Changes In Atmospheric Conditions Research Articles

Comparison of deep learning and regression-based MPPT algorithms in PV systems

Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve the highest efficiency in the energy generation processes. Maximum power point tracking (MPPT) applications using conventional methods may not be able to follow the global MPP (GMPP) of the PV system under changing atmospheric conditions and they could oscillate around the local MPP. In this study, a machine learning and deep learning (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary to the traditional MPPT applications using current and voltage sensors, the output resistance of the PV module estimation was made by using environmental parameters (such as temperature and radiation) and artificial intelligence algorithms in this study.The LSTM model was compared with artificial neural networks (ANN) and regression methods regarding mean square error (MSE), root mean square error(RMSE) and mean absolute error (MAE) parameters. It has been determined that the LSTM model has a better performance and could more successfully follow MPP compared to the other methods. Finally, after the comparison with the ANN method, it is proved that LSTM gives 37%, 21%, and 31% more successful MSE, RMSE, and MAE results, respectively.

Decentralized adaptive neuro-fuzzy dynamic surface control for maximum power point tracking of a photovoltaic system

The current work proposes a decentralized adaptive dynamic surface control approach for extracting the maximum power from a photovoltaic (PV) system and then regulating the required voltage for charging the battery. In this regard, two cascaded direct current-direct current (DC-DC) converters are utilized. The boost converter is interposed between the PV system and the load to help extract the maximum power. The buck-boost converter is then exploited to maintain the output voltage at a specified level which must meet the battery demand. Therefore, to handle the interactions between the cascaded converters, a decentralized control approach is developed. In the suggested approach, by introducing a nonlinear filter, an effective dynamic surface control (DSC) scheme is proposed with guaranteeing asymptotic tracking convergence. Further, by incorporating a nonlinear compensation term into the proposed control approach, the robustness of the resulting controller is improved. In addition, since the model of the converters is nonlinear with unknown uncertainties, the neuro-fuzzy system is used to estimate lumped uncertainties. The proposed control method has good attributes in terms of having a low tracking error, an excellent transition response, and a quick response to changes in atmospheric conditions. The stability of the whole control system is proved by the Lyapunov stability theorem. Finally, comprehensive simulation results are performed to validate the effectiveness of the suggested control approach.

Regional patterns of vegetation, temperature, and rainfall trends in the coastal mountain range of Chiapas, Mexico

Changes in atmospheric CO2, ocean temperature, and regional vegetation conditions in Mesoamerica indicate that significant trends in temperature and rainfall may have occurred in the Sierra Madre de Chiapas, Mexico. This is an important region for flora and fauna which could be affected by climate trends. We aimed to determine if and where climate trends had occurred in the Sierra Madre and lower elevation regions between 1990 and 2016 (27 years), if these trends were part of longer term (1960-2016 [57 years]) changes, and how changes in large-scale and regional/local conditions may be influencing these trends. In the Sierra Madre, overall minimum daily temperatures increased, maximum temperatures decreased, and the most significant mean temperature trends were cooler during the 27-yr period. Both the start and end of the wet season trended earlier in the year, and wet season rainfall increased significantly. Trends were not significant during the 57-yr period in the Sierra Madre; however, in the adjacent Pacific coast region, significant warmer temperature trends continued during this period. Within regions, there was large variation in temperature and rainfall changes and some local trends were opposite to the regional averages. Large-scale processes of warming sea surface temperatures in the east coast of Mexico, a change from the positive to negative phase of the Pacific Decadal Oscillation and increases in atmospheric CO2 may be influencing these trends. At the regional scale, increases in dense vegetation and evapotranspiration since 1990 may have created characteristics favoring a positive feedback of higher ocean-based moisture and vegetation-based precipitation cycling.

A New FL-MPPT High Voltage DC-DC Converter for PV Solar Application

To reduce the effects of global warming, there is an increasing need for renewable energy sources. Several studies have been carried out on photovoltaic (PV) systems to maximize their potential as an alternative electricity generator. However, various power converters for high voltage ratio applications have multiple drawbacks. This research was carried out to develop a power converter topology connected between the PV and the load for the need. In this research, the high step-up DC-DC converter for high-voltage gain conversion ratio and high efficiency is proposed. Furthermore, the fuzzy logic-based Maximum Power Point Tracking (MPPT) technique connected to the power converter was used to maximize the power converted from PV in changing atmospheric conditions. The MPPT control with fuzzy logic controller (FLC) was analysed and compared with the perturb and observe (P&O) algorithm. The results showed that the FLC algorithm could control the high step-up (HSU) DC-DC converter with an output voltage of 29% higher than the P&O algorithm.

Variety-specific response of bulk stomatal conductance of grapevine canopies to changes in net radiation, atmospheric demand, and drought stress.

In wine growing regions around the world, climate change has the potential to affect vine transpiration and overall vineyard water use due to related changes in daily atmospheric conditions and soil water deficits. Grapevines control their transpiration in response to such changes by regulating conductance of water through the soil-plant-atmosphere continuum. The response of bulk stomatal conductance, the vine canopy equivalent of stomatal conductance, to such changes were studied on Cabernet-Sauvignon, Merlot, Tempranillo, Ugni blanc, and Semillon vines in a non-irrigated vineyard in Bordeaux France. Whole-vine sap flow, temperature and humidity in the vine canopy, and net radiation absorbed by the vine canopy were measured on 15-minute intervals from early July through mid-September 2020, together with periodic measurements of leaf area, canopy porosity, and predawn leaf water potential. From these data, bulk stomatal conductance was calculated on 15-minute intervals, and multiple linear regression analysis was performed to identify key variables and their relative effect on conductance. For the regression analysis, attention was focused on addressing non-linearity and collinearity in the explanatory variables and developing a model that was readily interpretable.Variability of vapour pressure deficit in the vine canopy over the day and predawn water potential over the season explained much of the variability in bulk stomatal conductance overall, with relative differences between varieties appearing to be driven in large part by differences in conductance response to predawn water potential between the varieties. Transpiration simulations based on the regression equations found similar differences between varieties in terms of daily and seasonal transpiration. These simulations also compared well with those from an accepted vineyard water balance model, although there appeared to be differences between the two approaches in the rate at which conductance, and hence transpiration is reduced as a function of decreasing soil water content (i.e., increasing water deficit stress). By better characterizing the response of bulk stomatal conductance, the dynamics of vine transpiration can be better parameterized in vineyard water use modeling of current and future climate scenarios.

Heterogeneous Graphical Model for Non-Negative and Non-Gaussian PM2.5 data

AbstractStudies on the conditional relationships between PM2.5 concentrations among different regions are of great interest for the joint prevention and control of air pollution. Because of seasonal changes in atmospheric conditions, spatial patterns of PM2.5 may differ throughout the year. Additionally, concentration data are both non-negative and non-Gaussian. These data features pose significant challenges to existing methods. This study proposes a heterogeneous graphical model for non-negative and non-Gaussian data via the score matching loss. The proposed method simultaneously clusters multiple datasets and estimates a graph for variables with complex properties in each cluster. Furthermore, our model involves a network that indicate similarity among datasets, and this network can have additional applications. In simulation studies, the proposed method outperforms competing alternatives in both clustering and edge identification. We also analyse the PM2.5 concentrations' spatial correlations in Taiwan's regions using data obtained in year 2019 from 67 air-quality monitoring stations. The 12 months are clustered into four groups: January–March, April, May–September and October–December, and the corresponding graphs have 153, 57, 86 and 167 edges respectively. The results show obvious seasonality, which is consistent with the meteorological literature. Geographically, the PM2.5 concentrations of north and south Taiwan regions correlate more respectively. These results can provide valuable information for developing joint air-quality control strategies.

Reconstructed Centennial Mass Balance Change for Golubin Glacier, Northern Tien Shan

Mass balance measurements for Golubin glacier in Northern Tien Shan, Kyrgyzstan, have been discontinuous over the last century, with significant data gaps. We provide a unique over 100-year-long mass balance series on daily resolution. We applied a temperature index model calibrated with glaciological measurements and validated with secular mass balances derived from independent length change observations. A comparison with other recent geodetic studies reveals good agreement. Golubin lost −0.16 ± 0.45 m w.e. a−1 from 1900/1901 to 2020/2021. From the long-term mass balance time series, we identify a shift to a more negative/less positive regime with time, with a steepening of the ablation and accumulation gradients, especially for the past two decades. We observe a parallel shift of the mass balance gradient accompanied by a rotation of the ablation gradient due to increased ablation at the glacier tongue and accumulation above the equilibrium line altitude. This tendency is believed to intensify in the future, affecting glaciers’ mass balance sensitivity to changes in atmospheric conditions and year-to-year variability and resulting in irregular melt water release feeding the rivers that provide water to Bishkek. These kinds of datasets are sparse for Tien Shan and, yet, indispensable to enhancing our understanding of glacier changes in High Mountain Asia.

Intercomparison of holographic imaging and single-particle forward light scattering in situ measurements of liquid clouds in changing atmospheric conditions

Abstract. Upon a new measurement technique, it is possible to sharpen the determination of microphysical properties of cloud droplets using high resolving power imaging. The shape, size, and position of each particle inside a well-defined, three-dimensional sample volume can be measured with holographic methods without assumptions of particle properties. In situ cloud measurements were carried out at the Puijo station in Kuopio, Finland, focusing on intercomparisons between cloud droplet analyzers over 2 months in September–November 2020. The novel holographic imaging instrument (ICEMET) was adapted to measure microphysical properties of liquid clouds, and these values were compared with parallel measurements of a cloud droplet spectrometer (FM-120) and particle measurements using a twin-inlet system. When the intercomparison was carried out during isoaxial sampling, our results showed good agreement in terms of variability between the instruments, with the averaged ratios between ICEMET and FM-120 being 0.6 ± 0.2, 1.0 ± 0.5, and 1.2 ± 0.2 for the total number concentration (Nd) of droplets, liquid water content (LWC), and median volume diameter (MVD), respectively. This agreement during isoaxial sampling was also confirmed by mutual correlation and Pearson correlation coefficients. The ICEMET-observed LWC was more reliable than FM-120 (without a swivel-head mount), which was verified by comparing the estimated LWC to measured values, whereas the twin-inlet DMPS system and FM-120 observations of Nd showed good agreement both in variability and amplitude. Field data revealed that ICEMET can detect small cloud droplets down to 5 µm via geometric magnification.

Decrease of winter cyclone passage over northern Japan due to the reduction in the regional cyclogenesis associated with cold air outbreak

AbstractIn East Asia, a strong winter monsoon and high baroclinicity cause enhanced cyclone activity, which has been the subject of intensive study. Despite the high frequency of genesis of mesoscale cyclones in this area, most of the studies on the decadal timescale have focused only on synoptic‐scale cyclones. To clarify the interannual variation of the passage of winter cyclones including synoptic‐scale and mesoscale cyclones over and around Japan, a tracking algorithm was applied to 62 winter seasons (December–March) from 1958/1959 to 2019/2020. During the study period, the passage of cyclones around northern Japan showed a decreasing trend. Most cyclones that pass around northern Japan are generated over the northern part of the Sea of Japan, which is an area where cyclogenesis has decreased during the study period. Weather pattern classification based on synoptic scale atmospheric conditions revealed that passage of upper‐level troughs and occurrence of lower‐level cold air outbreaks are the preferential conditions for cyclogenesis in this region. In addition, the intensity of the cold air outbreak in this region was weakened during the study period. These results suggest that changes in atmospheric conditions due to climate change have reduced mesocyclone genesis over the northern Sea of Japan, resulting in the reduction in the number of cyclones passing over and around northern Japan.

The climatic interdependence of extreme-rainfall events around the globe.

The identification of regions of similar climatological behavior can be utilized for the discovery of spatial relationships over long-range scales, including teleconnections. Additionally, it provides insights for the improvement of corresponding interaction processes in general circulation models. In this regard, the global picture of the interdependence patterns of extreme-rainfall events (EREs) still needs to be further explored. To this end, we propose a top-down complex-network-based clustering workflow, with the combination of consensus clustering and mutual correspondences. Consensus clustering provides a reliable community structure under each dataset, while mutual correspondences build a matching relationship between different community structures obtained from different datasets. This approach ensures the robustness of the identified structures when multiple datasets are available. By applying it simultaneously to two satellite-derived precipitation datasets, we identify consistent synchronized structures of EREs around the globe, during boreal summer. Two of them show independent spatiotemporal characteristics, uncovering the primary compositions of different monsoon systems. They explicitly manifest the primary intraseasonal variability in the context of the global monsoon, in particular, the "monsoon jump" over both East Asia and West Africa and the mid-summer drought over Central America and southern Mexico. Through a case study related to the Asian summer monsoon, we verify that the intraseasonal changes of upper-level atmospheric conditions are preserved by significant connections within the global synchronization structure. Our work advances network-based clustering methodology for (i) decoding the spatiotemporal configuration of interdependence patterns of natural variability and for (ii) the intercomparison of these patterns, especially regarding their spatial distributions over different datasets.

Comparison of Extreme Coastal Flooding Events between Tropical and Midlatitude Weather Systems in the Delaware and Chesapeake Bays for 1980–2019

Abstract Coastal flooding is one of the most costly and deadly natural hazards facing the U.S. mid-Atlantic region today. Impacts in this heavily populated and economically significant region are caused by a combination of the location’s exposure and natural forcing from storms and sea level rise. Tropical cyclones (TCs) and midlatitude (ML) weather systems each have caused extreme coastal flooding in the region. Skew surge was computed over each tidal cycle for the past 40 years (1980–2019) at several tide gauges in the Delaware and Chesapeake Bays to compare the meteorological component of surge for each weather type. Although TCs cause higher mean surges, ML weather systems can produce surges just as severe and occur much more frequently, peaking in the cold season (November–March). Of the top 10 largest surge events, TCs account for 30%–45% in the Delaware and upper Chesapeake Bays and 40%–45% in the lower Chesapeake Bay. This percentage drops to 10%–15% for larger numbers of events in all regions. Mean sea level pressure and 500-hPa geopotential height (GPH) fields of the top 10 surge events from ML weather systems show a low pressure center west-southwest of “Delmarva” and a semistationary high pressure center to the northeast prior to maximum surge, producing strong easterly winds. Low pressure centers intensify under upper-level divergence as they travel eastward, and the high pressure centers are near the GPH ridges. During lower-bay events, the low pressure centers develop farther south, intensifying over warmer coastal waters, with a south-shifted GPH pattern relative to upper-bay events. Significance Statement Severe coastal flooding is a year-round threat in the U.S. mid-Atlantic region, and impacts are projected to increase in magnitude and frequency. Research into the meteorological contribution to storm surge, separate from mean sea level and tidal phase, will increase the scientific understanding and monitoring of changing atmospheric conditions. Tropical cyclones and midlatitude weather systems both significantly impact the mid-Atlantic region during different times of year. However, climate change may alter the future behavior of these systems differently. Understanding the synoptic environment and quantifying the surge response and subbay geographic variability of each weather system in this region will aid in public awareness, near-term emergency preparation, and long-term planning for coastal storms.

Synoptic Time Scale Variability in Precipitation and Streamflows for River Basins over Northern South America

The synoptic mode of variability (SMV) refers to changes in atmospheric conditions over periods ranging from 2 to 10 days. In tropical regions, this variability is driven by tropical waves that have a clear signal on the wavenumber–frequency power spectra of precipitation. This study uses the ensemble empirical mode decomposition (EEMD) method to identify the SMV in daily precipitation and streamflows in 47 river basins over northern South America. We found the presence of the frequency bands with periods of 3–12 days and 6–18 days, which agrees with the SMV associated with tropical waves that modulate precipitation over the region. Furthermore, our results reveal that variance explained by the SMV in rainfall over each catchment is greater than the variance explained by those SMV in streamflows, which suggests that catchments efficiently filter out this variability. We found that SMV explains from 5% to 20% of streamflow variability for catchments ranging from 1000 km2 to 5000 km2. Additionally, the variance explained by SMV decreases as a power fit with the catchment area. Thus, this study characterizes the SMV for potential applications on regional hydrology, diagnosis, modeling, short-time forecasting, prediction, and management of water resources.

Mount Everest's photogenic weather during the post‐monsoon

Mount Everest's photogenic weather during the post‐monsoon

Novel hybrid improved bat algorithm and fuzzy system based MPPT for photovoltaic under variable atmospheric conditions

Nowadays, photovoltaic (PV) systems are supplying electrical power networks in developed countries due to their economic, technical and environmental profits. However, to achieve the mentioned advantages, PV systems must produce power at their maximum possible power. Maximum power point tracking (MPPT) controllers are used to extract the maximum power in PV systems. Nevertheless, the performance of conventional MPPT controllers is not acceptable in variable climate circumstances. To manage this challenge, a fuzzy logic controller (FLC) with the improved bat algorithm (IBA) is used to reach efficiently MPP. The IBA algorithm is employed to fine-tune member functions (MFs) of FLC. Using this method, the stochastic behavior of irradiances and temperatures can be addressed. Simulation results in different case studies are done to confirm the edge of the suggested technique over conventional techniques. In addition, a battery storage system is utilized to meet the demand over some hours of the day that the PV system cannot supply the load alone. The results of case studies show that the IBA-FLC has advantages such as generating maximum power and low convergence time in changing atmospheric conditions over conventional MPPT methods.

Design, Fabrication and Characterization of an Adaptive Retroreflector (AR)

Recent work at the U.S. Naval Research Laboratory studied atmospheric turbulence on dynamic links with the goal of developing an optical anemometer and turbulence characterization system for unmanned aerial vehicle (UAV) applications. Providing information on the degree of atmospheric turbulence, as well as wind information and scintillation, in a low size, weight and power (SWaP) system is key for the design of a system that is also capable of adapting quickly to changes in atmospheric conditions. The envisioned system consists of a bi-static dynamic link between a transmitter (Tx) and a receiver (Rx), relying on a small UAV. In a dynamic link, the propagation distance between the Tx/Rx changes rapidly. Due to SWaP constraints, a monostatic system is challenging for such configurations, so we explored a system in which the Tx/Rx is co-located on a mobile platform (UAV), which has a mounted retroreflector. Beam divergence control is key in such a system, both for finding the UAV (increased beam divergence at the Tx) and for signal optimization at the Rx. This led us to the concept of using adaptive/active elements to control the divergence at the Tx but also to the implementation of an adaptive/active retroreflector in which the return beam divergence can be controlled in order to optimize the signal at the Rx. This paper presents the design, fabrication and characterization of a low SWaP adaptive retroreflector.

Metabarcoding of canopy arthropods reveals negative impacts of forestry insecticides on community structure across multiple taxa

Abstract Insecticides used to combat outbreaks of forest defoliators can adversely affect non‐target arthropods. Forestry insecticides typically suppress Lepidoptera larvae which are the cornerstone of the canopy community of deciduous oak forests. The abrupt removal of this dominant component of the food web could have far‐reaching implications for forest ecosystems, yet it is rarely investigated in practice owing to several methodological shortcomings. The taxonomic impediment and the biased nature of arthropod sampling techniques particularly impede the assessment of insecticide impacts on diverse communities. To tackle this issue, we propose an experimental approach combining sampling by pyrethrum knockdown and species determination via DNA metabarcoding, using community subsampling to derive estimates of species abundances. We applied this protocol to investigate the short‐term effects of the insecticides diflubenzuron (DFB) and Bacillus thuringiensis var. kurstaki (BTK) on canopy‐dwelling arthropod communities in German oak woodlands. Our approach allowed us to detect most of the diversity and integrate species abundances in our analyses. By classifying arthropod species into assemblages based on their expected sensitivity rather than coarse taxonomic groupings, we could unveil substantial effects of DFB across multiple taxa 5 weeks after application. Although strong effects on single species appear related to direct toxicity, substantial impacts of DFB on parasitoids and xylophagous beetles suggest that anti‐defoliator treatments can have previously unsuspected indirect effects on some components of forest arthropod communities. The impacts of BTK on community structure were consistent with that of DFB though much weaker. Synthesis and applications. Comparing diversity patterns in the arthropod communities of sprayed and unsprayed oak canopies, our results show that selective insecticides can alter species diversity in presumably non‐sensitive taxa. Even though the ecological significance of these impacts has yet to be assessed in an operational setting, their existence calls for increased regulatory scrutiny on indirect effects. As community approaches become more attainable with the rapid development of DNA metabarcoding, we suggest the inclusion of community‐level end points as regulatory requirements for the approval of forest‐use insecticides.

Solar power-driven permanent magnet synchronous motor coupled with pump load: design, development and control

This paper presents design, development and control of solar photovoltaic (SPV) supply system for permanent magnet synchronous motor (PMSM) driven pump load. The proposed system is very effective in curtailing power demand on existing grid supply on highly dense population areas. An incremental conductance method followed by sliding mode control scheme is used to track SPV array maximum power point (MPP) and vary the speed of the PMSM accordingly under the change in atmospheric conditions. The direct torque control (DTC), voltage source inverter (VSI) is employed to control the PMSM speed and to achieve stable operating point. A prototype of single stage SPV supply-based PMSM coupled with DC machine is developed in laboratory for emulating the centrifugal pump characteristics. Performances under different environmental and operating conditions are obtained on the developed prototype to prove the efficacy of the proposed control.

System Identification Based ARV-MPPT Technique for PV Systems Under Variable Atmospheric Conditions

Different tracking algorithms have been developed to obtain maximum efficiency from the PV power systems under changing atmospheric conditions. Some of these algorithms are effective under uniform irradiance, while the others are effective under partial shading conditions. In this study, a new MPPT method designed with System Identification-SI and named SI-based polynomial ARV is proposed for a PV system under uniform irradiance. The recommended method is also an adaptive reference voltage-ARV-based method. In this method, the model in which the reference voltage is obtained has a simple polynomial structure. This polynomial model has been obtained by assuming that the PV system is a nonlinear black-box type system. The input-output data, which are required for modeling, were created in MATLAB/Simulink environment. Then, by using these data with SI-Toolbox, the mathematical model of the PV system in polynomial structure giving the input-output relationship was obtained. T temperature information was used as model input, which was the generated reference voltage. Simulation studies were carried out under two different changing atmospheric scenarios; and the performance of the proposed method was analyzed. The obtained results were compared with perturb & observe-PO, incremental conductance-IC, constant voltage reference-CVR, artificial neural network-ANN-based ARV, and SI-based nonlinear ARV methods. All simulation results showed that the recommended method is simple structured, applicable, and has high performance.

Estimating Effect of Total Specific Atmospheric Attenuation on Performance of FSO Communication Link in South Africa

In comparison with Radio Frequency (RF), the Free Space Optical Communication (FSOC) provides higher bandwidth, free license operation, and less initial expenditure. However, its susceptibility to changes in atmospheric weather conditions. In this paper, the impact of irradiance fluctuation on FSO systems was estimated using Rytov theory for major cities in South Africa. The extent to which the refractive index structure parameter, propagation distance and link margin affect the optical signal power at the receiver is discussed and the different methods used in evaluating the atmospheric turbulence effect are investigated. In order to achieve the stated aim, meteorological data, altitude, visibility, and wind speed were obtained from the archive of South Africa Weather Services for a period of 3years (2016-2018) over seven locations which include Cape Town, Pretoria, Upington, Bloemfontein, Emalahleni, Polokwane, East London. Results show that Emalahleni was found to possess the poor visibility of 4.4 km because of foggy conditions due to the activities of miners and other environmental factors, followed by East London with average visibility of 4.8 km. From the analysis of link margin, it was shown that FSO link attenuation reduces at higher wavelengths and long link distances due to the effect of geometric and atmospheric losses. The results show that the rate of decrease in link margin is much higher in the inland regions as compared to the coastal regions; therefore, FSO systems are prone to outage during high rainfall and longer range of connections.

Laysan albatross exhibit complex behavioral plasticity in the subtropical and subarctic North Pacific Ocean

Animals that regularly traverse habitat extremes between the subtropics and subarctic are expected to exhibit foraging behaviors that respond to changes in dynamic ocean habitats, and these behaviors may facilitate adaptations to novel and changing climates. During the chick-provisioning stage, Laysan albatross Phoebastria immutabilis parents regularly undertake short- and long-distance foraging trips throughout the vast central North Pacific Ocean. We examined GPS tracking data among chick-provisioning albatrosses in Hawai‘i to characterize habitats during short- and long-distance trips. The study period encompassed a marine heatwave (2014) and the cooling period after an extreme El Niño event (2016), enabling us to examine foraging habitats under novel and changing climates. First passage time and generalized additive mixed models indicated that during 183 short and 110 long trips (n = 32 birds), wind-assisted flight efficiency, proximity to productive areas, and moonlit-searching were important in both subtropical and subarctic habitats. Laysan albatross took foraging trips that had similar lengths and durations in 2014 and 2016 and visited similar areas, indicating that their foraging range did not expand in response to climatic variability. A strategy that uses similar foraging areas across years combined with reliance on environmental processes that enhance flight efficiency (wind) and that enable searching behaviors (moonlight) indicate that Laysan albatross exhibit complex behavioral plasticity that allows them to utilize subtropical and subarctic habitats affected by dynamic climate variability. This strategy may benefit their ability to respond to oceanographic and climatic change, including expanding warm water regions and changing atmospheric conditions influenced by global warming.