scholarly journals MARINE HEATWAVES IN THE LAPTEV SEA IN 2019-2020

2021 ◽  
Vol 4 (1) ◽  
pp. 145-151
Author(s):  
Marina V. Kraineva ◽  
Elena N. Golubeva
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Observational Data ◽  
Heat Waves ◽  
Bottom Layer ◽  
Ocean Model ◽  
Sea Surface ◽  
The Arctic ◽  
Laptev Sea ◽  
The Laptev Sea

Marine heat waves are extreme events that represent a significant excess of average climatic temperatures. In this study marine heat waves calculated both from observational data and from numerical modeling data in the Laptev Sea region are analyzed. The regional numerical model SibCIOM (Siberian Coupled Ice-Ocean Model) is used to study the variability of the hydrological characteristics of the Arctic Ocean and its shelf seas. Observational data shows an increase in the frequency and intensity of the marine heat waves in recent years in this region. On the basis of numerical experiments, the work demonstrates intense warming in the bottom layer, as a consequence of the increase in the sea surface temperature of the Laptev Sea in recent years. The paper analyzes the possible causes and consequences of an increase in the sea surface temperature of the Laptev Sea.

scholarly journals Interannual Variability and Trends in Sea Surface Temperature, Lower and Middle Atmosphere Temperature at Different Latitudes for 1980–2019

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 454
Author(s):  
Andrew R. Jakovlev ◽  
Sergei P. Smyshlyaev ◽  
Vener Y. Galin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Lower Stratosphere ◽  
Middle Atmosphere ◽  
Southern Oscillation ◽  
Lower Troposphere ◽  
Reanalysis Data ◽  
Sea Surface ◽  
The Arctic ◽  
The Impact

The influence of sea-surface temperature (SST) on the lower troposphere and lower stratosphere temperature in the tropical, middle, and polar latitudes is studied for 1980–2019 based on the MERRA2, ERA5, and Met Office reanalysis data, and numerical modeling with a chemistry-climate model (CCM) of the lower and middle atmosphere. The variability of SST is analyzed according to Met Office and ERA5 data, while the variability of atmospheric temperature is investigated according to MERRA2 and ERA5 data. Analysis of sea surface temperature trends based on reanalysis data revealed that a significant positive SST trend of about 0.1 degrees per decade is observed over the globe. In the middle latitudes of the Northern Hemisphere, the trend (about 0.2 degrees per decade) is 2 times higher than the global average, and 5 times higher than in the Southern Hemisphere (about 0.04 degrees per decade). At polar latitudes, opposite SST trends are observed in the Arctic (positive) and Antarctic (negative). The impact of the El Niño Southern Oscillation phenomenon on the temperature of the lower and middle atmosphere in the middle and polar latitudes of the Northern and Southern Hemispheres is discussed. To assess the relative influence of SST, CO2, and other greenhouse gases’ variability on the temperature of the lower troposphere and lower stratosphere, numerical calculations with a CCM were performed for several scenarios of accounting for the SST and carbon dioxide variability. The results of numerical experiments with a CCM demonstrated that the influence of SST prevails in the troposphere, while for the stratosphere, an increase in the CO2 content plays the most important role.

On the factors contributing to heat waves in Europe. A global large ensemble approach with MPAS

2021 ◽  
Author(s):  
Gerhard Smiatek ◽  
Harald Kunstmann
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Heat Waves ◽  
Circulation Pattern ◽  
Sea Surface ◽  
Maximum Temperature ◽  
Large Ensemble ◽  
Sea Ice Extent ◽  
Weather Patterns ◽  
Potential Factors

<p>The European summer heat wave of 2003 with record-breaking temperature anomalies was brought into connection with a blocking Omega circulation pattern, soil moisture deficit and high sea surface temperature, especially in the Mediterranean Sea. <sup> </sup>We investigate the potential factors influencing extreme heat waves in Europe with a very large ensemble obtained from multiple global integrations of the Model for Prediction Across Scales (MPAS). The global MPAS runs are performed in approximately 60 km resolution with sea surface temperature (SST) and sea ice extent from ERA-Interim data as boundary condition initialized on different days.</p><p>The contribution investigates the results obtained from a total of 540 simulations. It concentrates on the regional SST and weather patterns and moisture obtained in simulations contributing to the upper 10% of the resulting probability density function (PDF) of the summer daily mean and maximum temperature. The investigation considers in total eight standard evaluation domains in Europe as defined in the PRUDENCE project.</p>

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

scholarly journals Satellite-based time-series of sea-surface temperature since 1981 for climate applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Christopher J. Merchant ◽  
Owen Embury ◽  
Claire E. Bulgin ◽  
Thomas Block ◽  
Gary K. Corlett ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Sea Surface ◽  
Climate Data ◽  
Satellite Measurements ◽  
Data Record ◽  
The Mean ◽  
Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

scholarly journals Change in the Recent Warming Trend of Sea Surface Temperature in the East Sea (Sea of Japan) over Decades (1982–2018)

2019 ◽  
Vol 11 (22) ◽  
pp. 2613 ◽  
Author(s):  
Eun-Young Lee ◽  
Kyung-Ae Park
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Of Japan ◽  
Warming Trend ◽  
Frequent Occurrence ◽  
Sea Surface ◽  
East Sea ◽  
The Arctic ◽  
Arctic Oscillation Index ◽  
Sst Cooling

Long-term trends of sea surface temperature (SST) of the East Sea (Sea of Japan, EJS) were estimated by using 37-year-long satellite data, for the observation period from 1982 to 2018. Overall, the SST tended to increase with time, for all analyzed regions. However, the warming trend was steeper in the earlier decades since the 1980s and slowed down during the recent two decades. Based on the analysis of the occurrence of events with extreme SST (high in the summertime and low in the wintertime), a shift toward the more frequent occurrence of events with extremely high SST and the less frequent occurrence of events with extremely low SST has been observed. This supports the observations of the consistent warming of the EJS. However, seasonal trends revealed continuous SST warming in the summertime, but frequent extreme SST cooling in the wintertime, in recent decades. The observed reduction in the warming rates occurred more frequently in specific regions of the EJS, where the occurrence frequency of events with extremely low SST was unusually high in the recent decade. The recent tendency toward the SST cooling was distinctively connected with variations in the Arctic Oscillation index. This suggests that changes in the Arctic Ocean environment likely affect the recently observed SST changes in the EJS, as one of the marginal seas in the mid-latitude region far from the polar region.

scholarly journals Intraseasonal Sea Surface Temperature Variability across the Indonesian Seas*

2015 ◽  
Vol 28 (22) ◽  
pp. 8710-8727 ◽  
Author(s):  
Asmi M. Napitu ◽  
Arnold L. Gordon ◽  
Kandaga Pujiana
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Intraseasonal Variability ◽  
Longwave Radiation ◽  
Active Phase ◽  
Ocean Model ◽  
Sea Surface ◽  
Indonesian Seas ◽  
Sst Variability ◽  
Slab Ocean

Abstract Sea surface temperature (SST) variability at intraseasonal time scales across the Indonesian Seas during January 1998–mid-2012 is examined. The intraseasonal variability is most energetic in the Banda and Timor Seas, with a standard deviation of 0.4°–0.5°C, representing 55%–60% of total nonseasonal SST variance. A slab ocean model demonstrates that intraseasonal air–sea heat flux variability, largely attributed to the Madden–Julian oscillation (MJO), accounts for 69%–78% intraseasonal SST variability in the Banda and Timor Seas. While the slab ocean model accurately reproduces the observed intraseasonal SST variations during the northern winter months, it underestimates the summer variability. The authors posit that this is a consequence of a more vigorous cooling effect induced by ocean processes during the summer. Two strong MJO cycles occurred in late 2007–early 2008, and their imprints were clearly evident in the SST of the Banda and Timor Seas. The passive phase of the MJO [enhanced outgoing longwave radiation (OLR) and weak zonal wind stress) projects on SST as a warming period, while the active phase (suppressed OLR and westerly wind bursts) projects on SST as a cooling phase. SST also displays significant intraseasonal variations in the Sulawesi Sea, but these differ in characteristics from those of the Banda and Timor Seas and are attributed to ocean eddies and atmospheric processes independent from the MJO.

Optimizing COSMO_REA6 reanalysis radiation flux for a high resolution coastal ocean model

2020 ◽  
Author(s):  
Martin Vodopivec ◽  
Matjaž Ličer
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atmospheric Model ◽  
Short Wave ◽  
Ocean Model ◽  
Sea Surface ◽  
Atmospheric Models ◽  
Fine Grid ◽  
Wave Flux

<p>When modelling coastal areas in high spatial resolution, it is also essential to obtain atmospheric forcing with suitably fine grid. The complex coastline and coastal orography exert strong influence on atmospheric fields, wind in particular, and the east Adriatic coast with numerous islands and coastal mountain ridges is a fine example. We decided to use a high resolution COSMO atmospheric reanalysis for our long term ROMS_AGRIF hindcasts, but in our initial experiments we found out that the atmospheric model significantly underestimates the short wave flux over the Mediterranean Sea, probably due to overestimation of high clouds formation and erroneous sea surface temperature used as a boundary condition. We explore different atmospheric models and different combinations of fluxes - direct, diffuse and clear sky solar radiation and combinations of fluxes from different atmospheric models (eg. ERA5). We compare them with solar irradiance observations at a coastal meteorological station and run year-long simulations to compare model sea surface temperature (SST) with satellite observations obtained from Coprenicus Marine Environment Monitoring Service.</p>

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