Seasonal Scales Research Articles

Plankton Community Changes and Nutrient Dynamics Associated with Blooms of the Pelagic Cyanobacterium Trichodesmium in the Gulf of Mexico and the Great Barrier Reef

Blooms of the harmful dinoflagellate Karenia brevis on the West Florida Shelf (WFS), Gulf of Mexico, are hypothesized to initiate in association with the colonial cyanobacterium Trichodesmium spp. and benefit from dissolved organic nitrogen (DON) release derived from N2-fixation by the cyanobacteria. Previous studies have detected DON release using direct experimental measurements, but there have been few studies that have followed nutrient release by in situ blooms of Trichodesmium and the associated plankton community. It was determined that long-term Trichodesmium spp. and Karenia brevis abundances on the WFS were related, following a 2-month lag. A separate Eulerian study of a Trichodesmium erythraeum bloom event was conducted over 9 days on the Great Barrier Reef. Concentrations of T. erythraeum increased over the course of the study, with coincident increases in dinoflagellate abundance and decreases in diatom abundance. Inside the bloom, concentrations of NH4+, PO43−, and DON increased significantly. The copepod grazer Macrosetella gracilis also increased in abundance as T. erythraeum numbers increased, contributing to nutrient release. Copepod grazing rates were measured, and N release rates estimated. Together, these studies show that Trichodesmium blooms have consequences for dinoflagellate abundance at both seasonal and ephemeral scales via direct and indirect N release.

Seasonal and interannual variations of suspended particulate matter in a West-African lagoon (Nokoué lagoon, Benin): Impact of rivers and wind

This study, based on five years of monthly in situ data collected from 2018 to 2022, examines the seasonal and interannual fluctuations of suspended particulate matter (SPM) concentration in Nokoué Lagoon, Benin. Seasonally, SPM exhibits significant variations primarily influenced by changes in river discharge. During low-flow periods (December to May), SPM concentrations are relatively low (<15 mg L-1) throughout the lagoon. During this time, slight temporal variations are correlated with wind energy and likely associated with wind-induced resuspension of sediments. This is confirmed by slightly higher concentrations of SPM in the bottom layers compared to the surface. Resuspension appears to be lower in the west than in the east, likely due to the increased presence of acadjas (brush parks) in the west, reducing fetch and wind intensity, thus decreasing resuspension. At the onset of the river flood period (July–August), associated with the West African monsoon, increased river flow generates a significant turbid plume extending from the northeast of the lagoon to the Cotonou channel, connecting the lagoon to the Atlantic Ocean. SPM levels then increase considerably (>100 mg L−1), with a pronounced SPM gradient from the western to eastern regions of the lagoon. The less dense freshwater laden with sediment from the rivers flows over the denser saline water of the lagoon, leading to slightly higher SPM concentrations in the surface layers. Between September and November, SPM concentration gradually decreases as river flows reach their peak values. Thus, on a seasonal scale, the relationships between SPM and river discharge show a temporal lag, resulting in a clockwise hysteresis cycle. This is explained by the early mobilization of fine sediments during rising river flows, followed by reduced sediment availability and dilution effects as the flood peaks. On an interannual scale, SPM variations are relatively low with slight temporal shifts observed in the formation and expansion of the turbid plume and peak SPM levels. The total SPM mass in the lagoon ranges from 0.2 to 0.3 × 104 tonnes during low-flow periods to 20-30 × 104 tonnes at the onset of flooding. We also discuss uncertainties associated with SPM determination, estimated at approximately 5–15%. This study leverages a unique database in West Africa and provides valuable insights into the hydro-sedimentary dynamics of Nokoué Lagoon.

A new method for estimating megacity NOx emissions and lifetimes from satellite observations

Abstract. We present a new method for estimating NOx emissions and effective lifetimes from large cities based on NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) (PAL dataset, May 2018–November 2021). As in previous studies, the estimate is based on the downwind plume evolution for different wind directions separately. The novelty of the presented approach lies in the simultaneous fit of downwind patterns for opposing wind directions, which makes the method far more robust (i.e., less prone to local minima with nonphysically high or low lifetimes) than a single exponential decay fit. In addition, the new method does not require the assumption of a city being a “point source” but also derives the spatial distribution of emissions. The method was successfully applied to 100 cities worldwide on a seasonal scale. Fitted emissions generally agree reasonably with the Emissions Database for Global Atmospheric Research (EDGAR) v6 (R=0.72) and are on average 14 % lower, while estimated uncertainties are still rather large (≈ 30 %–50 %). Lifetimes were found to be rather short (2.44 ± 0.68 h) and show no distinct dependency on season or latitude, which might be a consequence of discarding observations at high solar zenith angles (&gt;65°). The main limitations of this and similar methods are the underlying assumptions of steady state (meaning constant emissions, wind fields and chemical conditions) within about 100 km downwind from a city, which is probably a simplification that is too strong in order to reach higher accuracies.

A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.

Green hydrogen vs green ammonia: A hierarchical optimization-based integrated temporal approach for comparative techno-economic analysis of international supply chains

Recent global trends have increasingly focused on sustainable and green energy solutions,transitioning away from fossil fuels. Green hydrogen (GH) and green ammonia (GA) have emerged as promising contenders for energy carriers in international supply chains. Our manuscript delivers a thorough comparative analysis of GH and GA, acknowledging the complex interrelations among synthesis, storage, distribution, and conversion processes. This evaluation also incorporates the variability of weather, energy demand, and the delays inherent in shipping, on both hourly and seasonal scales. The study features a detailed case analysis of nine international trading routes, utilizing a hierarchical optimization framework and conducting comparative economic evaluations. Additionally, the study delves into a sensitivity analysis, exploring factors such as economies of scale and the operational constraints of water electrolyzer. One of the key findings of our research is the identification of GA as a more favorable option for an international energy carrier, particularly when considering renewable energy's seasonality and intermittency. GA's superior storage and transportation properties significantly mitigate uncertainties within the supply chain, offering a more robust solution in the context of green energy transportation and logistics. Furthermore, the techno-economic analyses survey their impact on each supply chain and reveal the interconnectedness between each supply chain element – power, conversion, storage, and transportation.

Moving to a new normal: Analysis of shifting climate normals in New Zealand

AbstractNew Zealand's National Institute of Water and Atmospheric Research publishes climate normals for New Zealand that are used for reporting the regional state of the climate, climate extremes and variability. Temperature and precipitation patterns are affected by both anthropogenic climate change and natural climate variability which in turn affect the climatological normal values calculated every decade. This study investigates how New Zealand's normals for temperature and precipitation have shifted over time at the national, regional and seasonal scales from the 1941–1970 period to the 1991–2020 period. Contrary to WMO recommendations, but aligned with many other countries, New Zealand's climate normals have traditionally not undergone homogenisation. The impact of introducing some homogenisation in the latest 1991–2020 normals, in contrast to historical non‐homogenized station normals, has been assessed using a new homogenized “Seventeen‐Station” temperature series from 1941 to 2020. We demonstrate that interpolating sparse non‐homogenized normals spatially to a grid can produce significant erroneous patterns and therefore undermine the accuracy of the conclusions drawn when using such normals. We find that the historical non‐homogenized temperature normals have a consistent negative bias in the long‐term trend at national, regional and seasonal scales. Our analysis of homogeneity tested precipitation showed consistent decreases at a national scale across all normal periods relative to the 1951–1980 precipitation normal. We also highlight how fixed period temperature and precipitation normals do not fully reflect the current state of a climate that is influenced by decadal variability and global warming. To derive normals fit for use in a changing climate it is suggested that automated methods for broad data homogenisation be developed along with alternative methods to derive normals that account for a non‐stationary climate.

Pollinator competition and the contingency of nectar depletion during an early spring resource pulse.

Concerns about competition between pollinators are predicated on the assumption of floral resource limitation. Floral resource limitation, however, is a complex phenomenon involving the interplay of resource production by plants, resource demand by pollinators, and exogenous factors-like weather conditions-that constrain both plants and pollinators. In this study, we examined nectar limitation during the mass flowering of rosaceous fruit trees in early spring. Our study was set in the same region as a previous study that found severe nectar limitation in summer grasslands. We used this seasonal contrast to evaluate two alternative hypotheses concerning the seasonal dynamics of floral resource limitation: either (H1) rates of resource production and consumption are matched through seasonal time to maintain a consistent degree of resource limitation, or (H2) a mismatch of high floral resource production and low pollinator activity in early spring creates a period of relaxed resource limitation that intensifies later in the year. We found generally much lower depletion in our spring study compared to the near 100% depletion found in the summer study, but depletion rates varied markedly through diel time and across sampling days, with afternoon depletion rates sometimes exceeding 80%. In some cases, there were also pronounced differences in depletion rates across simultaneously sampled floral species, indicating different degrees of nectar exploitation. These findings generally support the seasonal mismatch hypothesis (H2) but underscore the complex contingency of nectar depletion. The challenge of future work is to discern how the fluctuation of resource limitation across diel, inter-diel, and seasonal time scales translates into population-level outcomes for pollinators.

Annual and Seasonal Dynamics of CO2 Emissions in Major Cities of China (2019–2022)

To control the growth of CO2 emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies.

Spatial and temporal variability of martian water-ice cloud effective radius in EMIRS thermal infrared observations

New analyses of thermal infrared spectra obtained from the Emirates Mars Infrared Spectrometer (EMIRS) allow for retrieval of water-ice cloud effective radius, reff,ice, in the lower atmosphere of Mars across spatial, seasonal, and daytime diurnal scales. Using a one Martian-year observational dataset the mean retrieved reff,ice is 3.1 μm (standard deviation, σ = 1.5 μm) for an assumed effective variance, νeff,ice = 0.1, and with most individual values between 1 and 10 μm. Sensitivity analyses show the uncertainty associated with reff,ice is largely inversely proportional to water-ice cloud optical depth, τice. As a result, our retrieval is generally applicable to water-ice clouds with τice of at least ∼0.02 to 0.08 at 825 cm−1. Seasonal results show mean reff,ice values of 4.3 μm (σ = 1.9 μm) during aphelion season (Ls = 40°–140°), associated with larger particle sizes in the aphelion cloud belt (ACB), and 2.5 μm (σ = 0.8 μm) during perihelion season (Ls = 225°–360°). Diurnal analyses between roughly 06:00 and 20:00 local true solar time indicate the largest variability occurs in ACB clouds, with effective radii tending to be smaller during midday (zonal-mean reff,ice of 4–5 μm) as compared to the mornings and afternoons (zonal-mean reff,ice of 6–7 μm). Relatively large spatial gradients in reff,ice within the ACB are observed, including a seasonal mean reff,ice of ∼8 μm in the Tharsis region and regions in the southern mid-latitudes with seasonal mean reff,ice of ∼2 μm. Spatial variability in reff,ice tends to be smaller throughout the rest of the year, though with some similar patterns. This includes several time periods between regional dust storms when a low-latitude band of water-ice clouds formed in the afternoons near Tharsis, which grew in both optical depth and effective radius to maxima in the early evenings. Lastly, changes in reff,ice are found to be generally positively correlated with changes in τice and negatively correlated with estimated cloud height, though reff,ice distributions also show this to be a more complex relationship than simple averages might suggest.

Dynamics of Water Use Efficiency of Coniferous and Broad-Leaved Mixed Forest in East China

The aim of our study is to understand the patterns of variation in water use efficiency (WUE) in coniferous and broad-leaved mixed forest ecosystems across multiple scales and to identify its main controlling factors. We employ the eddy covariance method to gather data from 2017, 2018, and 2020, which we use to calculate the gross primary productivity and evapotranspiration of these forests in East China and to determine WUE at the ecosystem level. The mean daily variation in WUE ranges from 4.84 to 7.88 gC kg−1 H2O, with a mean value of 6.12 gC kg−1 H2O. We use ridge regression analysis to ascertain the independent effect of environmental factors on WUE variation. We find that WUE responds differently to environmental factors at different time scales. In mixed conifer ecosystems, temperature and relative humidity emerge as the most significant environmental factors influencing WUE variability. Especially at the seasonal scale, temperature and relative humidity can explain more than 51% of the WUE variation. Our results underscore the varied effects of environmental factors on WUE variation across different time scales and aid in predicting the response of WUE to climate change in coniferous and broad-leaved mixed forest ecosystems.

Space-Time Variability of Drought Characteristics in Pernambuco, Brazil

Drought is the most complex natural hazard that can occur over large spatial scales and during long time periods. It affects more people than any other natural hazard, particularly in areas with a dry climate, such as the semiarid region of the Brazilian Northeast (NEB), which is the world’s most populated dry area. In this work, we analyzed trends and the spatial distribution of drought characteristics (frequency, affected area, and intensity) based on the Standardized Precipitation Index (SPI) on annual (SPI-12) and seasonal (SPI-3) scales. The study used monthly precipitation data recorded between 1962 and 2012 at 133 meteorological stations in Pernambuco State, Brazil, which is located in the eastern part of the NEB and has more than 80% of its territory characterized by a semiarid climate. The regions of Sertão, Agreste, and Zona da Mata of Pernambuco were considered for comparison. The Mann–Kendall and Sen’s slope tests were used to detect the trend and determine its magnitude, respectively. The results indicated that annual droughts in the state of Pernambuco became more frequent from the 1990s onwards, with summer having the greatest spatial coverage, followed by winter, autumn, and spring. Sertão presented a greater number of stations with a significant positive trend in drought frequency. Regarding the drought-affected area, global events occurred in a greater number of years on an annual scale and during the summer. Trend analysis pointed to an increase in areas with drought events on both scales. As for the drought intensity, the entire state of Pernambuco experienced drought events with high intensity during the autumn. The relationship between drought characteristics indicated an increase in the affected area as the result of an increase in drought intensity.

Interpreting life-history traits, seasonal cycles, and coastal climate from an intertidal mussel species: Insights from 9000 years of synthesized stable isotope data.

Understanding past coastal variability is valuable for contextualizing modern changes in coastal settings, yet existing Holocene paleoceanographic records for the North American Pacific Coast commonly originate from offshore marine sediments and may not represent the dynamic coastal environment. A potential archive of eastern Pacific Coast environmental variability is the intertidal mussel species Mytilus californianus. Archaeologists have collected copious stable isotopic (δ18O and δ13C) data from M. californianus shells to study human history at California's Channel Islands. When analyzed together, these isotopic data provide windows into 9000 years of Holocene isotopic variability and M. californianus life history. Here we synthesize over 6000 δ18O and δ13C data points from 13 published studies to investigate M. californianus shell isotopic variability across ontogenetic, geographic, seasonal, and millennial scales. Our analyses show that M. californianus may grow and record environmental information more irregularly than expected due to the competing influences of calcification, ontogeny, metabolism, and habitat. Stable isotope profiles with five or more subsamples per shell recorded environmental information ranging from seasonal to millennial scales, depending on the number of shells analyzed and the resolution of isotopic subsampling. Individual shell profiles contained seasonal cycles and an accurate inferred annual temperature range of ~ 5°C, although ontogenetic growth reduction obscured seasonal signals as organisms aged. Collectively, the mussel shell record reflected millennial-scale climate variability and an overall 0.52‰ depletion in δ18Oshell from 8800 BP to the present. The archive also revealed local-scale oceanographic variability in the form of a warmer coastal mainland δ18Oshell signal (-0.32‰) compared to a cooler offshore islands δ18Oshell signal (0.33‰). While M. californianus is a promising coastal archive, we emphasize the need for high-resolution subsampling from multiple individuals to disentangle impacts of calcification, metabolism, ontogeny, and habitat and more accurately infer environmental and biological patterns recorded by an intertidal species.

Skill assessment of seasonal forecasts of ocean variables

There is growing demand for seasonal forecast products for marine applications. The availability of consistent and sufficiently long observational records of ocean variables permits the assessment of the spatial distribution of the skill of ocean variables from seasonal forecasts. Here we use state-of-the-art temporal records of sea surface temperature (SST), sea surface height (SSH) and upper 300m ocean heat content (OHC) to quantify the distribution of skill, up to 2 seasons ahead, of two operational seasonal forecasting systems contributing to the seasonal multi-model of the Copernicus Climate Change Services (C3S). This study presents the spatial distribution of the skill of the seasonal forecast ensemble mean in terms of anomaly correlation and root mean square error and compares it to the persistence and climatological benchmarks. The comparative assessment of the skill among variables sheds light on sources/limits of predictability at seasonal time scales, as well as the nature of model errors. Beyond these standard verification metrics, we also evaluate the ability of the models to represent the observed long-term trends. Results show that long-term trends contribute to the skill of seasonal forecasts. Although the forecasts capture the long-term trends in general, some regional aspects remain challenging. Part of these errors can be attributed to specific aspects of the ocean initialization, but others, such as the overestimation of the warming in the Eastern Pacific are also influenced by model error. Skill gains can be obtained by improving the trend representation in future forecasting systems. In the meantime, a forecast calibration procedure that corrects the linear trends can produce substantial skill gains. The results show that calibrated seasonal forecasts beat both the climatological and persistence benchmark almost at every location for all initial dates and lead times. Results demonstrate the value of the seasonal forecasts for marine applications and highlight the importance of representing the decadal variability and trends in ocean heat content and sea level.

Assessing the effects of dam regulation on multiscale variations in river hydrological regime and ecological responses

ABSTRACT Using a combination of the long short-term memory model and hydrological change indicators, this study proposes an assessment framework at inter-annual and intra-annual scales to quantify the hydrological regime changes and ecological event responses caused by the regulation of the Three Gorges Dam (TGD) in the upper reaches of the Yangtze River. The results indicate that during the post-TGD period (2004–2019), 2 indicators of the natural flow regime undergo a high degree of alteration at the inter-annual scale, which increase to 12 when regulated flows are considered. Furthermore, we find that while climate and incoming water change significantly reduces the annual flow and monthly flow during the flood season, it increases the complexity (79%) and ecodeficit at the seasonal scale (94%). Among the 32 indicators of hydrologic alteration, TGD is the dominant factor influencing changes in 20 indicators, increasing the magnitude of low-flow events, decreasing the frequency of high-flow pulses, and advancing the timing of 1-day minimum flow (43 Julian date). From the hydrological perspective, the altered rising water conditions due to TGD regulation may cause an average decrease of 19.5% in the fry abundance for the Four Famous Major Carps.

Intra-annual variation in oak masting affects wildlife feeding behavior

Abstract Oaks (Quercus spp.) provide an important food source for many wildlife species throughout the fall and winter. Most research evaluating oak masting patterns and the subsequent behavioral responses of wildlife focuses on the annual temporal scale. However, patterns in masting at the seasonal temporal scale may be important for wildlife behavior. We designed a study quantifying seasonal oak masting patterns of 3 oak species (water oak, Q. nigra; laurel oak, Q. laurifolia; and swamp chestnut oak, Q. michauxii) and linking those patterns to visitation and feeding behavior of 3 primary consumers (white-tailed deer, Odocoileus virginianus; gray squirrel, Sciurus carolinensis; and raccoon, Procyon lotor). We used seed traps to monitor the seasonal masting pattern of 205 trees in the fall of 2021 and 2022 and used camera traps concurrently to monitor wildlife behavior associated with a subset of 30 trees. Seasonal masting patterns differed between oak species both within a season and across years, and the timing of mast varied within oak species across years. White-tailed deer tended to visit swamp chestnut oak as the number of acorns increased and consumed their acorns. Gray squirrels and raccoons tended to visit laurel oak and consume water oak acorns with gray squirrels being more likely to consume as the number of acorns increased. Our results indicate that evaluating acorn production at multiple temporal scales may be necessary to fully understand oak masting relationships with wildlife. Furthermore, differences in wildlife behavior based on oak species may have important implications for oak regeneration.

Probabilistic Causal Network Modeling of Southern Hemisphere Jet Subseasonal to Seasonal Predictability

Abstract Skillful prediction of the Southern Hemisphere (SH) eddy-driven jet is crucial for representation of mid-to-high-latitude SH climate variability. In the austral spring-to-summer months, the jet and the stratospheric polar vortex variabilities are strongly coupled. Since the vortex is more predictable and influenced by long-lead drivers 1 month or more ahead, the stratosphere is considered a promising pathway for improving forecasts in the region on subseasonal to seasonal (S2S) time scales. However, a quantification of this predictability has been lacking, as most modeling studies address only one of the several interacting drivers at a time, while statistical analyses quantify association but not skill. This methodological gap is addressed through a knowledge-driven probabilistic causal network approach, quantified with seasonal ensemble hindcast data. The approach enables to quantify the jet’s long-range predictability arising from known late-winter drivers, namely, El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), upward wave activity flux, and polar night jet oscillation, mediated by the vortex variability in spring. Network-based predictions confirm the vortex as determinant for skillful jet predictions, both for the jet’s poleward shift in late spring and its equatorward shift in early summer. ENSO, IOD, late-winter wave activity flux, and polar night jet oscillation only provide moderate prediction skill to the vortex. This points to early spring submonthly variability as important for determining the vortex state leading up to its breakdown, creating a predictability bottleneck for the jet. The method developed here offers a new avenue to quantify the predictability provided by multiple, interacting drivers on S2S time scales. Significance Statement Predictions of the Southern Hemisphere midlatitude jet stream are crucial for skillful forecasts of the austral mid-to-high latitudes. Several oceanic and atmospheric phenomena could, if better represented in models, improve spring-to-summer jet predictions on subseasonal to seasonal time scales. However, the combined potential skill arising from the inclusion of such phenomena has not been quantified. This study does so by using a probabilistic causal network model, representing the connections between those drivers and the jet with conditional probabilities, trained on large sets of model data. The stratospheric polar vortex is confirmed as crucial predictor of jet variability but is itself hard to predict a month in advance due to submonthly variability, creating a predictability bottleneck for the jet.

Seasonal changes in ecosystem health and their spatial relationship with landscape structure in china's Loess plateau

Environmental managers have sought to optimize landscape structure to maintain regional ecosystem health (EH). However, we still lack a full understanding of the effects of changing landscape structure on EH, especially in terms of shorter time scales (e.g., monthly or seasonal scales) and spatial heterogeneity. To address this gap, this study used the vigor-organization-resilience-services (VORS) model to assess the annual and seasonal variation of EH in the Loess Plateau (LP) during 2000–2015, and then applied the geographically weighted regression (GWR) model to investigate the spatial effects of different aspects (i.e., landscape composition and configuration) of the landscape structure change on EH over time. Our results showed that the EH level in the LP from 2000 to 2015 displayed temporal non-stationarity at annual and seasonal scales, and a spatial pattern of decreasing from southeast to northwest. The effects of landscape structure on EH had obvious spatial and temporal heterogeneity, depending on the local landscape composition and configuration. In general, landscape configuration exerted a greater impact on EH than landscape composition apart from Shannon’s diversity index and percentage of forestland area, but the intensity and direction of the impact displayed higher temporally instability and therefore should receive more attention. Furthermore, regulation of the dominant patch size, landscape connectivity and forest spatial spacing is the optimal choice for regional ecosystem management. The results suggest that EH responses to changes in landscape structure in the LP vary by time and place, and that the integrating temporal variation and spatial characteristics can help decision-makers make precise spatial arrangements and temporal adjustments to keep the negative ecological impacts within reasonable limits.

Seasonal Scale Climatic Factors on Grassland Phenology in Arid and Semi-Arid Zones

Influenced by climate change, significant alterations in vegetation phenology have been observed globally. Grassland phenology is highly sensitive to climate change. However, research on the variations in grassland phenology and its responses to seasonal climatic changes in arid and semi-arid regions remains scarce. This study, utilizing Solar-Induced Chlorophyll Fluorescence (SIF) data, meteorological station data, and grassland type data, employs trend analysis and time series analysis to explore the trends of seasonal climatic variability and the sensitivity response of grassland phenology in Xinjiang to seasonal climates. The findings reveal the following: (1) The region experiences more pronounced warming in winter and spring than in summer and autumn, with ground temperature increments outpacing those of air temperatures. The summer season registers the peak in precipitation volume and rate of increase, where mountainous zones accrue more rainfall compared to basins and plains. The distribution of sunshine duration is characterized by higher values in eastern areas than in the west and more in the plains than in mountainous regions, potentially due to escalating cloudiness, which has contributed to a diminishing trend in sunshine hours across Xinjiang over the past 20 years. (2) Over the past two decades, the perennial greening phase of Xinjiang grasslands has predominantly occurred in early May, showing an overall trend of occurring earlier by approximately 5.47 days per decade, while the yellowing phase mainly occurs at the end of September and the beginning of October, demonstrating a delaying trend (6.61 days/decade). The average length of the growing season is 145 days, generally showing a slightly increasing trend (11.97 days/decade). (3) In spring, the rise in air and ground temperatures, along with increased sunshine duration, all promote grassland growth, leading to an earlier greening phase. Conversely, in autumn, increases in air temperature, ground temperature, and sunshine duration can inhibit grassland growth, resulting in an earlier yellowing phase. Increased precipitation in summer and autumn can delay the yellowing phase and extend the length of the grassland growing season. This research provides new insights into the factors influencing large-scale grassland phenology and offers references for grassland adaptation to future climate changes.

Seasonal thermo-enviro-economic performance analyses of concentrated PV/T integrated with humidification-dehumidification water desalination system

Concentrated solar photovoltaic thermal offers a great opportunity for water desalination by applying some thermally driven techniques. Humidification–dehumidification water desalination is appropriate for moderate installations and runs on low temperatures matching the attainable output of low concentrated photovoltaic thermal systems. So, in this paper, thermo-enviro-economic performance analyses of an integrated system consisting of compound parabolic collectors partially covered with photovoltaic cells powering humidification dehumidification water desalination system is theoretically introduced. The system performance is conducted at a seasonal scale using MATLAB throughout the whole year under the weather of New Borg El-Arab city-Alexandria-Egypt. The results indicate that raising the flow rate of the working fluid of the system increases the desalination output, reduces the PV cell temperature, and enhances PV efficiency. At a fluid flow rate of 0.02 kg/s suitable for PV safety temperature, the cell maximum and minimum temperature are 79 and 42.5 °C, maximum and output power is 6 and 3.8 kW, and maximum and minimum efficiency is 17.2 and 14.2 % respectively. The output water temperature is 73 and 36.9 °C in summer and winter, respectively. The desalination system produces maximally 4.7, 6.2, and 3.8 L/h at water to air mass flow rate ratios of 1.5, 2, and 3, respectively. The maximum achieved average monthly desalination system gain output ratio is 3.15 with an overall system efficiency of 41 %. The average annual system thermal energy and exergy yield is approximately 176 and 0.2 kWh, respectively. The annual freshwater cost is $0.0351/Liter. The system mitigates an annual average of 7.2 tons of CO2 emissions.

Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec

Abstract. Air pollution is a major health hazard, and while air quality overall has been improving in industrialized nations, pollution is still a major economic and public health issue, with some species, such as ozone (O3), still exceeding the standards set by governing agencies. Chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution both at local and regional scales. In this study, the Polair3D v1.11 CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model's capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales. The simulation by the model included three nested domains, at horizontal resolutions of 9 km by 9 km and 3 km by 3 km, as well as two 1 km by 1 km domains covering the cities of Montréal and Québec. We find that the model captures the spatial variability and seasonal effects and, to a lesser extent, the hour-by-hour or day-to-day temporal variability for a fixed location. The model at both the 3 km and the 1 km resolution struggled to capture high-frequency temporal variability and showed large variabilities in correlation and bias from site to site. When comparing the biases and correlation at a site-wide scale, the 3 km domain showed slightly higher correlation for carbon monoxide (CO), nitrogen dioxide (NO2), and nitric oxide (NO), while ozone (O3), sulfur dioxide (SO2), and PM2.5 showed slight increases in correlation at the 1 km domain. The performance of the Polair3D model was in line with other models over Canada and comparable to Polair3D's performance over Europe.