Improved Global Net Surface Heat Flux

Carton, James A.; Chepurin, Gennady A.; Chen, Ligan; Grodsky, Semyon A.

doi:10.1002/2017jc013137

Cited by 28 publications

(28 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study to determine the influence of freshwater and salinity into the SEAS region, multiple salinity products are used. The Simple Ocean Data Assimilation v3.12.2 (SODA) monthly ocean reanalysis product available on a 0.5° horizontal grid with 50 vertical layers is used from 1980 to 2016 (Carton et al, ; Carton et al, ). SODA v3.12.2 has observational data assimilation and is forced with JRA‐55DO with the Coupled Ocean‐Atmosphere Response Experiment version 4 bulk formulae for surface forcing.…”

Section: Methodsmentioning

confidence: 99%

The Role of Salinity in the Southeastern Arabian Sea in Determining Monsoon Onset and Strength

2020

View full text Add to dashboard Cite

In this study, we examine the role of freshwater transported from the Bay of Bengal into the southeastern Arabian Sea (SEAS) in determining both the timing of monsoon onset and the strength of the ensuing monsoon. To do this, we use a combination of satellite-derived salinity data and reanalysis products from 1980 to 2016 in order to discuss seasonal, interannual, subdecadal, and long-term decadal variability in various atmospheric and oceanic contributors such as ocean heat content, freshwater transport, ERA5 instantaneous moisture flux, mixed layer depth, and barrier layer thickness. The salt budget in the SEAS reveals the dominance of meridional freshwater advective transport that brings in variability in salinity and thus in salinity stratification, as well as in mixed layer processes. We find that these parameters exhibit the prevalence of a long-term decadal variability (with trends of increasing/decreasing phases over a 15-year duration) over which interannual (3-year trend) and intradecadal (7-year trend) variability trends are superimposed. The patterns of both the 3-and 7-year trends follow each other closely, and relatively high amplitudes are seen in the 3-year trends. The long-term decrease in moisture flux and freshwater transport into the SEAS along with a rise in ocean heat content over a 15-year duration after 1994 contributed to a lack of strong monsoons in recent years; the prevailing interannual and intradecadal variability in these parameters associated with the Indian Ocean Dipole and El Niño Southern Oscillation events favored weaker and normal monsoons after 1994. Plain Language SummaryThe timing and intensity of the southwest monsoon is difficult to forecast and has been the subject of a great deal of research. In particular, the genesis of a monsoon onset vortex over the southeastern Arabian Sea (SEAS) encompassing the Arabian Sea Mini Warm Pool has received increased attention for the role it plays on the onset of the southwest monsoon. In our study, we examine how the low salinity waters transported from the Bay of Bengal (both along the east coast of India and from the southern Bay of Bengal) into the SEAS region influence the strength and timing of the ensuing southwest monsoon. Our finding is that this low salinity water has a significant impact on barrier layer formation in the SEAS and the vertical thermal structure of the Arabian Sea Mini Warm Pool. Further examination of subdecadal, decadal, and long-period decadal trends from 1980 to 2016 shows that instantaneous moisture fluxes and zonal winds over the SEAS have decreased substantially, and barrier layer thickness has increased since 1994 in association with the increasing trend in ocean heat content and contributed to the lack of strong southwest monsoons after 1994.

show abstract

Section: Methodsmentioning

confidence: 99%

The Role of Salinity in the Southeastern Arabian Sea in Determining Monsoon Onset and Strength

2020

View full text Add to dashboard Cite

show abstract

“…Although the two Argo profile analyses resolve low SSS in the Amazon plume, it appears that its interannual variability is not resolved well. For a better representation of high variable Amazon plume SSS, a Simple Ocean Data Assimilation (SODA version 3; Carton et al, 2018) is used. Particular run used in this study (SODA 3.4.2) is driven by the Era-Interim surface forcing and monthly river discharge (Dai et al, 2009(Dai et al, ) and spans 1980(Dai et al, -2015 Observed monthly discharge at Obidos station is used as a simple proxy for Amazon discharge.…”

Section: Methodsmentioning

confidence: 99%

Delayed and Quasi‐Synchronous Response of Tropical Atlantic Surface Salinity to Rainfall

Grodsky

Carton

2018

JGR Oceans

Self Cite

View full text Add to dashboard Cite

Zonally elongated patterns of tropical Atlantic rainfall variability associated with the Atlantic meridional (dipole) and the El Niño‐Southern Oscillation (ENSO) modes extend over the ocean and adjacent land and lead to a complex sea surface salinity (SSS) response. SSS response to local ocean rainfall is quasi‐instantaneous, while SSS response to land rainfall is delayed through river hydrology. Amazon rainfall associated with the Atlantic meridional mode concentrates over coastal north‐east Brazil and results in a fast Amazon response. In contrast, ENSO rainfall occupies vast inland areas and results in 3‐ to 7‐month delayed Amazon discharge response. Although ocean profile analyses represent well interannual SSS forced by open ocean rainfall, they miss interannual SSS in the plume. Including thermosalinograph transect data improves the representation of plume SSS, but its dynamic is better captured by ocean reanalyses. In Simple Ocean Data Assimilation, the plume SSS anomaly persists several months following the peak of land rainfall and is mixed out by seasonally accelerating winds in boreal winter. Its discharge‐correlated magnitude is only a modest few tenths of PSU. Perhaps, such weak projection of continental discharge variations on simulated SSS is not surprising due to other factors contributing in this dynamically active western boundary area. Significant transient variability in local currents not associated with rainfall/discharge variability is a factor explaining why ocean analyses based on occasional profile observations fail in resolving the rain‐induced SSS in the Amazon plume.

show abstract

“…q q q q q q q q q q q q qq qq q q q q q q qq q q q q q q q q q q q qq q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q sion 3 (SODA3) reanalysis (Carton et al, 2018), which uses all temperature and salinity profiles from the World Ocean Database. This data is available for depths between 5 and 5000 meters below the surface at a spatial resolution of 0.5 degrees latitude and longitude.…”

Section: Real Data Examplementioning

confidence: 99%

“…. , 461, whereŜ P j,i is the prediction at location i in simulation j using the preferential model andŜ NP j,i the equivalent using the non-preferential q q qq q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q qq q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q qq q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q (Carton et al, 2018). Panel (b): Median of prediction difference between preferential and non-preferential models.…”

Section: Real Data Examplementioning

confidence: 99%

Modelling ocean temperatures from bio-probes under preferential sampling

Dinsdale¹,

Salibián‐Barrera²

2019

Ann. Appl. Stat.

View full text Add to dashboard Cite

In the last 25 years there has been an important increase in the amount of data collected from animal-mounted sensors (bio-probes), which are often used to study the animals' behaviour or environment. We focus here on an example of the latter, where the interest is in sea surface temperature (SST), and measurements are taken from sensors mounted on Elephant Seals in the Southern Indian ocean. We show that standard geostatistical models may not be reliable for this type of data, due to the possibility that the regions visited by the animals may depend on the SST. This phenomenon is know in the literature as preferential sampling, and, if ignored, it may affect the resulting spatial predictions and parameter estimates. Research on this topic has been mostly restricted to stationary sampling locations such as monitoring sites. The main contribution of this manuscript is to extend this methodology to observations obtained by devices that move through the region of interest, as is the case with the tagged seals. More specifically, we propose a flexible framework for inference on preferentially sampled fields, where the process that generates the sampling locations is stochastic and moving over time through a 2dimensional space. Our simulation studies confirm that predictions obtained from the preferential sampling model are more reliable when this phenomenon is present, and they compare very well to the standard ones when there is no preferential sampling. Finally, we note that the conclusions of our analysis of the SST data can change considerably when we incorporate preferential sampling in the model.

show abstract

Improved Global Net Surface Heat Flux

Cited by 28 publications

References 42 publications

The Role of Salinity in the Southeastern Arabian Sea in Determining Monsoon Onset and Strength

The Role of Salinity in the Southeastern Arabian Sea in Determining Monsoon Onset and Strength

Delayed and Quasi‐Synchronous Response of Tropical Atlantic Surface Salinity to Rainfall

Modelling ocean temperatures from bio-probes under preferential sampling

Contact Info

Product

Resources

About