Feasibility of improving a priori regional climate model estimates of Greenland ice sheet surface mass loss through assimilation of measured ice surface temperatures

Navari, Mahdi; Margulis, S. A.; Bateni, Sayed M.; Tedesco, Marco; Alexander, Patrick; Fettweis, Xavier

doi:10.5194/tc-10-103-2016

Cited by 16 publications

(31 citation statements)

References 69 publications

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“…This instrument, aboard the Terra and Aqua satellites, has been collecting radiance data from 24 February 2000 to present. The surface temperature products of the Greenland Ice Sheet are used as a baseline to investigate surface temperature trends (e.g., Hall et al, 2012), to monitor melt events on the ice sheet (Hall et al, 2013), and as input for surface mass balance or snowpack modeling (Fréville et al, 2014;Shamir and Georgakakos, 2014;Navari et al, 2016). In this study, we use the MOD/MYD11 Collection 6 (C6) product, where MOD refers to the Terra MODIS product and MYD refers to the Aqua MODIS product.…”

Section: Remote Sensing Of Surface Temperature With Modismentioning

confidence: 99%

Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

2018

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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 and can also be assessed using remote sensing techniques. Remote sensing is especially valuable over the Greenland Ice Sheet, where few ground-based air temperature measurements exist. 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 to 18 July 2015, near Summit Station in Greenland, to study surface temperature using the following measurements: skin temperature measured by an infrared (IR) sensor, 2 m air temperature measured by a National Oceanic and Atmospheric Administration (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 studies of MODIS products that used 2 m air temperature for validation. This inversion is present during our study period 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 and a mean bias of −0.4 • C, spanning a range of temperatures from −35 to −5 • C (RMSE = 1.6 • C and mean bias = −0.7 • C prior to cloud masking). 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. We show that the apparent cold bias present in other comparisons of 2 m air temperature and MODIS surface temperature may be a result of the near-surface temperature inversion. Further investigation of how in situ IR skin temperatures compare to MODIS surface temperature at lower temperatures (below −35 • C) is warranted to determine whether a cold bias exists for those temperatures.

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Section: Remote Sensing Of Surface Temperature With Modismentioning

confidence: 99%

Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

2018

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“…The surface temperature products of the Greenland Ice Sheet are used as a baseline to investigate future warming trends (e.g. Hall et al 2012), to monitor melt events on the ice sheet (Hall et al, 2013), and as input for surface mass balance or snowpack modeling (Fréville et al, 2014;Shamir and Georgakakos, 2014;Navari et al, 2016). A number of validation studies present results acquired over various time scales and in different locations to 130 determine the accuracy of the MODIS surface temperature products in the cryosphere (Hall et al, 2004(Hall et al, , 2008Koenig and Hall, 2010;Westermann et al, 2012;Hachem et al, 2012;Shuman et al, 2014;Østby et al, 2014;Shamir and Georgakakos, 2014;Hall et al, 2015;Williamson et al, 2017).…”

Section: Remote Sensing Of Surface Temperaturementioning

confidence: 99%

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

Adolph

Albert

Hall

2017

Preprint

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10As 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 15 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 18July 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 20 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 25 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 nearsurface 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. 30The Cryosphere Discuss., https://doi

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“…Aside from the modifications listed below, the model setup and initialization are similar to those used in our previous work (Navari et al 2016) in © 2018 American Geophysical Union. All rights reserved.…”

Section: Regional Climate Model and Snow Physical Modelmentioning

confidence: 99%

“…The perturbed MAR based hourly forcings were used as input to the Crocus to generate prior estimates of mass fluxes which are the main components of reanalysis estimates of SMB. For a more detailed discussion about the input data and perturbation technique, the reader is referred to Navari et al (2016).…”

Section: Model Input Datamentioning

confidence: 99%

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Improving Greenland Surface Mass Balance Estimates Through the Assimilation of MODIS Albedo: A Case Study Along the K‐Transect

Navari

Margulis

Tedesco

et al. 2018

Geophysical Research Letters

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Key Points: A data assimilation technique was used to improve the simulated estimation of the surface mass balance of the Greenland ice sheet along the K-transect stations. A particle batch smoother technique was used to condition the prior estimates of surface mass balance on MODIS-derived albedo. Results show that data assimilation techniques can be used to reduce uncertainty of the modeled surface mass balance estimates.© 2018 American Geophysical Union. All rights reserved. AbstractEstimating the Greenland Ice Sheet (GrIS) surface mass balance (SMB) is an important component of current and future projections of sea level rise. Given the lack of in situ information, imperfect models, and under-utilized remote sensing data, it is critical to combine the available observations with a physically based model to better characterize the spatial and temporal variation of the GrIS SMB. This work proposes a data assimilation framework that yields SMB estimates that benefit from a state-of-the-art snowpack model (Crocus) and a 16-day albedo product. Comparison of our results against in-situ SMB measurements from the Kangerlussuaq transect shows that assimilation of 16-day albedo product reduces the root mean square error (RMSE) of the posterior estimates of SMB from 1240 millimeter water equivalent (mmWE/yr) to 230 mmWE/yr and reduces the bias from 1140 mmWE/yr to -20 mmWE/yr. Plain Language SummaryDiagnosing the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) is a critical objective, which, despite its importance, continues to contain large uncertainties from significant errors in modeled precipitation as well as errors related to sub-grid process representation. This work uses a data assimilation framework (which has not been used in estimations of the GrIS SMB) and a satellite-derived 16-day albedo product to improve the estimates of the SMB on the southern Greenland ice sheet. We used the K-transect point-scale SMB measurements to validate our results over the 2009-2010 hydrological year. The data assimilation technique (i.e., particle batch smoother) reduces the spatial root mean square error of SMB over the K-transect stations by 82% from 1240 millimeter water equivalent (mmWE)© 2018 American Geophysical Union. All rights reserved.to 231 mmWE and bias of the estimates by 98% from 1142 mmWE to -20 mmWE. It was shown that this methodology has the potential to resolve the spatial variability of the surface processes along the K-transect stations and surface albedo which is not resolved by the model at a resolution of 25km. The modularity of the algorithm makes it possible to combine nearly any land surface model with different satellite-based or ground-based measurements.

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Feasibility of improving a priori regional climate model estimates of Greenland ice sheet surface mass loss through assimilation of measured ice surface temperatures

Cited by 16 publications

References 69 publications

Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

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

Improving Greenland Surface Mass Balance Estimates Through the Assimilation of MODIS Albedo: A Case Study Along the K‐Transect

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