the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Verification of parameterizations for clear sky downwelling longwave irradiance in the Arctic
Giandomenico Pace
Alcide di Sarra
Filippo Calì Quaglia
Virginia Ciardini
Tatiana Di Iorio
Antonio Iaccarino
Daniela Meloni
Giovanni Muscari
Claudio Scarchilli
Abstract. Ground-based high resolution observations of downward longwave irradiance (DLI), surface air temperature, water vapour surface partial pressure and column amount, zenith sky infrared (IR) radiance in the atmospheric window, and all-sky camera images are regularly obtained at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W), North-West Greenland. The datasets for the years 2017 and 2018 have been used to assess the performance of different empirical formulas to infer clear sky DLI. An algorithm to identify clear sky observations has been developed, based on value, variability, and persistence of zenith sky IR radiance. Seventeen different formulas to estimate DLI have been tested against the THAAO dataset, using the originally determined coefficients. The formulas which combine information on total column water vapour and surface air temperature appear to perform better than others, with a mean bias with respect to the measured DLI smaller than 1 W/m2 and a root mean squared error (RMSE) around 6 W/m2. Some formulas, specifically developed for the Arctic, are found to produce poor statistical results; this is attributed partly to limitations in the originally used dataset, which does not cover a whole year, or is relative to very specific conditions (i.e., the ice sheet). The bias displays a significant improvement when the coefficients of the different formulas are calculated using the THAAO dataset. The presence of two full years of data allows the investigation of the inter-annual variability, and the use of different years for the determination of the coefficients and the evaluation of results. The smallest values of the bias and RMSE reach 0.1 W/m2 and 5 W/m2, respectively. Overall, best results are found for formulas which use both surface parameters and total water vapour column, and have been developed from global datasets. Conversely, formulas which express the atmospheric emissivity as a linear function of the logarithm of the column integrated water vapour appear to poorly reproduce the observations at THAAO.
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Giandomenico Pace et al.
Status: open (until 30 Sep 2023)
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RC1: 'Comment on amt-2023-181', Christopher Cox, 08 Sep 2023
reply
Verification of parameterizations for clear sky downwelling longwave irradiance by Pace et al. uses data collected from Thule, Greenland, to evaluate parameterizations for estimating the downwelling longwave flux at the surface based on screen height meteorological measurements. I have not seen an intercomparison quite like this and I think the results are of interest and publishable in AMT. I have a few minor comments for the authors to consider before this manuscript is published.
- A major revision that expands this analysis to include the other YOPP supersites (https://www.polarprediction.net/key-yopp-activities/yoppsitemip/the-yopp-arctic-and-antarctic-supersites/) would make the conclusions more broadly interpretable. However, I respect the scope that the authors are setting and if they do not wish to expand the analysis, I think it would be beneficial to provide some additional text contextualizing the Thule area. For example, northwest Greenland is within a small sub-region of the Arctic where the atmosphere is generally drier (lower IWV) (e.g., Cox et al. 2012) and indeed in the vicinity of 1 cm IWV, there are spectral effects (Cox et al. 2015) that I suspect could impact derivation of coefficients for the parameterizations (more challenging still if one were to interpret Thule data as representative of higher elevations over the ice sheet). Clouds at Eureka, Canada, also in this dry region, are higher, colder, and thinner (e.g., Shupe et al. 2011) compared to much of the rest of the Arctic. Thus, Thule may not be an ideal analogue for either the ice sheet or for the Arctic as a whole. That does not to devalue the results presented here, but is relevant context for readers to understand.
- Long and Turner (2008) is essential reading on the topic presented here and should be referenced and considered for this study as well as future work (as indicated at L432-438).
- Could you explain in more detail (more quantitatively) the accuracy of your clear-sky detection method (L67-69)? Could you clarify if it is necessary to capture all instances of clear-sky or (I think) only to capture a large sample of confidently detected clear-sky? Could you clarify the sensitivity of the vulnerability in this method to assigning “clear-sky” to cases with high, cold, optically-thin (e.g. cirrus) clouds?
Some editorial comments:
- L37: Ohmura et al. (2001) is a good reference here as well.
- Figure 1: I assume the straight lines in 3 & 4 (just before 2018.75) and the bottom panel (between 2017.75 and 2018.00) are artifacts of the plotting technique? Some other short periods in year 2 bottom panel as well. Can these be removed so we can properly visualize data gaps?
- The word “which” is used frequently in places where the correct word is “that” (e.g., L16, 24, 25 but then throughout the text).
References
Cox, C. et al. (2012) A comparison of the atmospheric conditions at Eureka, Canada, and Barrow, Alaska (2006-2008). J. Geophys. Res., 117, D12204, https://doi.org/10.1029/2011JD017164
Cox, C. et al. (2015) Humidity trends imply increased sensitivity to clouds in a warming Arctic. Nat. Comms., 6, 10117, https://doi.org/10.1038/ncomms10117
Long, C. and D. Turner (2008) A method for continuous estimation of clear-sky downwelling longwave flux developed using ARM surface measurements. J. Geophys. Res., 113, D18206, https://doi.org/10.1029/2008JD009936
Ohmura, A. (2001) Physical basis for the temperature-based melt-index method, J. Appl. Meteorol., 40(4), 753 – 761, https://doi.org/10.1175/15200450(2001)040<0753:PBFTTB>2.0.CO;2
Shupe, M. et al. (2011) Clouds at Arctic atmospheric observatories. Part I: Occurrence and macrophysical properties. J. Appl. Meteorol. Clim., 50, 626-644, https://doi.org/10.1175/2010JAMC2467.1
Citation: https://doi.org/10.5194/amt-2023-181-RC1
Giandomenico Pace et al.
Data sets
Downward Shortwave Irradiance at the Thule High Arctic Atmospheric Observatory (THAAO_DSI) D. Meloni, A. Di Sarra, T. Di Iorio, G. Pace, G. Muscari, A. Iaccarino, and F. Calì Quaglia https://doi.org/10.13127/THAAO/DSI
Meteorological data at the Thule High Arctic Atmospheric Observatory (THAAO_Met) G. Muscari, A. Di Sarra, T. Di Iorio, G. Pace, D. Meloni, G. Sensale, F. Calì Quaglia, and A. Iaccarino https://doi.org/10.13127/THAAO/MET
Infrared Brightness Temperature at the Thule High Arctic Atmospheric Observatory (THAAO_IBT) G. Pace, G. Muscari, A. di Sarra, F. Calì Quaglia, D. Meloni, A. Iaccarino, and T. Di Iorio https://doi.org/10.12910/DATASET2023-001
Integrated Water Vapor measured by an HATPRO microwave radiometer at the Thule High Arctic Atmospheric Observatory (THAAO_IWV_HATPRO) G. Pace, G. Muscari, A. di Sarra, F. Calì Quaglia, D. Meloni, A. Iaccarino, and T. Di Iorio https://doi.org/10.12910/DATASET2023-002
Giandomenico Pace et al.
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