DOCSLIB.ORG

Searching for Atmospheric Signals in States of Low Antarctic Sea Ice Concentration

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Searching for Atmospheric Signals in States of Low Antarctic Sea Ice Concentration Searching for atmospheric signals in states of low Antarctic sea ice concentration Meteorological Institute, Stockholm University MO9001 - Degree Project Aiden J¨onsson Supervisors Frida Bender Meteorological Institute, Stockholm University Abhay Devasthale Swedish Meteorological and Hydrological Institute 5 October 2018 Abstract The Antarctic sea ice region is relatively stable in extent from year to year and sees little long-term variability, the primary driver for its seasonal advance and retreat being the seasonal changes in advection of heat through the atmosphere. However, observations show a slight positive trend in its extent over recent decades. Recent work has built on the hypothesis that anomalous poleward moisture fluxes could be seen in concert with anomalous decreases in sea ice variability by providing evidence of this correlation in the Arctic sea ice region. In order to test this hypothesis and to investigate the atmospheric circulation patterns during states of low sea ice concentration in the Antarctic, records of de-seasonalized sea ice concentration anomalies are made for five regions of the Antarctic polar region, and composite distributions of variables of atmospheric circulation for the lowest 10th percentile of months with low mean sea ice concentration are compiled. Merid- ional moisture fluxes from these composites are tested against the entire population of meridional moisture fluxes using the Student's t-test with a confidence level of 95%, and the differences from the overall mean fields for atmospheric conditions during these cases are calculated. Of the five regions, the Ross Sea, Weddell Sea, and Pacific Ocean sections exhibit significant local moisture flux anomalies in the direction of the pole during months with low sea ice concentration, supporting the hypothesis that moisture transport into the polar region is important for the variability of sea ice in the Antarctic. The Bellingshausen - Amundsen Seas and Indian Ocean sectors show weak local signals of poleward moisture fluxes, indicating that there are other varying factors affecting the sea ice more heavily in these regions. Mean geopotential height anomalies during months with anomalously low sea ice concentration indicate that the Weddell Sea and Pacific Ocean regions are coupled with the positive phase of the Southern Annular Mode, while low sea ice concentration in the Indian Ocean as well as the Bellingshausen and Amundsen Seas regions show concur- rence with the negative phase. With general circulation models predicting a persistence of the positive phase of the Southern Annular Mode in a warming climate, it is important to understand how the Antarctic sea ice region responds to the phase of this oscillation. Acknowledgements To MISU, the most welcoming institution I've ever been a part of, and all of the bright and passionate souls there who work so hard there. To the authors of the OSI-450 database for producing a truly remarkable tool for studying sea ice, and for giving patient, positive and constructive answers to questions about their work. A great thanks to my supervisors: to Frida, for being a compassionate, supportive supervisor and motivating me at every step to carry out quality work, and to Abhay, for sharing his inspiring knowledge and enthusiasm for the subject of this project, and for his skillful analyses along the way. To my dad, for encouraging and aiding me along the road to reach the end of the masters degree. To Danielle, for being by my side throughout every step of the process, reminding me to eat while writing, and working together with me to make our home a safe and relaxing place to carry out my studies. 1 Contents 1 Introduction 4 2 Methodology 10 2.1 Sea Ice Concentration . 10 2.2 Historical Reanalysis . 13 2.3 Calculations and Data Processing . 14 3 Results 16 4 Discussion and Conclusions 29 4.1 Discussion . 29 4.2 Limitations . 31 4.3 Societal Implications . 33 4.4 Conclusions . 35 4.5 Suggestions for Further Study . 36 2 Glossary of abbreviations BS: Bellingshausen and Amundsen Seas region DMSP: Defense Meteorological Satellite Program ECMWF: European Center for Medium-Range Weather Forecasts ENSO: El Ni~no-SouthernOscillation ERA: ECMWF Reanalysis EUMETSAT: European Organization for the Exploitation of Meteorological Satel- lites GCM: General Circulation Model IO: Indian Ocean region OSI SAF: Satellite Application Facility on Ocean and Sea Ice PO: Pacific Ocean region RS: Ross Sea region SAM: Southern Annular Mode SH: Southern Hemisphere SIC: Sea Ice Concentration SO: Southern Oceans VINMF: Vertically Integrated Northward Moisture Flux WS: Weddell Sea region WVI: Water Vapor Intrusion 3 Section 1 Introduction The polar regions are expected to experience the greatest change in climate relative to lower and middle latitudes with a globally warming climate. This effect { which has been dubbed polar amplification { is predicted early on by models in the IPCC 1990 report (Chapman and Walsh, 1993) and that is continuing to gain observational evidence. The differences in climate responses between lower and higher latitudes is expected to be most evident in the Arctic polar region, which has seen the greatest warming of any region on the planet; in direct contrast, a quickly warming climate is not seen in the Antarctic (Collins et al., 2013). The dominant factors controlling the difference between the two hemispheres' polar regions are widely believed to be the mixing of heat into deep waters in the Southern Oceans (Marshall et al., 2014), the ice sheet's persistence on the land mass (Pachauri et al., 2014), and the surface height and orography on the continent (Salzmann, 2017), although all possible causes have not been investigated or quantified. Figure 1.1: Seasonal average Antarctic sea ice concentration fields for the years 1982-2010 (DJF: December-January-February, MAM: March-April-May, JJA: June-July-August, SON: September-October-November). 4 The sources of heat in the Antarctic polar region also differ greatly from the Arc- tic. The Arctic region's heat budget is highly dynamic and shifts heavily throughout the seasons: during the fall and winter, the Arctic Ocean adds heat to the atmosphere as radiative solar heating subsides, and when radiative solar flux begins to increase during spring and summer, the atmosphere's heat energy continues to increase while the ocean begins to gain heat from the atmosphere (due to the melting of sea ice and increased radiative heating from the subsequently lower albedo) (Serreze et al., 2007). Advection of heat into the Arctic polar region by atmospheric circulation is quite low. In the Antarctic, downward vertical motion from the stratosphere transports most of the heat, while hori- zontal eddies transport about 3 to 5 times less heat into the region (Rubin and Weyant, 1963); the primary contributors to observed temperature changes between seasons are increased warm air advection and sea ice melt, and the stratosphere and troposphere are radiatively cooled at all times of the year. The Antarctic sea ice region is annually consis- tent in extent and variations are relatively small: most of the Southern Ocean is ice-free during southern hemisphere (SH) summer months and reaches a minimum in February. For the maximum sea ice extent, there is little variability from year to year despite a very slightly positive positive trend in recent decades: + 1.0 ± 0.4% during the years 1979 to 2006 (Cavalieri and Parkinson, 2008)(Macalady and Thomas, 2017). This is in contrast to the Arctic, which is experiencing a strong negative trend in sea ice extent at - 3.4 ± 0.2% per decade (Comiso and Nishio, 2008)(Stroeve et al., 2007). Despite modest trends in the Antarctic, the region's stability and lower variability of sea ice from year to year may allow for the possibility of a clearer picture of meteorological variables' effects on sea ice with less difficulty in removing any signals of long-term trends. A narrow band near the coast of the Antarctic continent that comprises only 1.5% of the total Antarctic sea ice region's surface area at the maximum is responsible for nearly all of the formation of sea ice in the southern hemisphere (Massom and Stammerjohn, 2010). As well as the production of sea ice, much of the formation of Antarctic Bottom Water occurs within this area, making processes near the coast crucial and far-reaching in effects on the circulation of the world's oceans. In the coastal zone, sea ice formation is controlled mostly by the interface between the ocean and the continental ice sheet and ice bergs, the seasonal change in temperature, as well as katabatic winds over the continent. These katabatic winds affect sea ice production by transporting newly formed, unpacked 5 ice away from the coast; strong, sustaining katabatic winds more perpendicular to the coast are associated with high sea ice production (Massom et al., 2001). The process of seasonal melt in the entire sea ice is affected by the atmospheric processes during the melt season. Primarily, heat advection during the onset of spring sets the stage for the melting of winter sea ice, and speeding the process of melting is the breaking of the sea ice pack by storms. The frequency of cyclones in the region as well as wind speeds during winter months are negatively correlated with maximum sea ice thickness (Heil, 2006). Aside from the mechanical breaking of sea ice by high wind speeds, storm-induced ocean swells cause further fragmentation and may cause breakage even beyond the storm path (Langhorne et al., 2001). Although there is not much year-to-year variability in the seasonal cycle of Antarctic sea ice, there are slight changes in the cycle currently being observed: the date of the sea ice maximum (defined as the reaching of a maximum thickness of the ice sheet) is delayed by 0.43 days per year (Heil, 2006).
Load more

Recommended publications
  • Verification of a New NOAA/NSIDC Passive Microwave Sea-Ice
    RESEARCH/REVIEW ARTICLE Verification of a new NOAA/NSIDC passive microwave sea-ice concentration climate record Walter N. Meier,1 Ge Peng,2,3 Donna J. Scott4 & Matt H. Savoie4 1 Cryospheric Sciences Lab, Code 615, National Aeronautics and Space Administration Goddard Space Flight Center, Greenbelt, MD 20771, USA 2 Cooperative Institute for Climate and Satellites, North Carolina State University, Raleigh, NC, USA 3 Remote Sensing and Applications Division, National Oceanic and Atmospheric Administration National Climatic Data Center, 151 Patton Avenue, Asheville, NC 28801, USA 4 National Snow and Ice Data Center, University of Colorado, UCB 449, Boulder CO 80309, USA Keywords Abstract Sea ice; Arctic and Antarctic oceans; climate data record; evaluation; passive A new satellite-based passive microwave sea-ice concentration product microwave remote sensing. developed for the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) programme is evaluated via comparison with Correspondence other passive microwave-derived estimates. The new product leverages two Walter N. Meier, Cryospheric Sciences well-established concentration algorithms, known as the NASA Team and Lab, Code 615, National Aeronautics and Bootstrap, both developed at and produced by the National Aeronautics and Space Administration Goddard Space Space Administration (NASA) Goddard Space Flight Center (GSFC). The sea- Flight Center, Greenbelt, MD 20771, USA. ice estimates compare well with similar GSFC products while also fulfilling all E-mail: [email protected] NOAA CDR initial operation capability (IOC) requirements, including (1) self- describing file format, (2) ISO 19115-2 compliant collection-level metadata, (3) Climate and Forecast (CF) compliant file-level metadata, (4) grid-cell level metadata (data quality fields), (5) fully automated and reproducible processing and (6) open online access to full documentation with version control, including source code and an algorithm theoretical basic document.
    [Show full text]
  • List of Commonly Used Variables for Sea-Ice Studies
    (A) (B) Free Drift Linear Viscosity (C) (D) Ideal Plastic Viscous Plastic Collision Induced Rheology Figure 2.1: Schematic representation of the most commonly used rheologies includ- ing (A) free drift, (B) linear viscosity, (C) ideal and viscous plastic, and (D) collision induced. Modified from Washington and Parkinson (2005; Figure 3.24) 15 Figure 2.2: Schematic representation of the energy balance vertically through an ice pack. Modified from Washington and Parkinson (2005; Figure 3.21). is balanced along the air/snow, air/ice, snow/ice, and ice/ocean interfaces. The steady-state equation for the conservation of energy at the surface of ice covered water follows: 0 if T0 < Tf QH + QL + QLW + (1 α0)QSW I0 QLW + QG0 = (2.14) ↓ − ↓ − − ↑ Q if T = T M 0 f where I0 is the amount of solar radiation that penetratesthe snow/ice column, and Tf is the salinity dependent freezing point. The surface energy balance for the sea- ice zone will be equal to zero for surface temperatures below freezing (T0 < Tf ), otherwise melt will occur (Wadhams 2000, Washington and Parkinson 2005). It should be noted that for sea ice, the sensible and latent heat fluxes are positive downward ( ) (Washington and Parkinson 2005). ↓ The steady-state equation for the conservation of energy along the air/snow interface follows equation 2.14 for the snow surface and the values for emissivity, 17 albedo, and the conductive flux are specific to the snow surface ("s, αs, and QGs ). Snowmelt is dependent on surface temperature, which is that of the snow surface, and equals 0 for surface temperature below freezing.
    [Show full text]
  • Sea Ice Concentration Products Over Polar Regions with Chinese FY3C/MWRI Data
    remote sensing Article Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data Lijian Shi 1,2,*, Sen Liu 1,2,3, Yingni Shi 4, Xue Ao 1,2, Bin Zou 1,2 and Qimao Wang 1,2 1 National Satellite Ocean Application Service, Beijing 100081, China; [email protected] (S.L.); [email protected] (X.A.); [email protected] (B.Z.); [email protected] (Q.W.) 2 Key Laboratory of Space Ocean Remote Sensing and Application, MNR, Beijing 100081, China 3 Zhuhai Orbita Aerospace Science & Technology Co., Ltd., Zhuhai 519080, China 4 Independent Researcher, Mailbox No. 5111, Beijing 100094, China; [email protected] * Correspondence: [email protected]; Tel.: +86-010-8248-1859 Abstract: Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect.
    [Show full text]
  • The Step-Like Evolution of Arctic Open Water Michael A
    www.nature.com/scientificreports OPEN The step-like evolution of Arctic open water Michael A. Goldstein 1,2, Amanda H. Lynch 3,4, Andras Zsom5, Todd Arbetter3, Andres Chang3 & Florence Fetterer6 Received: 18 December 2017 September open water fraction in the Arctic is analyzed using the satellite era record of ice Accepted: 31 October 2018 concentration (1979–2017). Evidence is presented that three breakpoints (shifts in the mean) occurred Published: xx xx xxxx in the Pacifc sector, with higher amounts of open water starting in 1989, 2002, and 2007. Breakpoints in the Atlantic sector record of open water are evident in 1971 in longer records, and around 2000 and 2011. Multiple breakpoints are also evident in the Canadian and Russian halves. Statistical models that use detected breakpoints of the Pacifc and Atlantic sectors, as well as models with breakpoints in the Canadian and Russian halves and the Arctic as a whole, outperform linear trend models in ftting the data. From a physical standpoint, the results support the thesis that Arctic sea ice may have critical points beyond which a return to the previous state is less likely. From an analysis standpoint, the fndings imply that de-meaning the data using the breakpoint means is less likely to cause spurious signals than employing a linear detrend. In the most recent decade, summer minimum sea ice extent has retreated to levels not seen since the beginning of the satellite record1. Te confuence of opportunity and risk at the retreating ice edge2 raises critical questions as to how well we observe and simulate Arctic ice area and extent.
    [Show full text]
  • Characterization of Moisture Sources for Austral Seas and Relationship with Sea Ice Concentration
    atmosphere Article Characterization of Moisture Sources for Austral Seas and Relationship with Sea Ice Concentration Michelle Simões Reboita 1, Raquel Nieto 2 , Rosmeri P. da Rocha 3, Anita Drumond 4, Marta Vázquez 2,5 and Luis Gimeno 2,* 1 Instituto de Recursos Naturais, Universidade Federal de Itajubá, Itajubá 37500-903, Minas Gerais, Brazil; [email protected] 2 Environmental Physics Laboratory (EPhysLab), CIM-UVigo, Universidade de Vigo, 32004 Ourense, Spain; [email protected] (R.N.); [email protected] (M.V.) 3 Departamento de Ciências Atmosféricas, Universidade de São Paulo, São Paulo 05508-090, Brazil; [email protected] 4 Instituto de Ciências Ambientais, Químicas e Farmacêuticas, Universidade Federal de São Paulo, Diadema 09913-030, Brazil; [email protected] 5 Instituto Dom Luiz, Universidade de Lisboa, 1749-016 Lisboa, Portugal * Correspondence: [email protected] Received: 7 August 2019; Accepted: 12 October 2019; Published: 17 October 2019 Abstract: In this study, the moisture sources acting over each sea (Weddell, King Haakon VII, East Antarctic, Amundsen-Bellingshausen, and Ross-Amundsen) of the Southern Ocean during 1980–2015 are identified with the FLEXPART Lagrangian model and by using two approaches: backward and forward analyses. Backward analysis provides the moisture sources (positive values of Evaporation minus Precipitation, E P > 0), while forward analysis identifies the moisture sinks (E P < 0). − − The most important moisture sources for the austral seas come from midlatitude storm tracks, reaching a maximum between austral winter and spring. The maximum in moisture sinks, in general, occurs in austral end-summer/autumn. There is a negative correlation (higher with 2-months lagged) between moisture sink and sea ice concentration (SIC), indicating that an increase in the moisture sink can be associated with the decrease in the SIC.
    [Show full text]
  • A Spurious Jump in the Satellite Record: Has Antarctic Sea Ice Expansion Been Overestimated?
    Supplement of The Cryosphere, 8, 1289–1296, 2014 http://www.the-cryosphere.net/8/1289/2014/ doi:10.5194/tc-8-1289-2014-supplement © Author(s) 2014. CC Attribution 3.0 License. Supplement of A spurious jump in the satellite record: has Antarctic sea ice expansion been overestimated? I. Eisenman et al. Correspondence to: I. Eisenman ([email protected]) I. Eisenman et al.: Antarctic sea ice record S-1 Supplemental Discussion and Figures derestimating sea ice concentrations (Comiso et al., 1997). Both algorithms have empirically adjusted parameters that differ between the two hemispheres, and the parameters in S1 Detailed description of data and methods the Bootstrap algorithm also vary on a daily basis. Various steps go into processing the ice concentration data Here we discuss the ice concentration fields analyzed in this to intercalibrate across the transition from one sensor to an- study and the resulting time series of ice extent and ice area other and to fill in missing or identifiably erroneous pixels. that we calculate. Although a number of brief data gaps exist, the instruments have provided data for at least 20 days of every month (10 S1.1 Ice concentration days for SMMR) from November 1978 to present with the exception of December 1987 and January 1988, when the The ice concentration data sets considered in this study are SSM/I instrument was turned off between 3 December 1987 derived from passive microwave measurements from instru- and 13 January 1988 due to overheating issues. ments flown on a series of satellites. The Scanning Multi- The effective resolution (sensor footprint) of the mi- channel Microwave Radiometer (SMMR) was flown on the crowave measurements vary as a function of frequency, with NASA Nimbus 7 satellite and provided data between 26 Oc- the resolution of the most coarse frequency used by the Boot- tober 1978 and 20 August 1987, with the Bootstrap sea ice strap and NASA Team algorithms being approximately 40 concentration using the data between 1 November 1978 and km 70 km.
    [Show full text]
  • Towards Improved Sea Ice Initialization and Forecasting with the IFS
    844 Towards Improved Sea Ice Initialization and Forecasting with the IFS B. Balan Sarojini, S. Tietsche, M. Mayer, M. A. Balmaseda, and H. Zuo Research Department March 2019 Series: ECMWF Technical Memoranda A full list of ECMWF Publications can be found on our web site under: http://www.ecmwf.int/en/research/publications Contact: [email protected] c Copyright 2019 European Centre for Medium-Range Weather Forecasts Shinfield Park, Reading, RG2 9AX, England Literary and scientific copyrights belong to ECMWF and are reserved in all countries. This publication is not to be reprinted or translated in whole or in part without the written permission of the Director- General. Appropriate non-commercial use will normally be granted under the condition that reference is made to ECMWF. The information within this publication is given in good faith and considered to be true, but ECMWF accepts no liability for error, omission and for loss or damage arising from its use. Level-3 OSISAF sea ice cover and CS2-SMOS sea ice thickness constraints Contents 1 Introduction 3 2 Sea Ice Assimilation and Initialization4 2.1 Models and Methodology..................................5 2.2 Current operational Level-4 SIC issues and Level-3 SIC..................5 2.3 Observations and Ocean-Sea-Ice Assimilation experiments................7 2.4 Impact of improved observations on the sea ice state....................9 2.4.1 Level-3 sea ice cover assimilation versus Level-4 sea ice cover assimilation...9 2.4.2 Sea ice thickness constraint against Level-4 sea ice cover assimilation...... 11 3 Sea Ice Forecasting 11 3.1 Sea-ice related SEAS5 biases...............................
    [Show full text]
  • Assessment of Contemporary Satellite Sea Ice Thickness Products
    The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-197 Manuscript under review for journal The Cryosphere Discussion started: 24 September 2018 c Author(s) 2018. CC BY 4.0 License. Assessment of Contemporary Satellite Sea Ice Thickness Products for Arctic Sea Ice Heidi Sallila1, Joshua McCurry2,3, Sinéad Louise Farrell2,3 and Eero Rinne1 1Finnish Meteorological Institute, Helsinki, Finland 5 2Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA 3NOAA Laboratory for Satellite Altimetry, College Park, MD, USA Correspondence to: Heidi Sallila ([email protected]) Abstract. Advances in remote sensing of sea ice over the past two decades have resulted in a wide variety of satellite- derived sea ice thickness data products becoming publicly available. Selecting the most appropriate product is challenging 10 given objectives range from incorporating satellite-derived thickness information in operational activities, including sea ice forecasting, routing of maritime traffic, and search and rescue, to climate change analysis, longer-term modeling, prediction, and future planning. Depending on the use case, selecting the most suitable satellite data product can depend on the region of interest, data latency, and whether the data are provided routinely, for example via a climate or maritime service provider. Here we examine a suite of current sea ice thickness data products, collating key details of primary interest to end users. We 15 assess sea ice thickness observations derived from sensors onboard the CryoSat-2 (CS2), Advanced Very-High-Resolution Radiometer (AVHRR) and Soil Moisture and Ocean Salinity (SMOS) satellites. We evaluate the satellite-only observations with independent ice draft and thickness measurements obtained from the Beaufort Gyre Exploration Project (BGEP) upward looking sonars (ULS) and Operation IceBridge, respectively.
    [Show full text]
  • Long-Term Arctic Snow/Ice Interface Temperature from Special Sensor for Microwave Imager Measurements
    remote sensing Article Long-Term Arctic Snow/Ice Interface Temperature from Special Sensor for Microwave Imager Measurements Sang-Moo Lee 1 , Byung-Ju Sohn 1,* and Christian D. Kummerow 2 1 School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Korea; [email protected] 2 Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA; [email protected] * Correspondence: [email protected]; Tel.: +82-02-880-8166 Received: 22 August 2018; Accepted: 9 November 2018; Published: 12 November 2018 Abstract: The Arctic sea ice region is the most visible area experiencing global warming-induced climate change. However, long-term measurements of climate-related variables have been limited to a small number of variables such as the sea ice concentration, extent, and area. In this study, we attempt to produce a long-term temperature record for the Arctic sea ice region using Special Sensor for Microwave Imager (SSM/I) Fundamental Climate Data Record (FCDR) data. For that, we developed an algorithm to retrieve the wintertime snow/ice interface temperature (SIIT) over the Arctic Ocean by counting the effect of the snow/ice volume scattering and ice surface roughness on the apparent emissivity (the total effect is referred to as the correction factor). A regression equation was devised to predict the correction factor from SSM/I brightness temperatures (TBs) only and then applied to SSM/I 19.4 GHz TB to estimate the SIIT. The obtained temperatures were validated against collocated Cold Regions Research and Engineering Laboratory (CRREL) ice mass balance (IMB) drifting buoy-measured temperatures at zero ice depth.
    [Show full text]
  • Broadband Albedo of Arctic Sea Ice from MERIS Optical Data
    The Cryosphere, 14, 165–182, 2020 https://doi.org/10.5194/tc-14-165-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Broadband albedo of Arctic sea ice from MERIS optical data Christine Pohl1, Larysa Istomina1, Steffen Tietsche2, Evelyn Jäkel3, Johannes Stapf3, Gunnar Spreen1, and Georg Heygster1 1Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany 2European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, RG2 9AX, UK 3Leipzig Institute for Meteorology, University of Leipzig, Stephanstr. 3, 04103 Leipzig, Germany Correspondence: Christine Pohl ([email protected]) Received: 26 March 2019 – Discussion started: 22 May 2019 Revised: 14 November 2019 – Accepted: 2 December 2019 – Published: 22 January 2020 Abstract. Arctic summer sea ice experiences rapid changes scales and in temporal patterns. However, consistency in in its sea-ice concentration, surface albedo, and the melt point-to-point comparison is rather poor, with differences up pond fraction. This affects the energy balance of the re- to 0.20, correlations between 0.69 and 0.79, and RMSDs gion and demands an accurate knowledge of those surface in excess of 0.10. Differences in sea-ice concentration and characteristics in climate models. In this paper, the broad- cloud-masking uncertainties play a role, but most discrepan- band albedo (300–3000 nm) of Arctic sea ice is derived from cies can be attributed to climatological sea-ice albedo values MEdium Resolution Imaging Spectrometer (MERIS) optical used in ERA5. They are not adequate and need revising, in swath data by transforming the spectral albedo as an out- order to better simulate surface heat fluxes in the Arctic.
    [Show full text]
  • An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery
    data Article An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery Dexuan Sha 1, Xin Miao 2, Mengchao Xu 1, Chaowei Yang 1,* , Hongjie Xie 3 , Alberto M. Mestas-Nuñez 3 , Yun Li 1 , Qian Liu 1 and Jingchao Yang 1 1 Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA; [email protected] (D.S.); [email protected] (M.X.); [email protected] (Y.L.); [email protected] (Q.L.); [email protected] (J.Y.) 2 Department of Geography, Geology and Planning, Missouri State University, Springfield, MO 65897, USA; [email protected] 3 Center for Advanced Measurements in Extreme Environments and Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA; [email protected] (H.X.); [email protected] (A.M.M.-N.) * Correspondence: [email protected] Received: 9 March 2020; Accepted: 14 April 2020; Published: 17 April 2020 Abstract: Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented
    [Show full text]
  • Low-Cloud, Boundary Layer, and Sea Ice Interactions Over the Southern Ocean During Winter
    1JULY 2017 W A L L E T A L . 4857 Low-Cloud, Boundary Layer, and Sea Ice Interactions over the Southern Ocean during Winter CASEY J. WALL,TSUBASA KOHYAMA, AND DENNIS L. HARTMANN Department of Atmospheric Sciences, University of Washington, Seattle, Washington (Manuscript received 30 June 2016, in final form 19 January 2017) ABSTRACT During austral winter, a sharp contrast in low-cloud fraction and boundary layer structure across the Antarctic sea ice edge is seen in ship-based measurements and in active satellite retrievals from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), which provide an unprecedented view of polar clouds during winter. Sea ice inhibits heat and moisture transport from the ocean to the atmosphere, and, as a result, the boundary layer is cold, stable, and clear over sea ice and warm, moist, well mixed, and cloudy over open water. The mean low-cloud fraction observed by CALIPSO is roughly 0.7 over open water and 0.4–0.5 over sea ice, and the 2 low-cloud layer is deeper over open water. Low-level winds in excess of 10 m s 1 are common over sea ice. Cold advection off of the sea ice pack causes enhanced low-cloud fraction over open water, and thus an enhanced longwave cloud radiative effect at the surface. Quantitative estimates of the surface longwave cloud radiative effect contributed by low clouds are presented. Finally, 10 state-of-the-art global climate models with satellite simulators are compared to observations. Near the sea ice edge, 7 out of 10 models simulate cloudier conditions over open water than over sea ice.
    [Show full text]