Global Air Temperature Research Articles

The Scientific Case Against Net Zero: Falsifying the Greenhouse Gas Hypothesis

The UK Net Zero by 2050 Policy was undemocratically adopted by the UK government in 2019. Yet the science of so-called ‘greenhouse gases’ is well known and there is no reason to reduce emissions of carbon dioxide (CO2), methane (CH4), or nitrous oxide (N2O) because absorption of radiation is logarithmic. Adding to or removing these naturally occurring gases from the atmosphere will make little difference to the temperature or the climate. Water vapor (H2O) is claimed to be a much stronger ‘greenhouse gas’ than CO2, CH4 or N2O but cannot be regulated because it occurs naturally in vast quantities. This work explores the established science and recent developments in scientific knowledge around Net Zero with a view to making a rational recommendation for policy makers. There is little scientific evidence to support the case for Net Zero and that greenhouse gases are unlikely to contribute to a ‘climate emergency’ at current or any likely future higher concentrations. There is a case against the adoption of Net Zero given the enormous costs associated with implementing the policy, and the fact it is unlikely to achieve reductions in average near surface global air temperature, regardless of whether Net Zero is fully implemented and adopted worldwide. Therefore, Net Zero does not pass the cost-benefit test. The recommended policy is to abandon Net Zero and do nothing about so-called ‘greenhouse gases’.

Changing windstorm characteristics over the US Northeast in a single model large ensemble

Abstract Extreme windstorms pose a significant hazard to infrastructure and public safety, particularly in the highly populated US Northeast (NE). However, the influence climate change and changing land use will have on these events remains unclear. A large ensemble generated using the Max-Planck Institute (MPI) Earth system model is used to generate projections of NE windstorms under different shared socioeconomic pathways (SSPs) and to attribute changes to projected land use land cover (LULC) change, externally forced changes and internal climate variability. To reduce the influence of coarse grid cell resolution and uncertainties in surface roughness lengths, windstorms are identified using simultaneous widespread exceedance of local 99th percentile 10 m wind speeds (U99). Projected declines in forest cover in the NE and the resulting reductions in surface roughness length under SSP3-7.0 lead to projections of large increases in U99 and derived windstorm intensity and scale. However, these projected changes in regional LULC under SSP3-7.0 are unprecedented in a historical context and may not be realistic. After corrections are applied to remove the influence of LULC on wind speeds, regionally averaged U99 exhibit declines for most of the single model initial-condition large ensemble (SMILE) members which are broadly proportional to the radiative forcing and global air temperature increase in the SSPs, with a median value of −0.15 ms−1 °C−1. While weak cyclones are projected to decline in frequency in the NE, intense cyclones and the resulting windstorms and indices of socioeconomic loss do not. Where present, significant trends in these loss indices are positive, and some MPI SMILE members generate future windstorms that are unprecedented in the historical period.

Unravelling spatiotemporal patterns in global sea surface temperatures with dynamic mode decomposition

AbstractExtracting global sea surface temperature (SST) patterns is essential for understanding climate variability in both regional and global perspective. Traditional methods, limited by assumptions of orthogonality, hinder our ability to fully capture the dynamics of global SST, particularly in the complex Pacific basin. This study introduces dynamic mode decomposition (DMD), a powerful method from fluid dynamics, to extract global SST patterns since 1900. Our analysis shows that DMD successfully captures well‐known patterns like global warming and the Atlantic Multidecadal Oscillation (AMO). More importantly, it reveals three Pacific SST modes that are spatially non‐orthogonal but exhibit distinct characteristics. Spectral analysis shows that these Pacific modes have distinct periodicities: Pacific Multi‐decadal Mode (PMDM, ~50 years), Quasi‐decadal Mode (PQDM, 8–16 years) and Interannual Mode (PIAM, 2–8 years). This highlights DMD's strength in not only capturing known patterns but also separating modes with similar spatial features but unique dynamics. Further analysis reveals significant differences in their impacts on global air temperature. During positive phases, all three modes induce tropical warming, with PIAM exerting the strongest influence and PMDM the weakest. In the Northern Hemisphere, both PQDM and PIAM drive mid‐latitude cooling and high‐latitude warming, with a more pronounced effect from PQDM. PMDM, in contrast, triggers widespread cooling across the Northern extratropics. In the Southern Hemisphere, PMDM's influence on temperature exhibits a distinct latitudinal banding pattern. PIAM and PQDM, on the other hand, exhibit a regionalized impact, primarily concentrated in the Amundsen and Ross Seas, with PQDM's influence extending to the Antarctic continent.

Multiple serial correlations in global air temperature anomaly time series.

Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth's climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.

Quality vs. Quantity: The Consequences of Elevated CO2 on Wood Biomaterial Properties.

Since the late 1800s, anthropogenic activities such as fossil fuel consumption and deforestation have driven up the concentration of atmospheric CO2 around the globe by >45%. Such heightened concentrations of carbon dioxide in the atmosphere are a leading contributor to global climate change, with estimates of a 2-5° increase in global air temperature by the end of the century. While such climatic changes are mostly considered detrimental, a great deal of experimental work has shown that increased atmospheric CO2 will actually increase growth in various plants, which may lead to increased biomass for potential harvesting or CO2 sequestration. However, it is not clear whether this increase in growth or biomass will be beneficial to the plants, as such increases may lead to weaker plant materials. In this review, I examine our current understanding of how elevated atmospheric CO2 caused by anthropogenic effects may influence plant material properties, focusing on potential effects on wood. For the first part of the review, I explore how aspects of wood anatomy and structure influence resistance to bending and breakage. This information is then used to review how changes in CO2 levels may later these aspects of wood anatomy and structure in ways that have mechanical consequences. The major pattern that emerges is that the consequences of elevated CO2 on wood properties are highly dependent on species and environment, with different tree species showing contradictory responses to atmospheric changes. In the end, I describe a couple avenues for future research into better understanding the influence of atmospheric CO2 levels on plant biomaterial mechanics.

Assessment of the Potential for Increasing the Energy Efficiency in the Cooling Sector

Cooling supply to ensure the indoor microclimate is becoming more important as global average air temperatures rise. With growing societal demands for higher thermal comfort and increasing individual cooling solutions in the building sector, global energy consumption for cooling is increasing accordingly. Scientific literature and studies estimate that the demand for cooling energy will have a significant impact on global energy demand in the future. In compliance with the European Union goal of achieving climate neutrality by 2050, it is essential to find solutions for reducing energy consumption in the building sector, which is already among the largest energy consumers and greenhouse gas emission producers. Replacing individual cooling solutions with district cooling in urban areas where higher energy density can be reached is one of the solutions for decarbonizing the building sector. To spatially assess the feasibility of district cooling in certain areas, energy demand mapping can be performed. Within this research mapping is carried out using a geographical information system (GIS) tool. The purpose of the mapping is to identify the places in the city of Riga with the highest district cooling potential. Spatial assessment using a GIS tool can be done in different ways – mostly it depends on the available data. The spatial data (buildings) of the cadastral information system and additional detailed information about buildings were obtained from the Latvian open data portal. Information on the type of use of the building, indoor area and the age of building was attached to each building on the map. Then building energy certificate data containing information on specific energy consumption for cooling (kWh/m2 per year) was obtained from the same portal. By processing the data of energy certificates of buildings, excluding outliers, a specific index was obtained for different types of buildings. For the residential sector, the age of the building is also used. Using cadastral data on the indoor area of buildings and the type of building use, the theoretical cooling demand in Riga is calculated and results are quantified and displayed visually. By visualizing the results with the GIS tool, hot spots with the highest cooling energy demand were detected. Results can be further used to calculate the technical and economic justification for the district cooling solutions in specific areas as well as assess the energy efficiency that would be provided by implementation of district cooling solutions.

Exploring the Multi-Sensory Coupling Relationship of Open Space on a Winter Campus

Exploring the combined effects of multisensory interactions in open spaces can help improve the comfort of campus environments. Nine typical spaces on a university campus in Fuzhou were selected for this study. Subjects perceived the environment and then completed an on-site subjective questionnaire. At the same time, meteorological data (global radiation, air temperature, globe temperature, wind speed, relative humidity, and illumination intensity) were measured to determine the interactions between visual and acoustic and thermal perceptions. Differences in the meteorological parameters between the measuring points were described using a one-way ANOVA and Tukey’s post hoc test, and a chi-square test of independence was used to determine significant associations between thermal, acoustic, and visual comfort, which in turn led to the study of interactions between visual, acoustic, and thermal comfort using a two-way ANOVA. The following conclusions were drawn: (1) the Thermal Comfort Vote (TCV) increased with the increasing Acoustic Comfort Vote (ACV) at all levels of thermal stress. (2) The highest and lowest Acoustic Sensation Vote (ASV) values for each sound type were derived from either “slightly cold” or “warm” conditions. Both the Thermal Comfort Vote (TCV) and the Acoustic Comfort Vote (ACV) were positively correlated. (3) When “neutral”, the Thermal Sensation Vote (TSV) increased with increasing illumination intensity (LUX). (4) The Sunlight Sensation Vote (SSV) increased with the increasing Universal Thermal Climate Index (UTCI) when illumination intensity (LUX) was moderate and bright. (5) The highest and lowest Acoustic Sensation Vote (ASV) values for each sound type came from either “slightly cold” or “warm” conditions.

Hydroclimatic anomalies detected by a sub-decadal diatom oxygen isotope record of the last 220 years from Lake Khamra, Siberia

Abstract. Northern latitudes have been significantly impacted by recent climate warming, which has increased the probability of experiencing extreme weather events. To comprehensively understand hydroclimate change and reconstruct hydroclimatic anomalies such as drought periods, appropriate proxy records reaching further back in time beyond meteorological measurements are needed. Here we present a 220-year (2015–1790 CE), continuous, stable oxygen isotope record of diatoms (δ18Odiatom) from Lake Khamra (59.99° N, 112.98° E) in eastern Siberia, an area highly sensitive to climate change and for which there is a demand for palaeohydrological data. This high-resolution proxy record was obtained from a 210Pb–137Cs-dated sediment short core and analysed to reconstruct hydroclimate variability at a sub-decadal scale. The interpretation of the δ18Odiatom record is supported by meteorological data, modern isotope hydrology and geochemical analyses of the same sediment, which is indicative of the conditions in the lake and catchment. A comparison with meteorological data going back to 1930 CE revealed that the δ18Odiatom record of Lake Khamra is primarily influenced by regional precipitation changes rather than the air temperature. We identified winter precipitation, which enters the lake as isotopically depleted snowmelt water, as the key process impacting the diatom isotope variability. We related the overall depletion of δ18Odiatom in recent decades to an observed increase in winter precipitation in the area, likely associated with the global air temperature rise, Arctic sea ice retreat and increased moisture transport inland. Available palaeoclimate proxy records, including a fire reconstruction for the same lake, support the idea that the new record is a valuable hydroclimate proxy that is indicative of precipitation deficits and excludes solar insolation and air temperature as primary driving forces, even before the first meteorological recordings. We propose two possible hydroclimatic anomalies that were detected in the Lake Khamra δ18Odiatom record: one at the beginning of the 19th century and a second prominent event in the 1950s. Both are interpreted as prolonged dry periods associated with enriched δ18Odiatom values likely caused by reduced winter precipitation, which coincide with phases of reconstructed severe wildfires in the region. Despite the apparent pristine lake area, we observed a three- to fourfold increase in mercury concentrations and accumulation rates within the sediment record since the early 20th century, which is partly attributed to human air pollution.

Representing natural climate variability in an event attribution context: Indo-Pakistani heatwave of 2022

Attribution of extreme climate events to global climate change as a result of anthropogenic greenhouse gas emissions has become increasingly important. Extreme climate events arise at the intersection of natural climate variability and a forced response of the Earth system to anthropogenic greenhouse gas emissions, which may alter the frequency and severity of such events. Accounting for the effects of both natural climate variability and the forced response to anthropogenic climate change is thus central for the attribution. Here, we investigate the reproducibility of probabilistic extreme event attribution results under more explicit representations of natural climate variability. We employ well-established methodologies deployed in statistical Earth System Model emulators to represent natural climate variability as informed from its spatio-temporal covariance structures. Two approaches towards representing natural climate variability are investigated: (1) where natural climate variability is treated as a single component; and (2) where natural climate variability is disentangled into its annual and seasonal components. We showcase our approaches by attributing the 2022 Indo-Pakistani heatwave to human-induced climate change. We find that explicit representation of annual and seasonal natural climate variability increases the overall uncertainty in attribution results considerably compared to established approaches such as the World Weather Attribution Initiative. The increase in likelihood of such an event occurring as a result of global warming differs slightly between the approaches, mainly due to different assessments of the pre-industrial return periods. Our approach that explicitly resolves annual and seasonal natural climate variability indicates a median increase in likelihood by a factor of 41 (95% range: 6-603). We find a robust signal of increased likelihood and intensification of the event with increasing global warming levels across all approaches. Compared to its present likelihood, under 1.5 °C (2 °C) of global near-surface air temperature increase relative to pre-industrial temperatures, the likelihood of the event would be between 2.2 to 2.5 times (8 to 9 times) higher. We note that regardless of the different statistical approaches to represent natural variability, the outcomes on the conducted event attribution are similar, with minor differences mainly in the uncertainty ranges. Possible reasons for differences are evaluated, including limitations of the proposed approach for this type of application, as well as the specific aspects in which it can provide complementary information to established approaches.

Dual carbon and oxygen isotopes in Siberian tree rings as indicator of millennia sunshine duration changes

The current lack of information on past summer sunshine duration variability from annually resolved palaeoclimatological archives is hindering progress in the understanding and modelling of the earth climate system. We show that a combination of tree-ring carbon and oxygen isotopes from Siberia provides robust information on summer sunshine duration, which we use for an annual 1505-year reconstruction of July sunshine duration variability (1,5K-SIB-JSDR). We found that the Medieval maximum is 56 % higher than the average over 1505 years. Rapid and drastic decreases in sunshine duration up to 60 % correspond to major stratospheric volcanic eruptions. Grand Solar Minima and total sunspot numbers are also well preserved in the 1,5K-SIB-JSDR. Coherency with a global air temperature composite and spring Arctic Oscillation indicate that a large-scale climate signal is retained in our sunshine reconstruction.

Community-scale changes to landfast ice along the coast of Alaska over 2000-2022

Landfast sea ice that forms along the Arctic coastline is of great importance to coastal Alaskan communities. It provides a stable platform for transportation and traditional activities, protects the coastline from erosion, and serves as a critical habitat for marine mammals. Here we present a full assessment of landfast ice conditions across a continuous 7885 km length of the Alaska coastline over 2000–2022 using satellite imagery. We find that the maximum landfast ice extent, usually occurring in March, averaged 67 002 km2 during our study period: equivalent to 4% of the state’s land area. The maximum extent of landfast ice, however, exhibits considerable interannual variability, from a minimum of 29 871 km2 in 2019 to a maximum of 87 571 km2 in 2010. Likewise, the landfast ice edge position averages 22.9 km from the coastline but, at the community-scale, can range from 2.8 km (in Gambell) to 71.1 km (in Deering). Landfast ice breakup date averages 2 June but also varies considerably both between communities (3 May in Quinhagak to 24 July in Nuiqsut) and interannually. We identify a strong control of air temperature on breakup timing and use this relationship to project future losses of ice associated with Paris Climate Agreement targets. Under 2 °C of global air temperature warming, we estimate the average Alaskan coastal community will lose 19 days of ice, with the northernmost communities projected to lose 50 days or more. Overall, our results emphasize the highly localized nature of landfast ice processes and the vulnerability of coastal Arctic communities in a warming climate.

Impact of meteorological variability on diurnal and seasonal net ecosystem productivity in a desert riparian forest ecosystem.

The desert riparian forests are susceptible to meteorological changes and contribute significantly to the net ecosystem productivity (NEP) variations of arid ecosystems. However, the responsive patterns of their NEP variations to the meteorological variabilities remain inadequately comprehended. To address this gap, we utilized seven years of eddy covariance flux measurements in a representative desert riparian forest to investigate the NEP variations and its response to changing meteorological factors across diverse temporal scales. The results revealed significant periodic variations in half-hourly NEP, with dominant cycles spanning from five hours to one year, with a principal oscillation period of one day. Key meteorological factors including global solar radiation (Rg), relative humidity (RH), air temperature (Ta), soil temperature (Ts), and vapor pressure deficit (VPD) exhibited synchronization with NEP on daily scales. This synchronization, coupled with the observed one-day periodic NEP variations, provides robust evidence supporting the existence of a circadian rhythm in the ecosystem carbon exchange of desert riparian forest regulated by meteorological conditions. Seasonal patterns were significant in the impact of Rg phase, Ta diurnal amplitude, and VPD diurnal amplitude on NEP diurnal amplitude and phase. The NEP diurnal amplitude significantly, directly, and positively affected daily NEP in both the dormant and growing seasons, whereas its phase yielded significant negative effects (P< 0.05). The averages, amplitudes, and phases of diurnal meteorological conditions controlled the daily NEP by regulating NEP diurnal amplitude and phase. These findings provide evidence that the variability in circadian rhythms, caused by the increase in diurnal Ta and VPD, significantly impact the daily NEP at an ecosystem scale. This study enriches our comprehension of the meteorological mechanisms governing diurnal and seasonal carbon uptake dynamics within desert riparian forests, providing fresh insights into the direct and indirect roles of climate change in shaping patterns of ecosystem carbon exchange.

Carbon Footprint Reduction and Climate Change Mitigation: A Review of the Approaches, Technologies, and Implementation Challenges

Since the Industrial Revolution, human economic activity and the global development of society in general have been heavily dependent on the exploitation of natural resources. The use of fossil fuels, deforestation, the drainage of wetlands, the transformation of coastal marine ecosystems, unsustainable land use, and many other unbalanced processes of human activity have led to an increase both in the anthropogenic emissions of climate-active gases and in their concentration in the atmosphere. It is believed that over the past ~150 years these phenomena have contributed to an increase in the global average temperature in the near-surface layer of the atmosphere by ~1 °C. Currently, the most pressing tasks facing states and scientific and civil societies are to reduce anthropogenic CO2 emissions and to limit the global air temperature increase. In this regard, there is an urgent need to change existing production systems in order to reduce greenhouse gas emissions and to sequester them. In this review, we consider up-to-date scientific approaches and innovative technologies, which may help in developing roadmaps to reduce the emissions of climate-active gases, control rising temperatures, decarbonize economies, and promote the sustainable development of society in general.

Discrepant trends in global land-surface and air temperatures controlled by vegetation biophysical feedbacks

Satellite-based land surface temperature (Ts) with continuous global coverage is increasingly used as a complementary measure for air temperature (Ta), yet whether they observe similar temporal trends remains unknown. Here, we systematically analyzed the trend of the difference between satellite-based Ts and station-based Ta (Ts–Ta) over 2003–2022. We found the global land warming rate inffered from Ts was on average 42.6% slower than that from Ta (Ts–Ta trend: −0.011 °C yr−1, p = 0.06) during daytime of summer. This slower Ts-based warming was attributed to recent Earth greening, which effectively cooled canopy surface through enhancing evapotranspiration and turbulent heat transfer. However, Ts showed faster warming than Ta during summer nighttime (0.015 °C yr−1, p < 0.01), winter daytime (0.0069 °C yr−1, p = 0.08) and winter nighttime (0.0042 °C yr−1, p = 0.16), when vegetation activity is limited by temperature and solar radiation. Our results indicate potential biases in assessments of atmospheric warming and the vegetation-air temperature feedbacks using satellite-observed surface temperature proxies.

Глобальное изменение климата и лесные экосистемы

The article examines the relationship between the process of global climate change and the forest ecosystem, analyzes forecasts for an increase in the average global air temperature and the intensification of deforestation processes. Keywords: FOREST ECOSYSTEMS, GLOBAL CLIMATE CHANGE, GREENHOUSE GASES, DEFORESTATION, AVERAGE ANNUAL TEMPERATURE

EPNN-based prediction of meteorological data for renewable energy systems

In this paper, an application of an Evolving Polynomial Neural Network (EPNN) for prediction of meteorological time series (global solar irradiation, air temperature, relative humidity, and wind speed) is described. Prediction of such data plays a very important role in design of the renewable energy systems. The problem of time series prediction is formulated as a system identification problem, where the input of the system is the past values (y (t - 1), y (t - 2), y (t - 3), …) of a time series and its desired output (y (t), y (t + 1), y (t + 2), …) are the future of a time series. In this study, a dataset of meteorological time series for five years collected in Algiers (Algeria) by the National Office of Meteorology has been used. The obtained results showed a good agreement between both series, measured and predicted. The correlation coefficient ( r ) is arranged between 0.9821 and 0.9923, the mean relative error over the whole data set is not exceed 15.4 %. The proposed model provides more accurate results than other ANN’s architecture, wavenet (wavelet-network) and Adaptive Neuro-Fuzzy Inference Scheme (ANFIS). In order to show the effectiveness of the proposed predictor, the predicted data have been used for sizing, and prediction of the output energy of photovoltaic systems.

Temporal variations of carbon and water fluxes in a subtropical mangrove forest: Insights from a decade-long eddy covariance measurement

Mangroves, highly efficient ecosystems in sequestering CO2, are strongly impacted by climate change. The lack of long-term observation in mangroves hinders the evaluation of seasonal and inter-annual variability in carbon and water fluxes and their responses to various environmental drivers. In this study, we measured net ecosystem CO2 exchange and evapotranspiration between the atmosphere and subtropical mangroves using the eddy covariance technique over a decade (2010–2019) in southern China. This mangrove forest acted as a strong CO2 sink, with annual net ecosystem production (NEP) ranging from 622.5 to 832.8 g C m−2 year−1. The annual evapotranspiration (ET) varied between 934.6 and 1004.9 mm year−1. During the study period, ET consistently remained higher in the wet season (May to October) compared to the dry season, while NEP did not exhibit consistent seasonal variation. Path analysis indicated that during the dry season, NEP was primarily influenced by global solar radiation and vapor pressure deficit. However, in the wet season, NEP was regulated by a combination of global solar radiation, vapor pressure deficit, air temperature, and tidal inundation time. Additionally, the promoting effect of global solar radiation on NEP decreased in the wet season, while the inhibitory influences of higher temperature and vapor pressure deficit on NEP intensified during the period. Unlike NEP, the dominant factors affecting ET (global solar radiation, air temperature, and vapor pressure deficit) and their intensities remained relatively consistent during both seasons. Furthermore, the relative importance of global solar radiation on NEP and ET increased over the decade, while the influence of tidal inundation time diminished. This study not only improves the understanding of the response of subtropical mangroves to climate change but also provides a valuable benchmark dataset to validate the interannual variability of mangrove carbon and water fluxes estimated from the models.

Modeling the Distribution of Atlantic Croaker and Spot in a Dynamic Seascape Using Metabolic Scope

In estuarine and coastal systems, anthropogenic activities and directional changes in global air temperatures have led to increased water temperatures, as well as increased frequency and severity of episodic hypoxia. These alterations have had population-level effects on aquatic organisms, including changes in species-specific distributions. Because physiology is the transfer function connecting environmental conditions to individual behaviors and eventually to population-level effects, we used individual-based models (IBMs) that incorporate changes in metabolic scope as motivation for movement. Our objective was to investigate the effects of temperature and hypoxia on the distribution of Atlantic croaker and spot in Chesapeake Bay. We compared the predicted monthly fish distributions from the IBMs with apparent fish distributions recorded by the VIMS Juvenile Fish Trawl Survey during 1988–2014. IBMs failed to reproduce accurately the apparent spatial distributions of Atlantic croaker and spot. More specifically, compared with the trawl survey, IBMs predicted larger proportions of these two species would be captured in the lower regions of Chesapeake Bay and smaller proportions in the York and Rappahannock rivers. We postulate that, because similar thermal and oxygen conditions were occupied by fish in the IBMs and in the wild, temperature may not be the most important factor motivating the movement of Atlantic croaker and spot in Chesapeake Bay, and other spatial factors (e.g., prey availability) act in concert with temperature and hypoxia to determine the spatial distributions of Atlantic croaker and spot in Chesapeake Bay. Alternatively, surveys used to estimate species occurrences could be biased if gear vulnerability is affected by temperature or oxygen conditions (or both). Additional research is needed to clarify which condition is true or if a combination of these factors (and others) led to discrepancies between predicted and apparent fish distributions based on trawl survey data.

High post-flowering phosphorus status promotes the tolerance of soybean to terminal heat stress

Global air temperature is rising and resulting in higher frequency and intensity of extreme climatic events. This scenario threatens food security, requiring measures to mitigate the harmful effects of heat stress on agricultural production. Therefore, our study focused on understanding how phosphorus (P) availability during the reproductive period affects seed weight responses of soybean to terminal heat stress. The experiment was carried out in a complete 3 × 2 factorial design, with varying post-flowering P supply (severe deficiency: 20 μmol P L−1; mild deficiency: 100 μmol P L−1; sufficiency: 500 μmol P L−1) and thermal regimes imposed during the seed filling phase (control: 28.0/18.0 °C; heat stress: 42.0/28.5 °C). Our results revealed that seed weight and yield were unaffected by heat stress in the plants grown under P sufficiency, whereas a detrimental effect of warming on these traits was found in the plants facing post-flowering P deficiency. Sufficient post-flowering P supply alleviated also the harmful effects of heat stress on the integrity of the photosynthetic apparatus, which contributed to improve biomass partitioning to the harvestable organs by extending seed filling duration. Moreover, heat stress disturbed sink metabolism and caused reduction in the concentration of oil and starch solely in the seeds of the plants exposed to P limitation. In conclusion, maintaining adequate post-flowering P supply proves critical in enhancing soybean’s tolerance to terminal heat stress by supporting essential processes involved in effective seed filling. Consequently, implementing precise P nutritional management during the reproductive period of soybean might be a promising approach to improve crop yield stability in challenging environmental conditions.

Utilization Potential of Tree-of-Heaven Species Biomass—A Review

Climate change and the subsequent increase in global mean air temperature already present a significant impact on forest vegetation. Especially in the near future, several forest species are expected to be in danger of extinction or compelled to migrate to colder regions. Some common species will be replaced by highly climate-tolerant species, primarily exotic plants, among others. The tree of Ailanthus, or “tree-of-heaven”, as it is widely known, constitutes a rapidly growing plant, considered to be native to parts of China, while since the middle of the 18th century, it gradually spread to Europe and North America. This species demonstrates a preference for warmer, drier environments, although it can also survive in a variety of habitats and endure pollution of urban areas. It is a species with several uses, such as for animal feed, fuel, timber, pharmaceutical applications, etc., while its suitability for specialized applications of high-added-value is constantly being investigated. Its wood has a desirable appearance and characteristics that are comparable to those of other hardwood species of similar densities/weight. This article discusses some of the most important characteristics of Ailanthus wood and presents a comprehensive and constructive review of the chemistry, pharmacology, traditional and innovative uses, quality control, biological resistance, potential utilization in bioenergy and biofuels and wood products (e.g., wood-based panels, other advanced structure materials, etc.), use challenges and limitations, in order to contribute to the utilization potential assessment of this species biomass.