Looking back and looking forwards: Historical and future trends in sea surface temperature (SST) in the Indo-Pacific region from 1982 to 2100

Khalil, Idham; Atkinson, Peter M.; Challenor, Peter

doi:10.1016/j.jag.2015.10.005

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…Increase in temperature on the Philippines seas has been reported by several studies (Peñaflor et al, 2009;Pörtner et al, 2014;Khalil et al, 2016;Hoegh-Guldberg et al, 2017;Geronimo, 2018). Sea surface temperature in the sea near the Philippines shows upward trend with the warming rate of 0.2 • C per decade over the period 1985-2017, based on 0.05 • resolution satellite-based sea surface temperature data (Peñaflor et al, 2009;Khalil et al, 2016). The warming trend is not spatially identical for the Philippines and the warming rate varies by region.…”

Section: Climate Change and Ocean In The Philippinesmentioning

confidence: 60%

“…The warming trend is not spatially identical for the Philippines and the warming rate varies by region. The warming rate in the West Philippine Sea bordering the west-central part of the Province of Ilocos Norte shows a faster rate while the rate in the sea surrounding Palawan Island and the sea between Catanduanes Island and Samar Island shows slower compared to other sea areas in the Philippines (Khalil et al, 2016). The forecasting model of warming with a scenario of greenhouse gas (GHG) concentration mitigation under the phase 5 of Coupled Model Intercomparison Project (CMIP), which is collaboration between climate modeling groups for the purpose of advance in knowledge of climate change, indicates that sea surface temperature in the Philippine will increase around 0.36 • C by 2100 based on the RCP 2.6 emissions scenario, noting that the majority of this warming will happen over the next 30 years (Khalil et al, 2016).…”

Section: Climate Change and Ocean In The Philippinesmentioning

confidence: 95%

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Projected Economic Impact of Climate Change on Marine Capture Fisheries in the Philippines

Suh

Pomeroy

2020

Front. Mar. Sci.

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Climate change and its impact on fisheries is a key issue for fishing nations, particularly the Philippines. The Philippines is highly vulnerable to the impacts of climate change on fisheries and it can lead to economic shock on the nation's economy. This paper examines the impact of climate change on marine capture fisheries in the Philippines using a computable general equilibrium (CGE) model to elaborate and project impacts on the national economy. In the simulation, one baseline scenario and two climate change scenarios based on greenhouse gas concentration-RCP 2.6 and RCP 8.5-were considered. The model focuses on Gross Domestic Product (GDP) and income distribution by region, which can represent economic conditions in terms of economic growth and distribution. Results show that there will be a negative change on both the fisheries and economic variables where more extreme changes in climate occur.

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Section: Climate Change and Ocean In The Philippinesmentioning

confidence: 60%

Section: Climate Change and Ocean In The Philippinesmentioning

confidence: 95%

Projected Economic Impact of Climate Change on Marine Capture Fisheries in the Philippines

Suh

Pomeroy

2020

Front. Mar. Sci.

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“…Spawning ground shifts have been reported for other fish species also (e.g., Japanese sardine, Arctic and Atlantic cods, chub mackerel) as being induced by fisheries and climate change (Kikuchi et al, 1992;Van Der Meeren and Ivannikov, 2006;Opdal, 2010;Kanamori et al, 2019). Throughout the SCS, average SST has increased by about 0.348°C from 1982 to 2010, and has been projected to increase by about 0.36°C over 9 decades from 2010 to 2,100 (Atkinson et al, 2016). The annual mean SST in the central SCS has increased by 0.92°C from 1950 to 2006 (Cai et al, 2009).…”

Section: Spawning Ground Distributions and Hydrological Conditionsmentioning

confidence: 97%

Identification of Eggs and Spawning Zones of Hairtail Fishes Trichiurus (Pisces: Trichiuridae) in Northern South China Sea, Using DNA Barcoding

Hou

Chen

et al. 2021

Front. Environ. Sci.

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Commercially important hairtails, Trichiurus spp., are widely distributed throughout China’s marine waters. Because eggs of these species are difficult to identify and the taxonomy of the group has recently been resolved, their spawning grounds in the northern South China Sea (SCS) are not well known. We identified three Trichiurus species (T. japonicus, T. nanhaiensis, and T. brevis) using DNA barcodes in spring and late summer–autumn ichthyoplankton surveys of 2019 in the northern SCS. Egg distributions reveal that the spawning grounds of T. japonicus and T. nanhaiensis occur mainly from the central and southern Beibu Gulf, along Hainan Island, to the waters off the Pearl River Estuary, and that egg densities are higher in spring than in late summer-autumn. Spawning of T. japonicus commonly occurs along the continental shelf, T. nanhaiensis along the continental shelf to slope (over seabed depths of 42–380 m), and T. brevis mainly in shallow water. Considering the salinity and temperature data, we found that the eggs of Trichiurus were associated with specific water masses. In spring, eggs of T. japonicus and T. nanhaiensis occurred at central and southern Beibu Gulf water mass (CSBGWM) and South China Sea surface water mass (SCSWM). While in late summer-autumn, eggs of T. japonicus and T. nanhaiensis mainly occurred in the waters of SCSWM. Our findings reveal that the occurrence of Trichiurus eggs and their spatial and temporal distribution are determined by hydrological conditions.

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“…This not only causes the warming of the eastern equatorial Pacific to be greater than that outside of the equatorial region but also causes the sea surface temperature gradient to weaken, which is one of the main mechanisms that leads to the frequent occurrence of extreme El Niño events [4]. The Intergovernmental Panel on Climate Change (IPCC) reported that the global average SST will increase by about approximately 0.20 • C per decade [5]. Therefore, the determination of SST and ST…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network Using Surface Data to Predict Subsurface Temperatures in the Pacific Ocean

et al. 2019

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This paper proposes a convolutional neural network (CNN) method to estimate subsurface temperature (ST) in the Pacific Ocean from a suite of satellite remote sensing measurements. These include sea surface temperature(SST), sea surface height (SSH), and sea surface salinity (SSS). We propose using the multisource sea surface parameters to establish a monthly CNN model to reconstruct the ocean subsurface temperature (ST) and use Argo data for accurate validation. The results show that the CNN can accurately estimate the ST of the Pacific Ocean by using the model. We trained the model for 12 months. The most prominent months are January, April, July, and October with average mean square error (MSE) values of 0.2659, 0.3129, 0.5318, and 0.5160, and the average coefficients of determination (R 2 ) were 0.968, 0.971, 0.949, and 0.967, respectively. This study improves the accuracy of ST estimation and the good results based on reanalysis indicate that the model is promising to be applied to satellite observations. INDEX TERMS Convolutional neural network, ocean data, satellite measurements, subsurface temperature.

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Looking back and looking forwards: Historical and future trends in sea surface temperature (SST) in the Indo-Pacific region from 1982 to 2100

Cited by 14 publications

References 29 publications

Projected Economic Impact of Climate Change on Marine Capture Fisheries in the Philippines

Projected Economic Impact of Climate Change on Marine Capture Fisheries in the Philippines

Identification of Eggs and Spawning Zones of Hairtail Fishes Trichiurus (Pisces: Trichiuridae) in Northern South China Sea, Using DNA Barcoding

A Convolutional Neural Network Using Surface Data to Predict Subsurface Temperatures in the Pacific Ocean

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