mechanisms of the entire Arctic system, perhaps well enough for humans to mitigate undesirable variations and adapt to inevitable change.

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