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Harmful Algae ◽  
2022 ◽  
Vol 112 ◽  
pp. 102183
Aude Boivin-Rioux ◽  
Michel Starr ◽  
Joël Chassé ◽  
Michael Scarratt ◽  
William Perrie ◽  

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 145
Siti Mariana Che Mat Nor ◽  
Shazlyn Milleana Shaharudin ◽  
Shuhaida Ismail ◽  
Sumayyah Aimi Mohd Najib ◽  
Mou Leong Tan ◽  

This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA) coupled with the clustering approach, is often applied to reduce the dimension of the data while simultaneously performing cluster partitions. However, the classical PCA is highly insensitive to outliers, as it assigns equal weights to each set of observations. Hence, applying the classical PCA could affect the cluster partitions of the rainfall patterns. Furthermore, traditional clustering algorithms only allow each element to exclusively belong to one cluster, thus observations within overlapping clusters of the torrential rainfall datasets might not be captured effectively. In this study, a statistical model of torrential rainfall pattern recognition was proposed to alleviate these issues. Here, a Robust PCA (RPCA) based on Tukey’s biweight correlation was introduced and the optimum breakdown point to extract the number of components was identified. A breakdown point of 0.4 at 85% cumulative variance percentage efficiently extracted the number of components to avoid low-frequency variations or insignificant clusters on a spatial scale. Based on the extracted components, the rainfall patterns were further characterized based on cluster solutions attained using Fuzzy C-means clustering (FCM) to allow data elements to belong to more than one cluster, as the rainfall data structure permits this. Lastly, data generated using a Monte Carlo simulation were used to evaluate the performance of the proposed statistical modeling. It was found that the proposed RPCA-FCM performed better using RPCA-FCM compared to the classical PCA coupled with FCM in identifying the torrential rainfall patterns of Peninsular Malaysia’s East Coast.

2022 ◽  
Nathan J Butterworth ◽  
James F Wallman ◽  
Nikolas P Johnston ◽  
Blake M Dawson ◽  
Angela McGaughran

Climate change and deforestation are causing rainforests to become increasingly fragmented, placing them at heightened risk of biodiversity loss. Invertebrates constitute the greatest proportion of this biodiversity, yet we lack basic knowledge of their population structure and ecology. It is not currently feasible to assess the population structure of every invertebrate species, so there is a compelling need to identify indicator species that are broadly indicative of habitat-level patterns and processes. Blowflies are an ideal candidate, because they are widespread, abundant, and can be easily collected within rainforests. Here, we present the first study of the blowfly Chrysomya latifrons , which is endemic to the rainforests of New South Wales, Australia. We genotyped 188 flies from 15 isolated rainforests and found low overall genetic diversity and a complete lack of genetic structure between populations, suggesting the presence of a single large panmictic population along 1,000 km of the Australian east coast. This highlights that: (1) Ch. latifrons inhabits every rainforest in NSW and undoubtedly plays an important role in these ecosystems, but low genetic diversity may cause it to struggle to adapt to a changing climate; (2) strongly dispersing insects have the capacity to migrate between isolated rainforests, likely carrying pollen, parasites, phoronts, and pathogens with them to form crucial trophic networks; and (3) there is an urgent need for similar studies on poorly dispersing rainforest insects, as these may be the most fragmented and at highest risk of local extinction.

2022 ◽  
Nguyen Minh Khiem ◽  
Yuki Takahashi ◽  
Hiroki Yasuma ◽  
Dang Thi Hoang Oanh ◽  
Tran Ngoc Hai ◽  

2022 ◽  
Vol 14 (2) ◽  
pp. 354
Jan Kavan ◽  
Guy D. Tallentire ◽  
Mihail Demidionov ◽  
Justyna Dudek ◽  
Mateusz C. Strzelecki

Tidewater glaciers on the east coast of Svalbard were examined for surface elevation changes and retreat rate. An archival digital elevation model (DEM) from 1970 (generated from aerial images by the Norwegian Polar Institute) in combination with recent ArcticDEM were used to compare the surface elevation changes of eleven glaciers. This approach was complemented by a retreat rate estimation based on the analysis of Landsat and Sentinel-2 images. In total, four of the 11 tidewater glaciers became land-based due to the retreat of their termini. The remaining tidewater glaciers retreated at an average annual retreat rate of 48 m year−1, and with range between 10–150 m year−1. All the glaciers studied experienced thinning in their frontal zones with maximum surface elevation loss exceeding 100 m in the ablation areas of three glaciers. In contrast to the massive retreat and thinning of the frontal zones, a minor increase in ice thickness was recorded in some accumulation areas of the glaciers, exceeding 10 m on three glaciers. The change in glacier geometry suggests an important shift in glacier dynamics over the last 50 years, which very likely reflects the overall trend of increasing air temperatures. Such changes in glacier geometry are common at surging glaciers in their quiescent phase. Surging was detected on two glaciers studied, and was documented by the glacier front readvance and massive surface thinning in high elevated areas.

Ocean Science ◽  
2022 ◽  
Vol 18 (1) ◽  
pp. 67-88
Alizée Roobaert ◽  
Laure Resplandy ◽  
Goulven G. Laruelle ◽  
Enhui Liao ◽  
Pierre Regnier

Abstract. The temporal variability of the sea surface partial pressure of CO2 (pCO2) and the underlying processes driving this variability are poorly understood in the coastal ocean. In this study, we tailor an existing method that quantifies the effects of thermal changes, biological activity, ocean circulation and freshwater fluxes to examine seasonal pCO2 changes in highly variable coastal environments. We first use the Modular Ocean Model version 6 (MOM6) and biogeochemical module Carbon Ocean Biogeochemistry And Lower Trophics version 2 (COBALTv2) at a half-degree resolution to simulate coastal CO2 dynamics and evaluate them against pCO2 from the Surface Ocean CO2 Atlas database (SOCAT) and from the continuous coastal pCO2 product generated from SOCAT by a two-step neuronal network interpolation method (coastal Self-Organizing Map Feed-Forward neural Network SOM-FFN, Laruelle et al., 2017). The MOM6-COBALT model reproduces the observed spatiotemporal variability not only in pCO2 but also in sea surface temperature, salinity and nutrients in most coastal environments, except in a few specific regions such as marginal seas. Based on this evaluation, we identify coastal regions of “high” and “medium” agreement between model and coastal SOM-FFN where the drivers of coastal pCO2 seasonal changes can be examined with reasonable confidence. Second, we apply our decomposition method in three contrasted coastal regions: an eastern (US East Coast) and a western (the Californian Current) boundary current and a polar coastal region (the Norwegian Basin). Results show that differences in pCO2 seasonality in the three regions are controlled by the balance between ocean circulation and biological and thermal changes. Circulation controls the pCO2 seasonality in the Californian Current; biological activity controls pCO2 in the Norwegian Basin; and the interplay between biological processes and thermal and circulation changes is key on the US East Coast. The refined approach presented here allows the attribution of pCO2 changes with small residual biases in the coastal ocean, allowing for future work on the mechanisms controlling coastal air–sea CO2 exchanges and how they are likely to be affected by future changes in sea surface temperature, hydrodynamics and biological dynamics.

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 445
Jeanie A. Aird ◽  
Rebecca J. Barthelmie ◽  
Tristan J. Shepherd ◽  
Sara C. Pryor

Two years of high-resolution simulations conducted with the Weather Research and Forecasting (WRF) model are used to characterize the frequency, intensity and height of low-level jets (LLJ) over the U.S. Atlantic coastal zone. Meteorological conditions and the occurrence and characteristics of LLJs are described for (i) the centroids of thirteen of the sixteen active offshore wind energy lease areas off the U.S. east coast and (ii) along two transects extending east from the U.S. coastline across the northern lease areas (LA). Flow close to the nominal hub-height of wind turbines is predominantly northwesterly and southwesterly and exhibits pronounced seasonality, with highest wind speeds in November, and lowest wind speeds in June. LLJs diagnosed using vertical profiles of modeled wind speeds from approximately 20 to 530 m above sea level exhibit highest frequency in LA south of Massachusetts, where LLJs are identified in up to 12% of hours in June. LLJs are considerably less frequent further south along the U.S. east coast and outside of the summer season. LLJs frequently occur at heights that intersect the wind turbine rotor plane, and at wind speeds within typical wind turbine operating ranges. LLJs are most frequent, intense and have lowest core heights under strong horizontal temperature gradients and lower planetary boundary layer heights.

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 79
Tingting Fan ◽  
Yuxing Yang ◽  
Shibin Xu

As a prime circulation system, the western Pacific subtropical high (WPSH) significantly impacts tropical cyclone (TC) activities over the western North Pacific (WNP), especially TCs landing on the east coast of China; however, the associated mechanism is not firmly established. This study investigates the underlying dynamic impact of the first two empirical orthogonal function (EOF) modes of the WPSH on the interannual variability in the genesis and number of TCs landing over the WNP. The results show that these two dominant modes control the WNP TC activity over different subregions via different environmental factors. The first mode (EOF1) affects the TC genesis number over region I (105°–128° E, 5°–30° N) (r = −0.49) and region II (130°–175° E, 17°–30° N) (r = −0.5) and controls the TCs landing on the east coast of China, while the second mode (EOF2) affects the TC genesis number over region III (128°–175° E, 5°–17° N) (r = −0.69). The EOF1 mode, a southwest-northeast-oriented enhanced pattern, causes the WPSH to expand (retreat) along the southwest-northeast direction, which makes both mid-low-level relative humidity and low-level vorticity unfavorable (favorable) for TC genesis in region I and region II and steers fewer (more) TC tracks to land on the coast of China. The EOF2 mode features a strengthened WPSH over the southeast quarter of the WNP region. The active (inactive) phases of this mode control the low-level vorticity and vertical wind shear in region III, which lead to less (more) TC genesis over this region. The prediction equations combining the two modes of the WPSH for the total number of TCs and TCs that make landfall show high correlation coefficients. Our findings verify the high prediction skill of the WPSH on WNP TC activities, provide a new way to predict TCs that will make landfall on the east coast of China, and help to improve the future projection of WNP TC activity.

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Kai Chang ◽  
Zesheng Li ◽  
Yu Long

This article attempts to fill important knowledge gaps to explore the spatial spillover effects of financial markets on regional financial efficiency in eight economic zones using three-stage superefficiency data envelopment analysis (DEA) and Durbin’s spatial econometric model. The average financial efficiencies in the North coast, East coast, and South coast economic zones reach the superefficiency DEA relatively efficient level, while the average financial efficiencies in the Northeast, Middle Yellow River, Middle Yangtze River, and large West-south and West-north economic zones reach the superefficiency DEA relatively inefficient level. Except for the North coast economic zone, seven equity markets have significant impacts on regional financial efficiency, and local equity markets in the Northeast, South coast, Middle Yellow River, and Middle Yangtze River economic zones generate significant spatial spillover effects on neighboring regions’ financial efficiency. Local credit markets only in the Northeast and South coast economic zones have significant spatial spillover influences on neighboring regions’ financial efficiency. Debt markets in the North coast, East coast, South coast, Middle Yangtze River, and large West-south economic zones have significant influences on regional financial efficiency, and local debt markets in the East coast and Middle Yangtze River economic zones generate significant spatial spillover effects on neighboring regions’ financial efficiency.

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