Ocean Climate Research Articles

Differential Analysis Based on Different Decomposition Methods of Time Series

A detailed analysis of four popular time series decomposition methods: Classical, X11, SEATS, and STL is provided in this study, with a focus on their applications, strengths, and limitations. Understanding seasonal patterns in time series data is an important aspect in several fields such as economics or environmental science. The R package seasonal was employed to conduct the decompositions on the official data of National Oceanic and Atmospheric Administration (NOAA) Climate Data from January 1980 till July 2024. It offers a detailed analysis of the performance of each method in isolating trends, seasonal components, and residuals. Findings reveal that although all methods can be used for time series data decomposition, their success is influenced by data nature and particular analytical tasks. For instance, SEATS is robust enough to handle irregularities while Classical decomposition wins when it comes to simple understanding. Similarly, X11 methods were known for lack of flexibility as well as recurring seasonal changes sensitivity, where STL is particularly efficient for varying seasonal patterns datasets with nonlinearity trends. From the findings, it can be concluded that it is important to choose the appropriate decomposition method to increase reliability and make appropriate adjustments in the forecasts incorporating time series data.

Substantial invasion of Antarctic Intermediate Water into the Arabian Sea during Younger Dryas and Heinrich Stadials

Antarctic Intermediate Water (AAIW) is an important component of the global thermohaline circulation and an essential limb of the Atlantic Meridional Overturning Circulation (AMOC), which redistributes heat, oxygen, and nutrients in the global ocean. Understanding the dynamics of intermediate water circulation over a millennial timeframe is essential to determine the impact of AMOC changes on ocean heat transport during abrupt climatic events. However, the precise relationship between global ocean intermediate water circulation and abrupt climate events, such as the Younger Dryas (YD) and Heinrich Stadials (HS), is not yet fully understood, particularly in tropical regions. Here, we present a ∼29 ka high-resolution record of Neodymium isotope (ƐNd) in authigenic phases, a water mass tracer, of a sediment core collected from the intermediate depth (840 m) in the eastern Arabian Sea (off Goa) to understand the past variability in AAIW circulation in the northern Indian Ocean on a millennial time scale. Our new ƐNd record reveals the pronounced temporal variation (−9.5 to −6.1) throughout the core, marked with enhanced radiogenic Nd signature during the YD, HS1, and HS2. These episodes of enhanced radiogenic Nd signatures signify an increased northward penetration of Antarctic Intermediate Water into the Northern Indian Ocean. The intervals of northward progression of AAIW coincide with the Northern Hemisphere cold events which could have resulted from the enhanced formation of AAIW in the Southern Ocean that occurred due to the warming-induced deceleration of the AMOC. This correlation underscores a robust connection between the formation of North Atlantic Deep Water (NADW) and Southern Ocean climate dynamics through the "bipolar seesaw" mechanism. Our study emphasizes that tracking changes in AAIW in the Arabian Sea can provide insights into the past and future variations in AMOC.

Multiple late Holocene glacier advances on the sub-Antarctic Kerguelen (49°S) islands: Evidence from a 1200 yr sediment core from a glacial threshold basin

Southern Ocean (SO) climate is rapidly changing because of global warming and regional climate feedback loops like shifts of the Southern Hemisphere (SH) westerly winds (SHW) and related Southern Annular Mode (SAM). Over the past decades, the former has been persistently positive, shifting the latter southwards: the ensuing changes in temperature and precipitation are linked to the rapid retreat of mid-latitude mountain glaciers. Beyond the short instrumental period, the long-term impact of this coupled SHW-SAM system on regional glaciers remains poorly constrained. To help close this gap, we reconstruct glacier advances from an outlet glacier on the sub-Antarctic Kerguelen (49°S, 69°E), an archipelago that is strategically located in an under-investigated region of the SHW core belt. Based on alternations between relatively organic and minerogenic mud detected using a multiproxy approach on a 1200-year-long sediment record from a glacial threshold basin, we document glacier advances between 1150 and 850, 820–620, 500–250 and 160–90 cal yr BP. Coincident glacier advances in adjacent regions like sub-Antarctic South Georgia and southern Patagonia, suggest that SO glaciers responded symmetrically to climate forcings during the past 1200 years. We attribute this synchronicity to shifting SAM-like conditions and associated temperature changes. We suggest that cold and negative SAM-like conditions, favourable for glacier growth on the Kerguelen and other SO land masses, dominated during much of this period. Furthermore, the findings presented here support the growing consensus for a two-phased regional expression of the Little Ice Age (LIA).

Investigating the potential for realizing life cycle net-zero energy buildings in Europe using multi-objective optimization

In pursuit of the long-term target of achieving climate neutrality by 2050, it is crucial to strive for buildings with net-zero energy balance from a life cycle perspective. This study employs multi-objective optimization, specifically the non-dominated sorting genetic algorithm II, to explore the feasibility of achieving a life cycle net-zero energy balance for a building case in diverse European climate conditions: including Gothenburg’s humid continental climate (characterized by warm to hot summers and cold, snowy winters), Frankfurt’s oceanic climate (featuring short, cool summers and long, relatively mild winters), and Madrid’s Mediterranean climate (marked by wet, mild winters and dry, hot summers). The study examines this feasibility across three building lifespan durations: the standard 50 years, commonly applied in life cycle analyses, as well as extended lifespans of 100 and 150 years. This examination is vital due to the impact of longer building lifespans, resulting in increased operational energy demand and higher embodied energy due to the more frequent replacements of building components and systems. The findings demonstrate the feasibility of realizing a life cycle net-zero energy building across diverse European climatic conditions and lifespan durations through the identification of optimal passive, active, and renewable measures. The results emphasize the essential role of renewable measures, such as photovoltaic (PV) panels, in effectively offsetting any surplus energy use throughout the building's life cycle, ultimately enabling the achievement of a life cycle net-zero energy building. It was observed that a minimum of 276 m2 (≈ 63.5 kWp), 246 m2 (≈ 56.5 kWp), and 131 m2 (≈ 30.1 kWp) of PV panels would be required to realize a life cycle net-zero energy building in Gothenburg, Frankfurt, and Madrid, respectively. Across all locations and lifespan durations, these PV panels were integrated with optimal active measures, which included geothermal heat pumps for heating, internal air conditioning units for cooling, and mechanical ventilation systems with heat recovery potential. However, the results also indicate that relying solely on active and renewable measures, without incorporating appropriate passive approaches, is insufficient to fully realize life cycle net-zero energy buildings.

Global Assessment of Mesoscale Eddies with TOEddies: Comparison Between Multiple Datasets and Colocation with In Situ Measurements

The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, preserving the natural spatial variability and enabling precise, high-resolution tracking of eddy dynamics. This dataset provides daily information on eddy characteristics, such as size, intensity, and polarity, over a 30-year period (1993–2023), capturing complex eddy interactions, including splitting and merging events that often produce networks of interconnected eddies. This unique approach challenges the traditional single-trajectory perspective, offering a nuanced view of eddy life cycles as dynamically linked trajectories. In addition to traditional metrics, TOEddies identifies both the eddy core (characterized by maximum azimuthal velocity) and the outer boundary, offering a detailed representation of eddy structure and enabling precise comparisons with in situ data. To demonstrate its value, we present a statistical overview of eddy characteristics and spatial distributions, including generation, disappearance, and merging/splitting events, alongside a comparative analysis with existing global eddy datasets. Among the multi-year observations, TOEddies captures coherent, long-lived eddies with lifetimes exceeding 1.5 years, while highlighting significant differences in the dynamic properties and spatial patterns across datasets. Furthermore, this study integrates TOEddies with 23 years of colocalized Argo profile data (2000–2023), allowing for a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties. This atlas aims to be a valuable resource for the oceanographic community, providing an open dataset that can support diverse applications in ocean dynamics, climate research, and marine resource management.

Understanding northeastern tropical atlantic ocean dynamics in relation to climate indices

Since 1993, Satellite Altimetry greatly enhanced the ability to study and understand ocean dynamics, particularly in the context of climate change. Though relatively low-energy and understudied, the Northeast Tropical Atlantic Ocean (NTAO) plays a crucial role in the Earth’s climate system. This study aims to deepen understanding of the NTAO region using the satellite altimetry-derived daily sea level gridded data set provided by the Copernicus Climate Change Service (C3S).The analysis of long-term regional Sea Level Anomaly (SLA) signals in the NTAO reveals a higher rate of sea level rise compared to the global average. The same analysis for regional Eddy Kinetic Energy (EKE) per unit mass and surface geostrophic currents shows declining rates, in contrast to global counterparts.Correlation analysis between SLA and climate indices (CI) uncovered significant links with the North Atlantic Oscillation, Tropical North/South Atlantic, Western Hemisphere Warm Pool, and Southern Oscillation Index. Composite maps of sea surface temperature (SST), sea level pressure (SLP), and wind anomalies, as well as complementary maps with anomalies of SLA, EKE, and ocean circulation, were examined to understand the primary mechanisms behind these correlations. SST emerged as the main forcing factor, with SLP and wind anomalies also contributing to specific regional and index correlations. EKE anomalies further elucidate differences in the anomalies of the surface geostrophic currents in the areas influenced by the key currents in the study region. The findings of this study show an intricate interplay between oceanic dynamics and climate phenomena, shedding light on the complex mechanisms driving changes in the Northeast Tropical Atlantic Ocean.

Assimilation of Surface Geostrophic Currents in the East Sea Using the Ensemble Kalman Filter

The conventional ocean data assimilation process typically involves assimilating hydrographic data, such as temperature and salinity measurements, obtained from both satellites and in-situ observations. This study introduces a novel approach to enhance ocean circulation modeling by assimilating surface geostrophic currents derived from satellite altimetry data using the ensemble Kalman filter. To match the time scales for the variability in the observed surface geostrophic currents and the model currents, the current velocities from the model were low-pass filtered. The optimal cut-off period for the low-pass filter was determined to be 31 days in the East Sea. Eight sensitivity experiments were then conducted to examine the effects of observation error and low-pass filtering during the assimilation of surface geostrophic current data. Assimilation experiments with surface geostrophic current data improved surface currents but had minor negative impacts on the temperature and salinity when compared with assimilation experiments without surface geostrophic current data. Notably, the experiment with an observation error of 10 cm/s for the geostrophic current outperformed the other experiments. Surface geostrophic current assimilation improved the sea surface temperature during winter and effectively modified surface current patterns during autumn in the East Sea. Assimilating satellite-derived surface geostrophic currents in the ocean circulation model thus enhanced the accuracy of surface circulation simulation. This improvement in ocean analysis data offers significant benefits for understanding ocean climate change and for developing marine management strategies.

Characteristics of R2019 Processing of MODIS Sea Surface Temperature at High Latitudes

Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic.

High Vulnerability of Rhodolith Bed Frameworks and Underlying Sediment to Ongoing Ocean Climate Change

ABSTRACTRhodoliths are non‐geniculate, free‐living coralline red algae that can accumulate on the seafloor and form structurally complex habitats supporting highly biodiverse communities termed rhodolith beds. Limited understanding of key rhodolith kinetical attributes and how they scale with environmental variability limits ability to predict changes in rhodolith bed distribution and abundance in a globally changing ocean climate. We carried out two experiments in an oscillatory wave tank to test the effects of (1) rhodolith (Boreolithothamnion glaciale) density and wave velocity on rhodolith displacement and abrasion over a hard substratum and (2) rhodolith density on rhodolith displacement and the stability of underlying sediment. We established that on a hard substratum, (1) a threshold wave velocity of ~0.3 m s−1 is required to induce noticeable displacement in average‐sized rhodoliths and (2) rhodolith abrasion increases (quasi‐linearly) with wave velocity up to this threshold. We also showed that (3) for a same rhodolith density, rhodolith displacement is at least two times smaller on a sedimentary than hard substratum and (4) the loss of sediment underneath rhodoliths decreases (quasi‐linearly) with an increase in rhodolith density. Rates documented and strong scaling with changes in water motion and rhodolith density indicate that relatively small changes in the density of rhodoliths or hydrodynamic forces can quickly destabilize rhodolith bed frameworks and underlying sediment. These rates can be used to develop predictive models of change in rhodolith bed distribution and abundance that can in turn inform development of more accurate, science‐based rhodolith bed conservation strategies.

Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes

AbstractGiven the essential implications of Kuroshio Extension (KE) bimodality on oceanic dynamical environment and climate, the present study investigates the targeted observation schemes, based on the conditional nonlinear optimal perturbation (CNOP) method and a reduced‐gravity shallow‐water model, to improve the forecast skills of transition processes of KE bimodal states. To obtain a suitable observing array, the observation schemes, with different numbers of observation sites and observation distances between two sites, are designed. Furthermore, to demonstrate the superiority of the observing networks in predicting KE transition processes, two existing observation schemes and six random observation schemes are compared with the CNOP‐determined observing array. Based on this, a relatively optimal observing array with three sites and observation distance of 90 km is established, which is mainly located between 31°N and 33°N in the south of Japan. This targeted observing network is universal for two KE transition processes. The removal of initial errors on this array results in the mean prediction improvements of about 9.2% and 22.5% for KE transition processes from the low‐ to the high‐energy state and from the high‐ to the low‐energy state, respectively.

Seasonal variations of the cold intermediate layer on the Newfoundland and Labrador Shelf

Across the Newfoundland and Labrador (NL) continental shelf, sub-surface temperatures remain below 0°C throughout the summer, when the surface is much warmer. This oceanographic feature is known as a cold intermediate layer (CIL), and its properties are assessed annually in the region to support ocean climate research and fisheries management. Monitoring in this region is either too infrequent or too sparse spatially to provide a detailed picture of sub-annual variations, so the GLORYS12 global ocean reanalysis is used for this purpose. GLORYS12 is shown to reproduce both the signs and magnitudes of inter-annual variations in CIL area on NL Shelf transects, although the magnitude of the seasonal cycle may be under-estimated.GLORYS12 indicates that sub-zero water volume is maximized across the region during March and decreases with time, beginning in the south and proceeding north. CIL minima are reached between November to December at most shelf locations, proceeding in the opposite direction. Stratification triggered by surface freshwater is an important contributor to CIL seasonal changes on all transects on the NL Shelf. While the CIL area tends to gradually erode throughout the summer, the downstream advection of CIL water from more northern transects leads to the development of secondary CIL area maxima during late August to early September on the Newfoundland component of the shelf. Onshore intrusions either at the shelf break or via cross-shelf troughs contribute to the erosion of CIL area from below at some transects, although this effect may be exaggerated in the model.

Future directions for deep ocean climate science and evidence-based decision making

IntroductionA defining aspect of the Intergovernmental Panel on Climate Change (IPCC) assessment reports (AR) is a formal uncertainty language framework that emphasizes higher certainty issues across the reports, especially in the executive summaries and short summaries for policymakers. As a result, potentially significant risks involving understudied components of the climate system are shielded from view.MethodsHere we seek to address this in the latest, sixth assessment report (AR6) for one such component—the deep ocean—by summarizing major uncertainties (based on discussions of low confidence issues or gaps) regarding its role in our changing climate system. The goal is to identify key research priorities to improve IPCC confidence levels in deep ocean systems and facilitate the dissemination of IPCC results regarding potentially high impact deep ocean processes to decision-makers. This will accelerate improvement of global climate projections and aid in informing efforts to mitigate climate change impacts. An analysis of 3,000 pages across the six selected AR6 reports revealed 219 major science gaps related to the deep ocean. These were categorized by climate stressor and nature of impacts.ResultsHalf of these are biological science gaps, primarily surrounding our understanding of changes in ocean ecosystems, fisheries, and primary productivity. The remaining science gaps are related to uncertainties in the physical (32%) and biogeochemical (15%) ocean states and processes. Model deficiencies are the leading cited cause of low certainty in the physical ocean and ice states, whereas causes of biological uncertainties are most often attributed to limited studies and observations or conflicting results.DiscussionKey areas for coordinated effort within the deep ocean observing and modeling community have emerged, which will improve confidence in the deep ocean state and its ongoing changes for the next assessment report. This list of key “known unknowns” includes meridional overturning circulation, ocean deoxygenation and acidification, primary production, food supply and the ocean carbon cycle, climate change impacts on ocean ecosystems and fisheries, and ocean-based climate interventions. From these findings, we offer recommendations for AR7 to avoid omitting low confidence-high risk changes in the climate system.

Herbarium specimens reveal a cryptic invasion of polyploid Centaurea stoebe in Europe.

Numerous plant species are expanding their native ranges due to anthropogenic environmental change. Because cytotypes of polyploid complexes often show similar morphologies, there may be unnoticed range expansions (i.e. cryptic invasions) of one cytotype into regions where only the other cytotype is native. We critically revised herbarium specimens of diploid and tetraploid Centaurea stoebe, collected across Europe between 1790 and 2023. Based on their distribution in natural and relict habitats and phylogeographic data, we estimated the native ranges of both cytotypes. Diploids are native across their entire European range, whereas tetraploids are native only to South-Eastern Europe and have recently expanded their range toward Central Europe. The proportion of tetraploids has exponentially increased over time in their expanded but not in their native range. This cryptic invasion predominantly occurred in ruderal habitats and enlarged the climatic niche of tetraploids toward a more oceanic climate. We conclude that spatio-temporally explicit assessments of range shifts, habitat preferences and niche evolution can improve our understanding of cryptic invasions. We also emphasize the value of herbarium specimens for accurate estimation of species´ native ranges, with fundamental implications for the design of research studies and the assessment of biodiversity trends.

A Regional Paleoclimate Record of the Tropical Aeolian Sands during the Last Deglaciation in Hainan, China

The KLD segment of the Kenweiyuan section in Wenchang, Hainan, China is a set of aeolian sand deposits of the Last Deglaciation. The chemical element and heavy mineral analysis performed in this study reveals the chemical index of alteration (CIA) in the segment to be as high as 93–95, with all the heavy minerals identified as stable and extremely stable making up 38–45% of the total. Furthermore, the zircon, tourmaline, and rutile content (ZTR index) of the segment is determined to range between 48–71. The (Al2O3 + TOFE)/SiO2 ratios display obvious fluctuations from old to new strata in the segment, with the low values corresponding to Heinrich event (H1), Dansgaard-Oeschager (D-O), and Younger Dryas (YD) and the high values corresponding to Bølling and Allerød. Our study suggests that these fluctuations are attributed to the alternation of the East Asian winter and summer monsoons. Hainan Island is also impacted by the surface ocean climate of the South China Sea, and characteristics of the KLD segment may be connected to the climate changes in the North Atlantic related to the winter monsoon season or westerlies. Furthermore, the segment presents a clear response to millennium-scale climate changes during the Last Deglaciation on Hainan Island. Based on the high CIA values in the KLD segment, and particularly due to the observed stable detrital minerals, the ratios can be linked to the overall tropical climate, indicating a relatively warm tropical climate environment in the Last Deglaciation in Hainan. The high CIA values also reveal the cause of aeolian sand formation under the tropical environmental conditions in the low latitude region of China in the Late Quaternary.

Identifying the major metabolic potentials of microbial-driven carbon, nitrogen and sulfur cycling on stone cultural heritage worldwide

Microbial activities and biochemical reactions are responsible for the biodeterioration of stone cultural heritage, but information on microbial metabolic potentials remains elusive. Here we profiled microbial community signatures and its functional traits on stone cultural heritage from different climate zones globally using sequencing datasets available publicly. Bacterial community on stone cultural heritage shows a significant separation between BSk (cold semi-arid climate) and Cfb (temperate oceanic climate) with Aw (tropical savanna climate) as a transition region. Importantly, the ubiquity of ammonia oxidizers and nitrite oxidizers on stone cultural heritage under different climates supports the active production and accumulation of nitrates while ammonia/ammonium can be supplied by dinitrogen fixation and dissimilatory nitrate reduction to ammonium (DNRA), together with the hydrolysis of urea, arginine, formamide and cyanate. Sulfate accumulation on stone cultural heritage is mainly resulted from the microbial-driven transformation of organosulfur and thiosulfate, with little dissimilatory reduction of sulfate. Pseudorhodoplanes was identified and reported in elemental sulfur turnover for the first time. Notably, carbon sequestration via the reductive tricarboxylic acid (rTCA) cycle and an incomplete 3-hydroxypropionate/4-hydroxybutynate (HP/HB) cycle other than the Calvin Benson-Bassham (CBB) cycle is also significant on stone cultural heritage under relatively humid climate. These results advance our understanding of microbial metabolic potentials and their genetical partitioning patterns on stone cultural heritage of different climate zones globally.

Evaluations of ground surface heat fluxes and its potential vegetation effects in the permafrost region of northeastern China

Ground surface-air temperature difference (GATD) is the primary source of sensible heat flux from the ground surface, and vegetation inevitably affects it by altering the ground surface characteristics. However, the relationship between vegetation and GATD in the permafrost region has not been well-elucidated. Using meteorological station data, National Oceanic and Atmospheric Administration Climate Data Record Normalized Difference Vegetation Index (NOAA CDR NDVI) data, and some auxiliary data, this study aims to investigate the patterns of GATD in the Northeast China permafrost region and quantify the response of GATD to vegetation changes. The study employs trend analysis, Geodetector, and correlation analysis methods. The results show that the GATD in the Northeast China permafrost region decreased from west to east and the annual mean GATD exhibited an upward from 1982 to 2020. The relationship between NDVI and GATD trend is non-linear, in areas with low vegetation, vegetation changes amplify the GATD trend, while in areas with high vegetation, vegetation changes inhibit the GATD trend. Furthermore, the rate of GATD increase is higher in the discontinuous permafrost (DP) region than in the sporadic permafrost (SP) and isolated permafrost (IP) regions, suggesting that the DP region may have experienced a faster ground surface state change process. The findings of this study will provide new insights for evaluating permafrost degradation and studying the resulting ecological environmental effects in the permafrost region.

Cold Coastal City Neighborhood Morphology Design Method Based on Multi-Objective Optimization Simulation Analysis

In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization method based on a multi-objective optimization algorithm, combined with tools such as Grasshopper, Ladybug, Honeybee and Wallacei, to provide scientific optimization decision intervals for morphology control and evaluation factors at the initial stage of coastal city block design. The effectiveness of this optimization strategy is verified through empirical research on typical coastal neighborhoods in Dalian. The results show that the strategy derived from the multi-objective optimization-based evaluation significantly improves the wind environment and thermal comfort of Dalian neighborhoods in winter and summer: the optimization reduced the average wind speed inside the block by 0.47 m/s and increased the UTCI by 0.48 °C in winter, and it increased the wind speed to 1.5 m/s and decreased the UTCI by 0.59 °C in summer. This study shows that the use of simulation assessment and multi-objective optimization technology to adjust the block form of coastal cities can effectively improve the seasonal wind and heat environment and provide a scientific basis for the design and renewal of coastal cities.

Influencia de la temperatura y humedad ambiental en la incidencia de infecciones de prótesis articulares

BackgroundProsthetic Joint Infection (PJI) are associated with significant morbidity and mortality, underscoring the importance of identifying the related risk factors. The objective of the present study was to evaluate whether environmental factors were correlated with an increase in PJI. Material and methodRetrospective cohort study of 1847 consecutive hip and knee prosthesis surgeries performed at a single center over a 10-year period. All patients who underwent surgery during this period were included, with a minimum follow-up of 2 years. The association between infection cases and environmental temperature and humidity was analyzed for both the day of surgical intervention and the week following the procedure. ResultsSixty-three cases of infection (3.4%) were identified. No statistically significant differences were observed in the infection rate according to the month (p=0.13) or season (p=0.42) in which the surgery was performed. Furthermore, no significant association was found between the incidence of PJI and the average temperature or humidity on the day or week following the prosthesis implantation. ConclusionsEnvironmental temperature and humidity do not influence the incidence of PJI in regions with an oceanic climate. The increase in PJI according to environmental conditions is primarily observed in large-scale studies based on national registries.

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental based AI modelling approach

Optimal humidity control is essential for enhancing crop yields and ensuring favourable growth conditions in greenhouse agriculture. Packed bed devices are effective tools for regulating humidity levels; however, accurately assessing their performance, especially for temperate oceanic climates, is yet explicitly unexplored. The current paper presents a packed bed system using water as the working fluid to increase humidity during winter for greenhouse cultivation. An experimental setup is developed, and a detailed parametric study is conducted. Also, an artificial intelligence (AI) based multi-layer perceptron neural network (MLPNN) is designed to evaluate the performance of packed bed systems under varying environmental conditions with different inlet air flow rates (176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr). The results show that the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda when operating with water at an average temperature of 15.7°C and a flow rate of 12.8 kg/min. The MLPNN is trained with 112 non-repeated datasets and observed that a topology of 2-10-10-1 includes 2 input neurons, 2 hidden layers with 10 neurons each, and 1 output neuron, has high prediction accuracy in estimating Δωa values for the packed bed system. The predictions closely align with experimental data, showing a maximum discrepancy within ±2.5%. This research advances the use of packed bed systems by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental based AI modelling approach

Optimal humidity control is essential for enhancing crop yields and ensuring favourable growth conditions in greenhouse agriculture. Packed bed devices are effective tools for regulating humidity levels; however, accurately assessing their performance, especially for temperate oceanic climates, is yet explicitly unexplored. The current paper presents a packed bed system using water as the working fluid to increase humidity during winter for greenhouse cultivation. An experimental setup is developed, and a detailed parametric study is conducted. Also, an artificial intelligence (AI) based multi-layer perceptron neural network (MLPNN) is designed to evaluate the performance of packed bed systems under varying environmental conditions with different inlet air flow rates (176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr). The results show that the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda when operating with water at an average temperature of 15.7°C and a flow rate of 12.8 kg/min. The MLPNN is trained with 112 non-repeated datasets and observed that a topology of 2-10-10-1 includes 2 input neurons, 2 hidden layers with 10 neurons each, and 1 output neuron, has high prediction accuracy in estimating Δωa values for the packed bed system. The predictions closely align with experimental data, showing a maximum discrepancy within ±2.5%. This research advances the use of packed bed systems by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.