Large-scale Variability Research Articles

Rainfall Variability and Teleconnections with Large-Scale Atmospheric Circulation Patterns in West-Central Morocco

Morocco is characterized by a semi-arid climate influenced by the Mediterranean, Atlantic, and Saharan environments, resulting in high variability in rainfall and hydrological conditions. Certain regions suffer from insufficient understanding concerning the spatiotemporal patterns of precipitation, along with facing recurrent periods of drought. This study aims to characterize the current trends and periodicities of precipitation in west-central Morocco at monthly and annual scales, using data from six rain gauges. The link between monthly precipitation and both the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO) indices was tested to identify potential teleconnections with large-scale variability modes. The results reveal interannual variability in precipitation and climate indices, while showing decreasing insignificant trends in annual precipitation. On a monthly scale, temporal precipitation patterns are similar to the annual scale. Furthermore, a remarkably robust and significant component with a periodicity of 6–8 years emerges consistently across all monitoring stations. Intriguingly, this band exhibits a more pronounced presence on the plains as opposed to the mountainous stations. Additionally, it is noteworthy that the NAO modulated winter precipitation, whereas the influence of the WeMO extends until March and April. This mode could be linked to the fluctuations of the WeMO from 1985 to 2005 and, subsequently, to NAO variations. Indeed, this is consistent with the strong significant correlations observed between rainfall and the NAO/WeMO. This study serves as a baseline for future research aiming to understand the influence of climate indices on rainfall in the North African region.

Southern Hemisphere Storm Tracks and Large-Scale Variability: What Do the Latest Reanalyses Say?

Abstract In this study, we explore linkages between the monthly mean 500 hPa height field (Z500) and its high-frequency variability over 2–8-day periods, a proxy for Southern Hemisphere storm track activity. We apply maximum covariance analysis to identify leading modes of covariability between the Z500 and high-frequency variance anomalies on monthly and submonthly time scales, using two reanalysis products. We also calculate covariance with indices of large-scale variability [Southern Annular Mode (SAM), El Niño–Southern Oscillation (ENSO), and zonal wave 3 (ZW3)]. We find large-scale circulation patterns emerge as prominent modes of covariability, particularly SAM and ENSO, accounting for 11.1% and 7.8% of covariability on the monthly scale, respectively. The seasonal cycle plays a prominent role in explaining variability in both SAM and ENSO interactions with the storm track. We find that despite a broadly linear response, both SAM and ENSO teleconnections present additional complexities and nonlinearities. Despite strong ZW3 signals in the mean height field, its influence on the high-frequency variance field remains unclear. We conclude the mean height field is most likely strongly linked with high-frequency variance, but interference from other influences may lead to an inconsistent response. We note both fields have an apparent hemispheric response to the SAM, but ENSO has a more regional response and ZW3 a relatively incoherent response. This suggests ENSO and ZW3 patterns depend less on feedbacks between the storm track and the mean height field than the SAM.

Process-Oriented Diagnosis of Tropical Cyclones in Reanalyses Using a Moist Static Energy Variance Budget

Abstract Global models are frequently used for tropical cyclone (TC) prediction and climate projections but have biases in their representation of TCs that are not fully understood. The objective of this work is to assess how well and how robustly physical processes that are important for TC development are represented in modern reanalysis products and to consider whether reanalyses can serve as an observationally constrained reference against which model representation of these physical processes can be evaluated. Differences in the representation of large-scale environmental variables relevant to TC development do not readily explain the spread in TC climatologies across climate models, as found in prior work, or across reanalysis datasets, as shown here. This motivates the use of process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled and can be used to identify areas to target for model improvement. Using the column-integrated moist static energy (MSE) variance budget, we analyze radiative and surface flux feedbacks across five different reanalyses. We construct an intensity-bin composite of the MSE variance budget to compare storms of similar intensity. Our results point to some fundamental differences across reanalyses in how they represent MSE variance and surface flux and radiative feedbacks in TCs, which could contribute to differences across reanalyses in how they represent TCs, but other factors also likely contribute. Any future work that evaluates these diagnostics in GCMs against reanalyses should do so cautiously, and efforts should be undertaken to provide a true observational estimate of these processes.

Assessment of Rainfall Variability over Karnataka state in India

Karnataka state situated in the southern part of India is surrounded by a long coast line and western ghat mountain regions on the western side. The rainfall distribution shows a large variability in different parts of the state. North interior part in the state receives less rainfall as compared to the south interior region, which receives moderate rainfall. Although coastal Karnataka witnesses a high rainfall and is one of the most important regions in the country, in recent years the state has faced drought because of less rainfall which may be attributed to the regional climate change. Because of the decreasing trend in rainfall almost all the important sectors like agriculture and water resources have been affected a lot in Karnataka. In this work, assessment of the spatial and temporal distribution of monsoon rainfall is presented in terms of the climatology, variability, trend etc. The time series analysis also indicated the increasing trend of rainfall as a whole but the trend in recent decades is very less as compared to the long term. The El-Niño Southern Oscillation (ENSO) and the regional rainfall pattern relation is also explored. The increase in heavy rainfall in recent times is observed from the analysis. This study can be useful in addressing regional climate change, understanding the local and large scale variability impacts and in can be a good input for preparing the policy formulation and pro-active disaster management.

What is the probability that a drought will break in Australia?

Large scale modes of climate variability influence rainfall and soil moisture in southeastern Australia, and extended dry conditions have been associated with a lack of climate mode phases conducive to wetter conditions. However, the role of large-scale climate variability in breaking ongoing soil moisture droughts has not been well quantified, and the utility of large-scale signals in drought recovery assessments have not been explored. Here, we study the influence of El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on the probability of soil moisture drought breaking in eastern and southeastern Australia, using logistic regression modelling. A long-term historical dataset from the Australian Water Resources Assessment Landscape (AWRA-L) model is used for the assessment. The probability estimates from the logistic regression modelling validate well against observed probability of a drought ending. We then use model estimates to understand the probability contributions from different climate modes. We show that there is a seasonal pattern in soil moisture drought breaking probabilities with higher probabilities in austral summer in eastern Australia and summer/autumn in southeastern Australia. ENSO has the largest influence on probabilities in winter with extreme opposite phases of the mode resulting in regional average probability differences of 15–26%. The IOD exhibits the largest influences during spring and winter and opposite phases result in differences of about 18%. The method can be used to estimate soil moisture drought breaking probabilities in near real-time during drought events, and may assist decision making by managers engaged in drought risk and water resources planning.

Statistical Connections between Large-Scale Climate Indices and Observed Mean and Extreme Temperatures in the US from 1948 to 2018

In order to better understand the extent to which global climate variability is linked to the frequency and intensity of heat waves and overall changes in temperature throughout the United States (US), correlations between long-term monthly mean, minimum, and maximum temperatures throughout the contiguous US on the one hand and low-frequency variability of multiple climate indices (CIs) on the other hand are analyzed for the period from 1948 to 2018. The Pearson’s correlation coefficient is used to assess correlation strength, while leave-one-out cross-validation and a bootstrapping technique (p-value) are used to address potential serial and spurious correlations and assess the significance of each correlation. Three parameters defined the sliding windows over which surface temperature and CI values were averaged: window size, lag time between the temperature and CI windows, and the beginning month of the temperature window. A 60-month sliding window size and 0 lag time resulted in the highest correlations overall; beginning months were optimized on an individual site basis. High (r ≥ 0.60) and significant (p-value ≤ 0.05) correlations were identified. The Western Hemisphere Warm Pool (WHWP) and El Niño/Southern Oscillation (ENSO) exhibited the strongest links to temperatures in the western US, tropical Atlantic sea surface temperatures to temperatures in the central US, the WHWP to temperatures throughout much of the eastern US, and atmospheric patterns over the northern Atlantic to temperatures in the Northeast and Southeast. The final results were compared to results from previous studies focused on precipitation and coastal sea levels. Regional consistency was found regarding links between the northern Atlantic and overall weather and coastal sea levels in the Northeast and Southeast as well as on weather in the upper Midwest. Though the MJO and WHWP revealed dominant links with precipitation and temperature, respectively, throughout the West, ENSO revealed consistent links to sea levels and surface temperatures along the West Coast. These results help to focus future research on specific mechanisms of large-scale climate variability linked to US regional climate variability and prediction potential.

Non-stationary large-scale statistics of precipitation extremes in central Europe

Extreme precipitation shows non-stationarity, meaning that its distribution can change with time or other large-scale variables. For a classical frequency-intensity analysis this effect is often neglected. Here, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage, maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. Models are chosen with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large-scale information. Also, the new model enables insights into the effect of large-scale variables on extreme precipitation. Results suggest that the probability of extreme precipitation increases with time since 1950 in all seasons. High probabilities of extremes are positively correlated with blocking situations in summer and with temperature in winter. However, they are negatively correlated with blocking situations in winter and temperature in summer.

Knowledge-driven adaptive evolutionary multi-objective scheduling algorithm for cloud workflows

Workflow scheduling in cloud platforms is a highly challenging issue because it faces multiple conflicting optimization objectives and large-scale decision variables. Most of the existing multi-objective workflow scheduling algorithms regard the focused problems as black boxes, and optimize large-scale decision variables as a whole. This leads to inefficiency in searching solution spaces that grow exponentially with the increase of decision variables. To compensate the above deficiency, this paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm, KAMSA for short, to optimize makespan and cost of workflow execution in cloud platforms. Specifically, we excavate the knowledge that adjustment of a task’s execution only affects its successor tasks to divide large-scale decision variables into a series of groups, so as to give play to the strengths of divide-and-conquer technology to improve the evolutionary search efficiency. Moreover, we develop an adaptive resource allocation scheme to reward more evolution opportunities for groups with high contributions to further improve the evolutionary search efficiency. We compare the proposed KAMSA with five state-of-the-art competitors in the context of 20 real-world workflows and the Amazon elastic compute cloud (EC2). The comparison results verify the KAMSA’s advantages by prevailing over the five competitors on 18 out of the 20 test cases with respect to the metric hypervolume.

Personality-dependent nest site selection and nest success during incubation in wild chestnut thrushes

In birds, little is known about how individuals choose nest sites based on their personality traits. Here, we investigate whether a female's personality (activity and breathing rate) can affect patterns of nest site selection at different spatial scales in a wild population of chestnut thrush (Turdus rubrocanus) and determine whether nest site characteristics and female personality traits affect clutch size and nest success during incubation. We found that neither activity nor breathing rate were associated with large-scale nesting habitat variables. At the fine-scale level, more active females chose nest sites with greater nest lateral concealment. Females with higher breathing rates laid smaller clutch sizes than individuals with lower breathing rates. Nests of females with lower breathing rate had higher nest success during incubation. This work highlights the relationships between personality and nest site selection in birds, and the important role of female personality traits in reproductive success.

El Niño Southern Oscillation (ENSO) Implication towards Crocodile River Water Quality in South Africa

This paper investigates the impact of ENSO on water quality by looking at COD, SS, and Escherichia coli along the Crocodile River. Water samples were collected between 2016 and 2021 at three strategic sites on the river and were tested in an accredited laboratory. Python (version 3.8), Spyder and Microsoft Excel 2019 were used to analyze the data. The highest COD concentration (800 mg/L) was recorded at the White River site during El Niño, followed by 600 mg/L during the normal period, and 240 mg/L during the La Niña period. In 2019 during La Niña and the normal period, the E. coli levels were centered in one place at 60 cfu/100 mL, while in 2021 no E. coli levels were detected from the La Niña, El Niño, and normal periods. The suspended solids in this study were more prevalent in the White River (upstream) during the El Niño period. These analyses demonstrate that it is possible to evaluate the local effects associated with large-scale climate variability.

Comparison of data-driven methods for linking extreme precipitation events to local and large-scale meteorological variables

Extreme precipitation events can lead to severe negative consequences for society, the economy, and the environment. It is therefore crucial to understand when such events occur. In the literature, there are a vast number of methods for analyzing their connection to meteorological drivers. However, there has been recent interest in using machine learning methods instead of classic statistical models. While a few studies in climate research have compared the performance of these two approaches, their conclusions are inconsistent. To determine whether an extreme event occurred locally, we trained models using logistic regression and three commonly used supervised machine learning algorithms tailored for discrete outcomes: random forests, neural networks, and support vector machines. We used five explanatory variables (geopotential height at 500 hPa, convective available potential energy, total column water, sea surface temperature, and air surface temperature) from ERA5, and local data from the Danish Meteorological Institute. During the variable selection process, we found that convective available potential energy has the strongest relationship with extreme events. Our results showed that logistic regression performs similarly to more complex machine learning algorithms regarding discrimination as measured by the area under the receiver operating characteristic curve (ROC AUC) and other performance metrics specialized for unbalanced datasets. Specifically, the ROC AUC for logistic regression was 0.86, while the best-performing machine learning algorithm achieved a ROC AUC of 0.87. This study emphasizes the value of comparing machine learning and classical regression modeling, especially when employing a limited set of well-established explanatory variables.

Synoptic meteorological conditions and contributing factors to air quality during the SIJAQ campaign

The Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) field campaign was conducted from October 10 to November 25, 2021, to assess air quality across the Korean Peninsula and validate the results of the Geostationary Environment Monitoring Spectrometer (GEMS) employing remote sensing, modeling, and satellite, aircraft, and ground measurements. To integrate all measurements collected during the campaign, synoptic features across the Korean Peninsula were analyzed. We identified cyclones and anticyclones by tracing weather charts and divided the campaign into five periods (P1 to P5): P1 - Sustained marine stagnation (October 10–28, 2021), P2 - Transitional stagnant high (October 29–November 7, 2021), P3 - Continental migratory low (November 8–14, 2021), P4 - Continental migratory high (November 15–21, 2021), and P5 - Continental migratory low (November 22–25, 2021). The synoptic features, such as the locations of stagnant or migratory cyclones and anticyclones were analyzed during the periods, in association with levels of particulate matter with a diameter of less than 2.5 μm (PM2.5 levels) over the Seoul metropolitan area (SMA). The synoptic characteristics showed that the presence of a high pressure system was well associated with the occurrence of high PM2.5 level over the SMA, and a pattern of near-surface divergence and descending motions originating from the mid-troposphere. In addition, we identified and compared two PM2.5 level peaks caused by local accumulation and transboundary transport process. We also investigated the underlying synoptic meteorological mechanisms of these peaks in terms of large-scale dynamic variables, such as geostrophic wind speed, transient synoptic scale features, vorticity, and vorticity advection at 850 hPa geopotential height, which are responsible for transboundary transport and local accumulation-driven process of PM2.5 in the SMA.

A fast and efficient teaching-learning-based optimization algorithm for large-scale multi-objective optimization problems

ABSTRACT Multi-objective optimization problems with large-scale decision variables, known as LSMOPs, are characterized by their large-scale search space and multiple conflicting objectives to be optimized. They have been involved in many emergent real-world applications, such as feature and instance selection, data clustering, adversarial attack, vehicle routing problem, and others. In this work, we propose a new teaching-learning-based optimization algorithm to effectively tackle large-scale multi-objective optimization problems. The proposed approach ensures both the diversity of solutions, requested for high dimensionality of LSMOPs, and the balance between the exploitation and exploration during the optimization process. The experimental studies conducted on 54 instances of a large-scale multi-objective optimization problems test suite and the comparisons against well-known and state-of-the-art algorithms have shown the superiority of the proposed algorithm in terms of Pareto front approximation and computation time by using limited evaluation budget.

АНАЛИЗ ПРЕДСКАЗУЕМОСТИ СОСТОЯНИЯ АТМОСФЕРЫ И ВЕРХНЕГО СЛОЯ ОКЕАНА НА СРОК ДО 5 ЛЕТ С ПОМОЩЬЮ РЕТРОСПЕКТИВНЫХ ПРОГНОЗОВ МОДЕЛИ КЛИМАТА INMCM5

A series of hindcasts for 1-5 years were performed using the INM RAS climate model (INMCM5). Each forecast started on November 1 every year during 1980-2020. The predictability of large-scale climate variability modes on timescales from a season to 5 years is assessed with the series of INMCM5 hindcasts. The spatial distribution of correlation coefficients of the temporal anomalies of the annual mean ocean water temperature averaged for the depths from 0 to 300 m and correlation coefficients of temporal anomalies of the Arctic sea ice area are calculated using the hindcasts, historical experiments and their extensions according to the SSP3-7.0 scenario of the INM RAS climate model. The contributions of initial conditions and external forcings to the predictability of the anomaly correlation coefficients are evaluated.

Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling

Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.

Large-scale spatial variation and phenotypic integration in three Argynnini species inform about functions and evolutionary drivers of butterfly wings

Understanding how large-scale environmental variability may shape the distribution of phenotypic variation remains central to evolutionary biology. Across-species comparisons of trait variation alongside environmental gradients may offer valuable insights into how different species may respond to similar selective pressures. We conducted a comparative morphological study (>32° latitude and >47° longitude) on three closely related Argynnini butterfly species, Speyeria aglaja, Fabriciana adippe, and F. niobe. We measured wing size and coloration to assess (1) whether they respond similarly or differently to environmental factors (longitude, latitude, altitude, temperature, precipitation, solar radiation, wind speed); (2) if these factors correspond with those associated with the species’ genetic structure based on a previous study; and (3) whether correlations between phenotypic traits within individuals are species-specific. We found common and species-specific associations of climatic (precipitation, wind speed) and geographic (longitude, altitude) factors with the composite phenotypic variation. Wing size was associated with different variables in the studied species, while melanisation mainly increased in cooler regions in all species, suggesting that the need for temperature regulation is a strong selective pressure on melanisation. Wing size was associated with the genetic structure in all species, highlighting the functional importance of this trait. The environmental drivers associated with the phenotypic variation in S. aglaja and F. adippe were largely the same as those associated with their genetic structure, hinting at a genetic underpinning of the observed morphological variation due to local adaption. We report some distinct intraspecific trait correlations in S. aglaja and F. adippe, indicative of independent phenotypic integration. These phenotypes seem to be associated with protection against predators and thermal regulation in the respective habitats of both species, suggesting that similar selective pressures have resulted in the evolution of different trait combinations. Some of the inter-specific differences could be related to diverging niche breadths and dispersal capacities, exemplifying that the evolution of trait integration and spatial phenotypic differentiation may differ between closely related species with overlapping distribution ranges. Our findings highlight the importance of comparative assessments of variation, and demonstrate that the relative effects of drivers of variability may vary between sister species.

POPULATION DYNAMICS OF THE CLIMBING MICE Rhipidomys itoan (COSTA, 2011) IN THE BRAZILIAN ATLANTIC FOREST

One of the fundamental questions in population ecology is what factors influence size and stability of populations. Rodents are one of the best-studied mammalian species regarding population dynamics, but little knowledge about rodent dynamics in tropical environments is available, especially in tropical rainforests, where special adaptations in behavior and physiology may have evolved. Here, we used a detailed dataset from a long-term monitoring program to identify the feedback structure (density dependence) and determine the effect of local (rainfall and temperature) and large-scale (El Niño events) climate variables on population dynamics of the climbing mice Rhipidomys itoan in the Brazilian Atlantic Forest. Individuals were captured and marked (ear tags) from August 2000 to February 2016 and monitored every other month in three trapping grids in trapping sessions of five consecutive nights. Time series diagnostic tools were used to identify the feedback structure and linear models were used to evaluate the relationship between climate and population size. The population was dominated by a first-order negative feedback, possibly as a consequence of intraspecific competition for resources and/or the effect of a generalist predator. Mean maximum temperature with and without one-year time lag acted as the main exogenous factor negatively affecting the population dynamics of this Cricetidae rodent, mainly at low densities. These results have important ecological and conservation implications, since this is the first study elucidating R. itoan population dynamics, a new species classified as Data Deficient by the Chico Mendes Institute for the Biodiversity Conservation of Brazil.

Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network

ABSTRACT Sparse and irregular climate observations in many developing countries are not enough to satisfy the need of assessing climate change risks and planning suitable mitigation strategies. The wide-used statistical downscaling model (SDSM) software tools use multi-linear regression to extract linear relations between large-scale and local climate variables and then produce high-resolution climate maps from sparse climate observations. The latest machine learning techniques (e.g. SRCNN, SRGAN) can extract nonlinear links, but they are only suitable for downscaling low-resolution grid data and cannot utilize the link to other climate variables to improve the downscaling performance. In this study, we proposed a novel hybrid RBF (Radial Basis Function) network by embedding several RBF networks into new RBF networks. Our model can well incorporate climate and topographical variables with different resolutions and extract their nonlinear relations for spatial downscaling. To test the performance of our model, we generated high-resolution precipitation, air temperature and humidity maps from 34 meteorological stations in Bangladesh. In terms of three statistical indicators, the accuracy of high-resolution climate maps generated by our hybrid RBF network clearly outperformed those using a multi-linear regression (MLR), Kriging interpolation or a pure RBF network.

Impact of climate change on climate extreme indices in Kaduna River basin, Nigeria.

This study examined the impact of climate change on climate extreme indices in the Kaduna River basin, Nigeria. Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal-Wallis test, and the modified Mann-Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980-2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. The CMIP6 should be used to compare with the results of this study to provide a detailed assessment of the current implication of climate change on the catchment.

Spatiotemporal variability of micronekton at two central North Pacific Fronts

The North Pacific Subtropical Frontal Zone (STFZ) seasonally aggregates economically important fish and protected species. The aggregation of top predators is hypothesized to be a response to convergent flow at the prominent thermohaline Subtropical Front (STF) in the STFZ and a sharp northward increase in primary productivity, the Transition Zone Chlorophyll Front (TZCF), which is thought to link primary productivity to top predators via secondary and tertiary consumers. Given existing data gaps in our knowledge on forage biomass, distribution, and composition in the area, characteristics of micronekton, forage for top predators, were investigated using in situ multi-frequency active acoustics from three springtime shipboard surveys conducted along the 158°W meridional. The effects of STF and TZCF on micronekton was accessed using in situ CTD profiles. Results of this study showed a significant positive effect of the STF on micronekton biomass. The acoustic data implied that the STF also acted as a boundary for the distribution of micronekton with differing taxonomic composition from south to its north. The TZCF, as well as Chl-a concentrations, did not show a significant effect on micronekton relative biomass or composition that might be due to effects of larger-scale variability masked in the in situ data. Contrary to expectation, significantly higher relative micronekton biomass was associated with higher temperatures, the mechanisms of which still need to be determined.