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 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.

Associations among temperature, sea ice and phytoplankton bloom dynamics in the Barents Sea

2020 ◽  
Vol 635 ◽  
pp. 25-36 ◽  
Author(s):  
K Dong ◽  
ØK Kvile ◽  
NC Stenseth ◽  
LC Stige
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Barents Sea ◽  
Phytoplankton Bloom ◽  
Sea Surface ◽  
The Barents Sea ◽  
Ice Retreat ◽  
Sea Ice Retreat ◽  
Time Substitution

Variations in physical conditions caused by climate change are likely to have large influences on marine organisms, including phytoplankton. Here, we investigated associations between satellite-derived chlorophyll a data from the Barents Sea and 2 key abiotic factors: sea surface temperature and sea-ice concentration. Specifically, we investigated how climate variability, through the measured physical factors, associated with phytoplankton phenology between 1998 and 2014. Associations between sea surface temperature and phytoplankton bloom dynamics differed depending on the area. The spring phytoplankton bloom occurred earlier and had higher magnitude in warm compared to cold years in the northern part of the Barents Sea, but there was no significant association in the southern part. In seasonally ice-covered regions, the association between the timing of the sea-ice retreat and the phytoplankton peak was nonlinear: sea-ice retreat time before mid-May was not associated with bloom timing, whereas the phytoplankton bloom occurred before or immediately following the ice retreat when the ice retreated after mid-May. Although drivers that are relatively constant across years, such as insolation, probably influenced the spatial gradient in chlorophyll, a space-for-time substitution captured the predicted effects of sea-ice retreat on the timing and magnitude of the phytoplankton bloom quite well.

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 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 Satellite ice extent, sea surface temperature, and atmospheric methane trends in the Barents and Kara Seas

2018 ◽  
Author(s):  
Ira Leifer ◽  
F. Robert Chen ◽  
Thomas McClimans ◽  
Frank Muller Karger ◽  
Leonid Yurganov
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Barents Sea ◽  
Mixed Layer Depth ◽  
Kara Sea ◽  
Sea Surface ◽  
The South ◽  
The Barents Sea ◽  
Barents And Kara Seas

Abstract. Over a decade (2003–2015) of satellite data of sea-ice extent, sea surface temperature (SST), and methane (CH4) concentrations in lower troposphere over 10 focus areas within the Barents and Kara Seas (BKS) were analyzed for anomalies and trends relative to the Barents Sea. Large positive CH4 anomalies were discovered around Franz Josef Land (FJL) and offshore west Novaya Zemlya in early fall. Far smaller CH4 enhancement was found around Svalbard, downstream and north of known seabed seepage. SST increased in all focus areas at rates from 0.0018 to 0.15 °C yr−1, CH4 growth spanned 3.06 to 3.49 ppb yr−1. The strongest SST increase was observed each year in the southeast Barents Sea in June due to strengthening of the warm Murman Current (MC), and in the south Kara Sea in September. The southeast Barents Sea, the south Kara Sea and coastal areas around FJL exhibited the strongest CH4 growth over the observation period. Likely sources are CH4 seepage from subsea permafrost and hydrate thawing and the petroleum reservoirs underlying the central and east Barents Sea and the Kara Sea. The spatial pattern was poorly related to seabed depth. However, the increase in CH4 emissions over time may be explained by a process of shoaling of strengthening warm ocean currents that would also advect the CH4 to areas where seasonal deepening of the surface ocean mixed layer depth leads to ventilation of these water masses. Continued strengthening of the MC will further increase heat transfer to the BKS, with the Barents Sea ice-free in ~ 15 years. We thus expect marine CH4 flux to the atmosphere from this region to continue increasing.

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.

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