A New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community Atmosphere Model

2008 ◽  
Vol 21 (19) ◽  
pp. 5145-5153 ◽  
Author(s):  
James W. Hurrell ◽  
James J. Hack ◽  
Dennis Shea ◽  
Julie M. Caron ◽  
James Rosinski
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Surface Boundary ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Community Atmosphere Model ◽  
Mean Sea Surface ◽  
Atmosphere Model

Abstract A new surface boundary forcing dataset for uncoupled simulations with the Community Atmosphere Model is described. It is a merged product based on the monthly mean Hadley Centre sea ice and SST dataset version 1 (HadISST1) and version 2 of the National Oceanic and Atmospheric Administration (NOAA) weekly optimum interpolation (OI) SST analysis. These two source datasets were also used to supply ocean surface information to the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40). The merged product provides monthly mean sea surface temperature and sea ice concentration data from 1870 to the present: it is updated monthly, and it is freely available for community use. The merging procedure was designed to take full advantage of the higher-resolution SST information inherent in the NOAA OI.v2 analysis.

scholarly journals Analysis of 24 years of mesopause region OH rotational temperature observations at Davis, Antarctica – Part 2: Evidence of a quasi-quadrennial oscillation (QQO) in the polar mesosphere

2020 ◽  
Vol 20 (14) ◽  
pp. 8691-8708
Author(s):  
W. John R. French ◽  
Andrew R. Klekociuk ◽  
Frank J. Mulligan
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Meridional Wind ◽  
Sea Surface ◽  
Research Station ◽  
Data Sets ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration

Abstract. Observational evidence of a quasi-quadrennial oscillation (QQO) in the polar mesosphere is presented based on the analysis of 24 years of hydroxyl (OH) nightglow rotational temperatures derived from scanning spectrometer observations above Davis research station, Antarctica (68∘ S, 78∘ E). After removal of the long-term trend and solar cycle response, the residual winter mean temperature variability contains an oscillation over an approximately 3.5–4.5-year cycle with a peak-to-peak amplitude of 3–4 K. Here we investigate this QQO feature in the context of the global temperature, pressure, wind, and surface fields using satellite, meteorological reanalysis, sea surface temperature, and sea ice concentration data sets in order to understand possible drivers of the signal. Specifically, correlation and composite analyses are made with data sets from the Microwave Limb Sounder on the Aura satellite (Aura/MLS v4.2) and the Sounding of the Atmosphere using Broadband Emission Radiometry instrument on the Thermosphere Ionosphere Mesosphere Energetics Dynamics satellite (TIMED/SABER v2.0), ERA5 reanalysis, the Extended Reconstructed Sea Surface Temperature (ERSST v5), and Optimum-Interpolation (OI v2) sea ice concentration. We find a significant anti-correlation between the QQO temperature and the meridional wind at 86 km altitude measured by a medium-frequency spaced antenna radar at Davis (R2∼0.516; poleward flow associated with warmer temperatures at ∼0.83±0.21 K (ms−1)−1). The QQO signal is also marginally correlated with vertical transport as determined from an evaluation of carbon monoxide (CO) concentrations in the mesosphere (sensitivity 0.73±0.45 K ppmv−1 CO, R2∼0.18). Together this relationship suggests that the QQO is plausibly linked to adiabatic heating and cooling driven by the meridional flow. The presence of quasi-stationary or persistent patterns in the ERA5 data geopotential anomaly and the meridional wind anomaly data during warm and cold phases of the QQO is consistent with tidal or planetary waves influencing its formation, which may act on the filtering of gravity waves to drive an adiabatic response in the mesosphere. The QQO signal plausibly arises from an ocean–atmosphere response, and appears to have a signature in Antarctic sea ice extent.

scholarly journals Assessing bias corrections of oceanic surface conditions for atmospheric models

2019 ◽  
Vol 12 (1) ◽  
pp. 321-342 ◽  
Author(s):  
Julien Beaumet ◽  
Gerhard Krinner ◽  
Michel Déqué ◽  
Rein Haarsma ◽  
Laurent Li
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
General Circulation Models ◽  
Substantial Improvement ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Analogue Method

Abstract. Future sea surface temperature and sea-ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcings for the downscaling of future climate experiments. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly method and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea-ice concentration (SIC) are presented. For SIC, we also propose a new analogue method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiments and some real-case applications using observations. We find that with respect to other previously existing methods, the analogue method is a substantial improvement for the bias correction of future SIC. Consistency between the constructed SST and SIC fields is an important constraint to consider, as is consistency between the prescribed sea-ice concentration and thickness; we show that the latter can be ensured by using a simple parameterisation of sea-ice thickness as a function of instantaneous and annual minimum SIC.

scholarly journals Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty

2014 ◽  
Vol 27 (1) ◽  
pp. 57-75 ◽  
Author(s):  
Shoji Hirahara ◽  
Masayoshi Ishii ◽  
Yoshikazu Fukuda
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Specific Area ◽  
Sea Surface ◽  
Sampling Errors ◽  
Sea Ice Concentration ◽  
Sst Variability ◽  
Analysis Errors ◽  
Global Mean

Abstract A new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice–SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.

Climatic trends of sea surface temperature and sea ice concentration in the Barents Sea

2021 ◽  
Author(s):  
Bayoumy Mohamed ◽  
Frank Nilsen ◽  
Ragnheid Skogseth
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Seasonal Cycle ◽  
Barents Sea ◽  
Warming Trend ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration

<p>Sea ice loss in the Arctic region is an important indicator for climate change. Especially in the Barents Sea, which is expected to be free of ice by the mid of this century (Onarheim et al., 2018). Here, we analyze 38 years (1982-2019) of daily gridded sea surface temperature (SST) and sea ice concentration (SIC) from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) project. These data sets have been used to investigate the seasonal cycle and linear trends of SST and SIC, and their spatial distribution in the Barents Sea. From the SST seasonal cycle analysis, we have found that most of the years that have temperatures above the climatic mean (1982-2019) were recorded after 2000. This confirms the warm transition that has taken place in the Barents Sea over the last two decades. The year 2016 was the warmest year in both winter and summer during the study period.   </p><p>Results from the linear trend analysis reveal an overall statistically significant warming trend for the whole Barents Sea of about 0.33±0.03 °C/decade, associated with a sea ice reduction rate of about -4.9±0.6 %/decade. However, the SST trend show a high spatial variability over the Barents Sea. The highest SST trend was found over the eastern part of the Barents Sea and south of Svalbard (Storfjordrenna Trough), while the Northern Barents Sea shows less distinct and non-significant trends. The largest negative trend of sea ice was observed between Novaya Zemlya and Franz Josef Land. Over the last two decades (2000-2019), the data show an amplified warming trend in the Barents Sea where the SST warming trend has increased dramatically (0.46±0.09 °C/decade) and the SIC is here decreasing with rate of about -6.4±1.5 %/decade.  Considering the current development of SST, if this trend persists, the Barents Sea annual mean SST will rise by around 1.4 °C by the end of 2050, which will have a drastic impact on the loss of sea ice in the Barents Sea.   </p><p> </p><p>Keywords: Sea surface temperature; Sea ice concentration; Trend analysis; Barents Sea</p>

scholarly journals Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons

2017 ◽  
Vol 30 (20) ◽  
pp. 8179-8205 ◽  
Author(s):  
Boyin Huang ◽  
Peter W. Thorne ◽  
Viva F. Banzon ◽  
Tim Boyer ◽  
Gennady Chepurin ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
High Latitude ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Near Surface ◽  
Analysis Technique ◽  
Observation Instruments ◽  
Data Source

Abstract The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.

scholarly journals Assimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean

2015 ◽  
Vol 93 ◽  
pp. 22-39 ◽  
Author(s):  
Alexander Barth ◽  
Martin Canter ◽  
Bert Van Schaeybroeck ◽  
Stéphane Vannitsem ◽  
François Massonnet ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Ice Concentration ◽  
Sea Ice Drift
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