Year on ice gives climate insights

Perovich, D. K.; Andreas, Edgar L.; Curry, J. A.; Eiken, Hans Geir; Fairall, C. W.; Grenfell, Thomas C.; Guest, Peter; Intrieri, J. M.; Kadko, David; Lindsay, R. W.; McPhee, Miles G.; Morison, James H.; Moritz, Richard E.; Paulson, Clayton A.; Pegau, W. Scott; Persson, P. Ola G.; Pinkel, Robert; Richter-Menge, J.; Stanton, Timothy P.; Stern, Harry L.; Sturm, Matthew; Tucker, W. B.; Uttal, Taneil

doi:10.1029/eo080i041p00481-01

Cited by 192 publications

(190 citation statements)

References 5 publications

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“…To be sure, process studies ultimately are linked to a specific process and not a particular site or year. However, large, expensive programs such as the Study of the Heat Budget of the Arctic Ocean (SHEBA, Perovich et al 1999), typically only run for a single field season. Representations (so-called parameterizations) of important processes in numerical models are then based on the suite of interrelated processes and phenomena sampled at that particularly site in the given year.…”

Section: Is This Normal? Inter-annual Variability and Guidance From Likmentioning

confidence: 99%

Indigenous Knowledge and Sea Ice Science: What Can We Learn from Indigenous Ice Users?

Eicken

2010

SIKU: Knowing Our Ice

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Drawing on examples mostly from Inupiaq and Yupik sea ice expertise in coastal Alaska, this contribution examines how local and indigenous knowledge (LIK) can inform and guide geophysical and biological sea ice research. Part of the relevance of LIK derives from its linkage to sea ice use and the services coastal communities derive from the ice cover. As a result, indigenous experts keep track of a broad range of sea ice variables at a particular location. These observations are embedded into a broader worldview that speaks to both longterm variability or change and the system of values associated with ice use. The contribution examines eight different contexts in which transmission of LIK is particularly relevant. These include the role of LIK in study site selection and assessment of a sampling campaign in the context of inter-annual variability, the identification of rare or inconspicuous phenomena or events, the contribution by indigenous experts to hazard assessment and emergency response, the record of past and present climate embedded in LIK, and the value of holistic sea ice knowledge in detecting subtle, intertwined patterns of environmental change. The relevance of local, indigenous sea ice expertise in helping advance adaptation and responses to climate change as well as its potential role in guiding research questions and hypotheses are also examined. The challenges that may have to be overcome in creating an interface for exchange between indigenous experts and sea ice researchers are considered. Promising approaches to overcome these challenges include cross-cultural, interdisciplinary education, and the fostering of Communities of Practice.

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Section: Is This Normal? Inter-annual Variability and Guidance From Likmentioning

confidence: 99%

Indigenous Knowledge and Sea Ice Science: What Can We Learn from Indigenous Ice Users?

Eicken

2010

SIKU: Knowing Our Ice

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“…In this study we derive microwave surface emissivity from airborne radiometer data collected during the FIRE Arctic Clouds Experiment (FIRE ACE) and Surface Heat Budget of the Arctic (SHEBA)aircraft observing period Perovich et al, 1999a]. During May, June, and July 1998, two aircraft carrying microwave radiometers overflew the SHEBA ice camp.…”

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confidence: 99%

Variability of sea ice emissivity estimated from airborne passive microwave measurements during FIRE SHEBA

Haggerty¹

2001

J. Geophys. Res.

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Abstract. Passive microwave radiometers with frequencies ranging from 37 GHz to 220 GHz were flown over the Surface Heat Budget of the Arctic (SHEBA) experimental site in May and July 1998. These measurements were motivated by the possibility of determining cloud liquid water path, ice water path, and precipitation over sea ice from these frequencies. The comprehensive cloud data set collected in this experiment offers a unique opportunity for improving and adapting passive microwave retrieval methods for application to arctic clouds. However, retrieval of cloud properties from a downward looking radiometer requires an estimate of the surface emissivity and its spectral, spatial, and temporal variation. In this study, brightness temperature measurements are used to calculate sea ice emissivity at each frequency using ancillary aircraft data to characterize the atmosphere and obtain surface temperature. Surface emissivities on clear sky days during the FIRE Arctic Cloud Experiment (FIRE ACE) aircraft campaign have been calculated and compared with previous estimates cited in the literature. Average emissivity at nadir for snow-covered sea ice in the experimental region during late May is estimated as 0.89 at 37 GHz, 0.74 at 89 GHz, 0.72 at 90 GHz, 0.73 at 150 GHz, and 0.84 at 220 GHz. In early July the average nadir emissivity for melting sea ice is 0.86 for 37 GHz and 0.84 for 90 GHz. Estimates of emissivity at 50 ø off nadir are compared with previous satellite and ground-based measurements of dual polarized emissivities at 37 GHz and 90 GHz. Significant variability exists in published emissivity values due to variations in dielectric and physical properties of snow and ice, but our results fall within previously observed ranges. Uncertainties in the emissivity calculations are estimated, and the accuracy required for use of surface emissivity estimates in cloud retrieval methods is discussed.

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“…Next, Polar WRF was tested over the Arctic Ocean in order to improve the surface treatment of sea ice [Bromwich et al, 2009]. Polar WRF version 2.2 was evaluated against observations made at the drifting station SHEBA [Perovich et al, 1999;Uttal et al, 2002]. Fractional sea ice was implemented, specifying conditions associated with ice and open-water areas of sea ice grid boxes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis

Wilson¹,

Bromwich²,

Hines³

2011

J. Geophys. Res.

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[1] The Polar version 3.1.1 of the Weather Research and Forecasting model (WRF), a high-resolution regional scale model, is used to simulate conditions for the year December 2006 to November 2007. The goal is to compare model output of near-surface and tropospheric variables to observational data sets. The domain mirrors that of the Arctic System Reanalysis (ASR), an assimilation of model fields with Arctic observations being conducted partly by the Polar Meteorology Group of the Byrd Polar Research Center at Ohio State University. A key development in this Polar WRF study is the extension of the seasonal progression of sea ice albedo to the entire Arctic Ocean. The boundary conditions are specified by the NCEP Final global gridded analysis archive (FNL), a 1°× 1°global grid updated every 6 h. The simulations are performed in 48 h increments initialized daily at 0000 UTC, with the first 24 h discarded for model spin-up of the hydrologic cycle and boundary layer processes. Model large-scale variables of atmospheric pressure and geopotential height show good agreement with observations. Spatial distribution of near-surface air temperatures compares well with ERA-Interim despite a small negative bias in the station analysis. Surface dewpoint temperatures and wind speeds show small biases, but model skill is modest for near-surface winds. Tropospheric temperatures and wind speeds, however, agree well with radiosonde observations. This examination provides a benchmark from which to improve the model and guidance for further development of Polar WRF as ASR's primary model.

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Year on ice gives climate insights

Cited by 192 publications

References 5 publications

Indigenous Knowledge and Sea Ice Science: What Can We Learn from Indigenous Ice Users?

Indigenous Knowledge and Sea Ice Science: What Can We Learn from Indigenous Ice Users?

Variability of sea ice emissivity estimated from airborne passive microwave measurements during FIRE SHEBA

Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis

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