Regime shift in sea-ice characteristics and impact on the spring bloom in the Baltic Sea

Pärn, Ove; Friedland, René; Rjazin, Jevgeni; Adolf, Stips

doi:10.1016/j.oceano.2021.12.004

Cited by 7 publications

(4 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A decrease occurred in the Black Sea and the North Sea. These observations agree with other studies [69][70][71][72] . www.nature.com/scientificdata www.nature.com/scientificdata/ Averaging time series cross-correlations across the areas revealed overall similarities between the parameter trends.…”

Section: Technical Validationsupporting

confidence: 94%

Global-scale parameters for ecological models

2023

View full text Add to dashboard Cite

This paper presents a collection of environmental, geophysical, and other marine-related data for marine ecological models and ecological-niche models. It consists of 2132 raster data for 58 distinct parameters at regional and global scales in the ESRI-GRID ASCII format. Most data originally belonged to open data owned by the authors of this article but residing on heterogeneous repositories with different formats and resolutions. Other data were specifically created for the present publication. The collection includes 565 data with global scale range; 154 at 0.5° resolution and 411 at 0.1° resolution; 196 data with annual temporal aggregation over ~10 key years between 1950 and 2100; 369 data with monthly aggregation at 0.1° resolution from January 2017 to ~May 2021 continuously. Data were also cut out on 8 European marine regions. The collection also includes forecasts for different future scenarios such as the Representative Concentration Pathways 2.6 (63 data), 4.5 (162 data), and 8.5 (162 data), and the A2 scenario of the Intergovernmental Panel on Climate Change (180 data).

show abstract

Section: Technical Validationsupporting

confidence: 94%

Global-scale parameters for ecological models

2023

View full text Add to dashboard Cite

show abstract

“…Although we did not plan our first implementation of buoyancy-driven mixing mesocosms with the explicit objective to test any given plankton theory, useful inferences can be drawn from the comparison of the four treatments. In particular, the mean temperature, turbulent intensities, and experiment duration for the four treatments were similar to the conditions during the transition sequence in mixing regimes (chronologically, pelagic waters go from convection to stratification regimes via the transitional/isothermal and linearlystratified regimes) that occur in the spring in the Bothnian sea (Pärn et al, 2022). Most theories that explain the onset of spring blooms focus on this transition period.…”

Section: Contribution Of Experimental Convection Mixing To Plankton T...mentioning

confidence: 62%

Using convective mixing in mesocosms to study climate-driven shifts in phytoplankton community distributions

et al. 2023

View full text Add to dashboard Cite

With climate change predicted to alter water column stability and mixing across the world’s oceans, a mesocosm experiment was designed to ascertain how a natural phytoplankton community would respond to these changes. As a departure from other mesocosm experiments, we used heating and cooling to produce four different climate-inspired mixing scenarios ranging from well-mixed water columns representative of typical open turbulence (ϵ = 3 x 10-8 m2/s3) through to a quiescent water column with stable stratification (ϵ = 5 x 10-10 m2/s3). This method of turbulence generation is an improvement on previous techniques (e.g., grid, shaker, and aeration) which tend to produce excessive dissipation rates inconsistent with oceanic turbulence observations. Profiles of classical physical parameters used to describe turbulence and mixing (turbulent dissipation rate, buoyancy frequency, turbulent eddy diffusivity, Ozmidov scale) were representative of the profiles found in natural waters under similar mixing conditions. Chlorophyll-a profiles and cell enumeration showed a clear biological response to the different turbulence scenarios. However, the responses of specific phytoplankton groups (diatoms and dinoflagellates) did not conform to the usual expectations: diatoms are generally expected to thrive under convective, turbulent regimes, while dinoflagellates are expected to thrive in converse conditions, i.e., in stable, stratified conditions. Our results suggest that responses to mixing regimes are taxon-specific, with no overwhelming physical effect of the turbulence regime. Rather, each taxon seemed to very quickly reach a given vertical distribution that it managed to hold, whether actively or passively, with a high degree of success. Future studies on the effects of climate change on phytoplankton vertical distribution should thus focus on the factors and mechanisms that combine to determine the specific distribution of species within taxa. Our convection-based mesocosm approach, because it uses a primary physical force that generates turbulence in open waters, should prove a valuable tool in this endeavor.

show abstract

“…The SST phenology (i.e., seasonal timing) metrics were used to assess seasonal cycle changes [64], and summer transition and duration metrics were derived from daily SST data for each year, based on the first day and the number of consecutive days that exceeded the climatological summer mean (3.6 • C), respectively. The duration of the warm summer is defined as the length of the within-year period with SSTs greater than the threshold of 3.6 • C. The duration of the sea ice season was estimated according to [10,65,66]. The average duration of the sea-ice season is calculated by counting the number of days when SIC is at least 30% for each grid point for each year and then taking the spatial average of the number of ice-covered days for the Barents Sea.…”

Section: Statistical Analysesmentioning

confidence: 99%

Interannual and Decadal Variability of Sea Surface Temperature and Sea Ice Concentration in the Barents Sea

Mohamed

Nilsen²,

Skogseth³

2022

Remote Sensing

View full text Add to dashboard Cite

Sea ice loss and accelerated warming in the Barents Sea have recently been one of the main concerns of climate research. In this study, we investigated the trends and possible relationships between sea surface temperature (SST), sea ice concentration (SIC), and local and large-scale atmospheric parameters over the last 39 years (1982 to 2020). We examined the interannual and long-term spatiotemporal variability of SST and SIC by performing an empirical orthogonal function (EOF) analysis. The SST warming rate from 1982 through 2020 was 0.35 ± 0.04 °C/decade and 0.40 ± 0.04 °C/decade in the ice-covered and ice-free regions, respectively. This climate warming had a significant impact on sea-ice conditions in the Barents Sea, such as a strong decline in the SIC (−6.52 ± 0.78%/decade) and a shortening of the sea-ice season by about −26.1 ± 7.5 days/decade, resulting in a 3.4-month longer summer ice-free period over the last 39 years. On the interannual and longer-term scales, the Barents Sea has shown strong coherent spatiotemporal variability in both SST and SIC. The temporal evolution of SST and SIC are strongly correlated, whereas the Atlantic Multidecadal Oscillation (AMO) influences the spatiotemporal variability of SST and SIC. The highest spatial variability (i.e., the center of action of the first EOF mode) of SST was observed over the region bounded by the northern and southern polar fronts, which are influenced by both warm Atlantic and cold Arctic waters. The largest SIC variability was found over the northeastern Barents Sea and over the Storbanken and Olga Basin. The second EOF mode revealed a dipole structure with out-of-phase variability between the ice-covered and ice-free regions for the SST and between the Svalbard and Novaya Zemlya regions for SIC. In order to investigate the processes that generate these patterns, a correlation analysis was applied to a set of oceanic (SST) and atmospheric parameters (air temperature, zonal, and meridional wind components) and climate indices. This analysis showed that SST and SIC are highly correlated with air temperature and meridional winds and with two climate indices (AMO and East Atlantic Pattern (EAP)) on an interannual time scale. The North Atlantic Oscillation (NAO) only correlated with the second EOF mode of SST on a decadal time scale.

show abstract

Regime shift in sea-ice characteristics and impact on the spring bloom in the Baltic Sea

Cited by 7 publications

References 52 publications

Global-scale parameters for ecological models

Global-scale parameters for ecological models

Using convective mixing in mesocosms to study climate-driven shifts in phytoplankton community distributions

Interannual and Decadal Variability of Sea Surface Temperature and Sea Ice Concentration in the Barents Sea

Contact Info

Product

Resources

About