The Contributions of Winter Cloud Anomalies in 2011 to the Summer Sea‐Ice Rebound in 2012 in the Antarctic

Wang, Yunhe; Yuan, Xiaojun; Bi, Haibo; Liang, Yingqiu; Huang, Haijun; Zhang, Zehua; Liu, Yanxia

doi:10.1029/2018jd029435

Cited by 12 publications

(10 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where Q e,ice and ω 0,ice are determined for averages over a lognormal distribution of particle radii corresponding to the effective radius r ice . For the fast preliminary retrieval, spheres were assumed for ice, and single-scattering parameters for each particle radius were calculated from Mie theory using the index of refraction of Warren et al (2008), based on a temperature of 266 K. For liquid, single-scattering parameters determined from temperature-dependent indices of refraction at temperatures of 240, 253, 263, and 273 K were used (Rowe et al, 2013;Zasetsky et al, 2005;Wagner et al, 2005). Letting T 1 be the temperature from this list that is closest to but lower than the cloud temperature and T 2 be the temperature closest to but higher than the cloud temperature, Q a,liq is given as the weighted sum:…”

Section: Fast Preliminary Retrievalmentioning

confidence: 99%

Toward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical properties

et al. 2019

View full text Add to dashboard Cite

Abstract. Improvements to climate model results in polar regions require improved knowledge of cloud properties. Surface-based infrared (IR) radiance spectrometers have been used to retrieve cloud properties in polar regions, but measurements are sparse. Reductions in cost and power requirements to allow more widespread measurements could be aided by reducing instrument resolution. Here we explore the effects of errors and instrument resolution on cloud property retrievals from downwelling IR radiances for resolutions of 0.1 to 20 cm−1. Retrievals are tested on 336 radiance simulations characteristic of the Arctic, including mixed-phase, vertically inhomogeneous, and liquid-topped clouds and a variety of ice habits. Retrieval accuracy is found to be unaffected by resolution from 0.1 to 4 cm−1, after which it decreases slightly. When cloud heights are retrieved, errors in retrieved cloud optical depth (COD) and ice fraction are considerably smaller for clouds with bases below 2 km than for higher clouds. For example, at a resolution of 4 cm−1, with errors imposed (noise and radiation bias of 0.2 mW/(m2 sr cm−1) and biases in temperature of 0.2 K and in water vapor of −3 %), using retrieved cloud heights, root-mean-square errors decrease from 1.1 to 0.15 for COD, 0.3 to 0.18 for ice fraction (fice), and 10 to 7 µm for ice effective radius (errors remain at 2 µm for liquid effective radius). These results indicate that a moderately low-resolution, surface-based IR spectrometer could provide cloud property retrievals with accuracy comparable to existing higher-resolution instruments and that such an instrument would be particularly useful for low-level clouds.

show abstract

Section: Fast Preliminary Retrievalmentioning

confidence: 99%

Toward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical properties

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The important geophysical parameters of sea ice (e.g., SIC, SIE, SIA, and SIT) can well reflect the change process of sea ice. Meanwhile, it is noteworthy that the melting and freezing of sea ice will be affected by several complicated factors, such as temperature, melt ponds, snow cover, and so forth (Polashenski et al, 2012; Wang, Yuan, et al, 2019; Webster et al, 2014). Combining with the Arctic temperature, Ke et al (2013) concluded that the annual average sea ice outer line area of the Arctic from 2003 to 2010 was negatively correlated with the annual average temperature, and the correlation coefficient was −0.81, indicating that the change of temperature had a significant effect on the freezing and thawing of sea ice.…”

Section: Resultsmentioning

confidence: 99%

Spatial and Temporal Variations of Arctic Sea Ice From 2002 to 2017

Wang

Zeng

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

The variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. In this paper, the latest sea ice data sets from the European Space Agency Climate Change Initiative were firstly combined to comprehensively analyze the spatial and temporal variation of Arctic sea ice concentration (SIC) and sea ice thickness (SIT) from 2002 to 2017. The results show that during this period, the SIC of the Kara Sea and the Barents Sea decreases the most significantly, reaching −1.11%•year −1. The Arctic annual average sea ice extent (SIE) and sea ice area (SIA) exhibit noticeable decreasing trends, reaching −0.0592 × 10 6 km•year −1 and −0.0628 × 10 6 km•year −1 , respectively. Both of Arctic SIE and SIA vary seasonally, and they reach the minimum values of 4.97 × 10 6 km 2 and 3.96 × 10 6 km 2 in September and the maximum values of 14.58 × 10 6 km 2 and 13.35 × 10 6 km 2 in March, respectively. Moreover, the Arctic SIE and SIA anomaly series from 2002 to 2017 were analyzed based on the linear regression method. It is found the most noticeable decreasing trends of the Arctic SIE and SIA are from July to October. The maximum variation of the SIE is −1.06 × 10 6 km 2 (10a) −1 , and the maximum variation of the SIA is −1.09 × 10 6 km 2 (10a) −1. The Arctic SIE and SIA present the smallest decreasing trends from April to May, which are −0.19 × 10 6 km 2 (10a) −1 and −0.2 × 10 6 km 2 (10a) −1 , respectively. The Arctic SIT also displays a pronounced decreasing trend with −0.015 m•year −1 in autumn and −0.018 m•year −1 in winter. The annual average SIT reaches a minimum of 1.28 m in 2013 and a maximum of 1.66 m in 2005. Particularly, the SIT of the Hudson Bay decreases the most significantly, reaching −0.051 m•year −1. Finally, the Arctic average SIT was tested by the Mann-Kendall method. It is found that the SIT has a sudden change in 2007, from a slight increase in 2003-2007 to a significant decrease in 2007-2017. Overall, the decreasing trend of the Arctic sea ice (both SIC and SIT) is significant in the past decade. These findings can contribute to future research on polar regions and global climate change.

show abstract

“…Noontime UV index refers to the UV index measured at minimum SZA of the day. The maximum noon UV indices of the spring-autumn seasons were 6 and 9 in -2018, respectively. In 2017-2018, the maximum noon UV index was measured both in the spring (October) and in the summer (January).…”

Section: First 2 Years Of Measurementsmentioning

confidence: 94%

“…Both UV and PAR affect micro-organisms living in Antarctic ice and the Southern Ocean (Deppeler and Davidson, 2017;Häder et al, 2014). Changes in the amount of aerosols and pollution, as well as changes in sea ice extension or ground albedo, are also reflected in both UV and VIS radiation time series (Fountoulakis et al, 2014;Wild, 2009). In 2013, the FMI installed Solar Light Model 501A radiometers at Marambio to monitor both incoming and outgoing UV radiation.…”

Section: Introductionmentioning

confidence: 99%

New continuous total ozone, UV, VIS and PAR measurements at Marambio, 64° S, Antarctica

Lakkala

Aun

Sánchez

et al. 2020

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. A ground-based ultraviolet (GUV) multi-filter radiometer was set up at Marambio, 64∘ S, 56∘ W, Antarctica, in 2017. The instrument continuously measures ultraviolet (UV) radiation, visible (VIS) radiation and photosynthetically active radiation (PAR). The measurements are designed for providing high-quality long-term time series that can be used to assess the impact of global climate change in the Antarctic region. The quality assurance includes regular absolute calibrations and solar comparisons performed at Marambio and at Sodankylä, Finland. The measurements continue observations at Marambio that were performed with (Norwegian Institute for Air Re-search UV Radiometer (NILU-UV) radiometers between 2000 and 2010 as part of the Antarctic NILU-UV network. These measurements are ideally suited for assessing the effects of the ongoing stratospheric ozone recovery on the ecosystem, as the data products include information on radiation at various wavelengths ranging from UV to VIS so that changes on biologically effective radiation due to ozone can be separated from those due to other factors. Data products include total ozone, photosynthetically active radiation (PAR), visible (VIS) radiation at 555 nm, UV index, UV irradiance at 5 channels, UVB and UVA dose rate and daily dose, and biologically weighted UV dose rate and daily dose, calculated with 10 different action spectra. The data from the last 5 d and the daily maximum UV index time series are plotted and updated daily on the following web page: http://fmiarc.fmi.fi/sub_sites/GUVant/ (last access: 17 April 2020). The first 2 years of UV measurements were very different in terms of the results: for October, November and December the monthly average of daily maximum UVB dose rates were clearly higher in 2018 than in 2017. The largest difference was observed in October, when the average of daily maximum UVB dose rates was 76 and 102 µW cm−2 in 2017 and 2018, respectively. Monthly averages of the 3 months were similar in 2018, while in 2017 the monthly average of October was lower than those of November and December. The VIS and PAR time series show that daily maxima in 2018–2019 exceed those in 2017–2018 during late spring and summer (mid-November–January). The studied dataset, including daily maximum irradiances at five UV channels and one VIS channel; daily maximum UVB, UVA, and PAR dose rates; noon UVB, UVA, and PAR dose rates; noon total column ozone; and UVB and UVA daily doses, is freely accessible at https://doi.org/10.5281/zenodo.3688700 (Lakkala et al., 2019).

show abstract

The Contributions of Winter Cloud Anomalies in 2011 to the Summer Sea‐Ice Rebound in 2012 in the Antarctic

Cited by 12 publications

References 78 publications

Toward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical properties

Toward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical properties

Spatial and Temporal Variations of Arctic Sea Ice From 2002 to 2017

New continuous total ozone, UV, VIS and PAR measurements at Marambio, 64° S, Antarctica

Contact Info

Product

Resources

About