scholarly journals An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000–1849

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eric Samakinwa ◽  
Veronika Valler ◽  
Ralf Hand ◽  
Raphael Neukom ◽  
Juan José Gómez-Navarro ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Night Time ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Annual Component ◽  
Filter Approach

AbstractThis paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations.

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.

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 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

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

2017 ◽  
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 ◽  
Analog Method ◽  
Sea Ice Concentration ◽  
Ice Concentration

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 forcing for the downscaling of future climate experiment. 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 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 analog method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiment and some real-case applications using observations. With respect to other previously existing methods for SIC, the analog method is a substantial improvement for the bias correction of future sea–ice concentrations.

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.

A Statistical Reconstruction of Sea-Surface Temperature and Sea-Ice Concentration for the Last Glacial Maximum

2021 ◽  
Author(s):  
Lachlan Astfalck ◽  
Daniel Williamson ◽  
Niall Gandy ◽  
Lauren Gregoire ◽  
Ruza Ivanovic
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Last Glacial Maximum ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Spatio Temporal ◽  
The Last Glacial Maximum ◽  
The Last Glacial

<p>Recent geoscience and palaeoclimatic modelling advances in have seen an increasing demand for spatio-temporal reconstructions of climatic variables. Satisfactory reconstructions should consider all sources of information: both numerical model ensembles and measured data. The difficulty in modelling climatic variables often gives rise to a multiplicity of models due to large uncertainty in the inputs. Climate proxy-based measurements are similarly uncertain due to both measurement noise and reconstruction error. It is thus vital to provide a reconstruction methodology in which these uncertainties are appropriately quantified. Instead of utilising probability based approaches that can be very computationally demanding for geospatio-temporal problems, we have developed a new approach to do this utilising a second-order framework; namely, Bayes linear analysis. This framework avoids the explicit specification of probability distributions and allows reconstructions to be described simply by means and variances. Methodological advances are made to the traditional Bayes linear mechanics to allow for non-linearity. To demonstrate the methodology, average monthly spatial reconstructions of sea-surface temperature and sea-ice concentration are estimated for the Last Glacial Maximum (21 ka), combining PMIP3 and PMIP4 outputs and available palaeodata syntheses. The methodology presented is generalisable to many spatio-temporal quantities and is highly germane to the geoscience community. </p>

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