scholarly journals What is the Role of the Sea Surface Temperature Uncertainty in the Prediction of Tropical Convection Associated with the MJO?

2015 ◽  
Vol 143 (8) ◽  
pp. 3156-3175 ◽  
Author(s):  
Wanqiu Wang ◽  
Arun Kumar ◽  
Joshua Xiouhua Fu ◽  
Meng-Pai Hung
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Rainfall Variability ◽  
Lower Boundary ◽  
Ocean Model ◽  
Sea Surface ◽  
Climatic Data ◽  
Sst Variability ◽  
National Climatic Data Center ◽  
Convection Schemes

Abstract This study investigated the influence of the uncertainty in the sea surface temperature (SST) on the representation of the intraseasonal rainfall variability associated with the Madden–Julian oscillation (MJO) and how this influence varies with convection parameterization. The study was motivated by the fact that there exist substantial differences in observational SST analyses, and by the possibility that lacking sufficient accuracy for SSTs in dynamical models may degrade the MJO simulation and prediction. Experiments for the DYNAMO intensive observing period were carried out using the NCEP atmospheric Global Forecast System (GFS) with three convection schemes forced by three SST specifications. The SST specifications included the widely used National Climatic Data Center (NCDC) daily SST analysis, the TRMM Microwave Imager (TMI) SST retrieval, and an SST climatology that only contains climatological seasonal cycle. The experiments show that for all convection schemes, the advantage of using observed (TMI and NCDC) SSTs over the climatology SSTs can be seen as early as 5 days to 1 week after the start of the forecast. Further, the prediction with TMI SSTs was more skillful than that with the NCDC SSTs, indicating that the current level of SST uncertainties in the observational analyses can lead to large differences when they are used as the lower boundary conditions. The results suggest that the simulation and prediction can be improved with an atmosphere-only model forced by more accurate SSTs, or with a coupled atmosphere–ocean model that has a more realistic representation of the SST variability. Differences in the prediction among the convection schemes are also presented and discussed.

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.

scholarly journals Long term trends in the sea surface temperature of the Black Sea

Ocean Science ◽  
2010 ◽  
Vol 6 (2) ◽  
pp. 491-501 ◽  
Author(s):  
G. I. Shapiro ◽  
D. L. Aleynik ◽  
L. D. Mee
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Black Sea ◽  
Sea Surface ◽  
The Black Sea ◽  
The North ◽  
Sst Variability ◽  
Long Term Trends ◽  
Cooling Trend

Abstract. There is growing understanding that recent deterioration of the Black Sea ecosystem was partly due to changes in the marine physical environment. This study uses high resolution 0.25° climatology to analyze sea surface temperature variability over the 20th century in two contrasting regions of the sea. Results show that the deep Black Sea was cooling during the first three quarters of the century and was warming in the last 15–20 years; on aggregate there was a statistically significant cooling trend. The SST variability over the Western shelf was more volatile and it does not show statistically significant trends. The cooling of the deep Black Sea is at variance with the general trend in the North Atlantic and may be related to the decrease of westerly winds over the Black Sea, and a greater influence of the Siberian anticyclone. The timing of the changeover from cooling to warming coincides with the regime shift in the Black Sea ecosystem.

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 Spatio-Temporal Variability and Trend of Rainfall and Its Association with Pacific Ocean Sea Surface Temperature in West Harerge Zone, Eastern Ethiopia

2020 ◽  
Author(s):  
Getachew Bayable Tiruneh ◽  
Gedamu Amare ◽  
Getnet Alemu ◽  
Temesgen Gashaw
Keyword(s):  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temporal Variability ◽  
Rainfall Variability ◽  
Sea Surface ◽  
Annual Rainfall ◽  
Eastern Ethiopia ◽  
Spatio Temporal ◽  
Annual Time

Abstract Background: Rainfall variability is a common characteristic in Ethiopia and it exceedingly affects agriculture particularly in the eastern parts of the country where rainfall is relatively scarce. Hence, understanding the spatio-temporal variability of rainfall is indispensable for planning mitigation measures during high and low rainfall seasons. This study examined the spatio-temporal variability and trends of rainfall in the West Harerge Zone, eastern Ethiopia.Method: The coefficient of variation (CV) and standardized anomaly index (SAI) was employed to analyze rainfall variability while Mann-Kendall (MK) trend test and Sen’s slop estimator were employed to examine the trend and magnitude of the rainfall changes, respectively. The association between rainfall and Pacific Ocean Sea Surface Temperature (SST) was also evaluated by the Pearson correlation coefficient (r).Results: The annual rainfall CV ranges from 12-19.36% while the seasonal rainfall CV extends from 15-28.49%, 24-35.58%, and 38-75.9% for average Kiremt (June-September), Belg (February-May), and Bega (October-January) seasons, respectively (1983-2019). On the monthly basis, the trends of rainfall decreased in all months except in July, October, and November. However, the trends of rainfall were not statistically significant (α = 0.05), unlike November. The annual rainfall trends showed a non-significant decreasing trend. On a seasonal basis, the trend of mean Kiremt and Belg seasons rainfall was decreased. But, it increased in Bega season although it was not statistically significant. Moreover, the correlation between rainfall and Pacific Ocean SST was negative for Kiremt while positive for Belg and Bega seasons. Besides, the correlation between rainfall and Pacific Ocean SST was negative at annual time scales.Conclusions: High spatial and temporal rainfall variability on monthly, seasonal, and annual time scales was observed in the study area. Seasonal rainfall has high inter-annual variability in the dry season (Bega) than other seasons. The trends in rainfall were decreased in most of the months. Besides, the trend of rainfall was increased annually and in the Bega season rather than other seasons. Generally, the occurrence of droughts in the study area was associated with ENSO events like most other parts of Ethiopia and East Africa.

scholarly journals On the Interpretation of the North Atlantic Averaged Sea Surface Temperature

2020 ◽  
Vol 33 (14) ◽  
pp. 6025-6045
Author(s):  
Jing Sun ◽  
Mojib Latif ◽  
Wonsun Park ◽  
Taewook Park
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Climate Models ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
The North ◽  
Sst Variability ◽  
The North Atlantic ◽  
Different Time Scales

AbstractThe North Atlantic (NA) basin-averaged sea surface temperature (NASST) is often used as an index to study climate variability in the NA sector. However, there is still some debate on what drives it. Based on observations and climate models, an analysis of the different influences on the NASST index and its low-pass filtered version, the Atlantic multidecadal oscillation (AMO) index, is provided. In particular, the relationships of the two indices with some of its mechanistic drivers including the Atlantic meridional overturning circulation (AMOC) are investigated. In observations, the NASST index accounts for significant SST variability over the tropical and subpolar NA. The NASST index is shown to lump together SST variability originating from different mechanisms operating on different time scales. The AMO index emphasizes the subpolar SST variability. In the climate models, the SST-anomaly pattern associated with the NASST index is similar. The AMO index, however, only represents pronounced SST variability over the extratropical NA, and this variability is significantly linked to the AMOC. There is a sensitivity of this linkage to the cold NA SST bias observed in many climate models. Models suffering from a large cold bias exhibit a relatively weak linkage between the AMOC and AMO and vice versa. Finally, the basin-averaged SST in its unfiltered form, which has been used to question a strong influence of ocean dynamics on NA SST variability, mixes together multiple types of variability occurring on different time scales and therefore underemphasizes the role of ocean dynamics in the multidecadal variability of NA SSTs.

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 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.

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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