Global Surface Temperature Research Articles

Large-ensemble simulations of the North American and Greenland ice sheets at the Last Glacial Maximum with a coupled atmospheric general circulation–ice sheet model

Abstract. The Last Glacial Maximum (LGM) was characterised by huge ice sheets covering the Northern Hemisphere, especially over North America, and by its cold climate. Previous authors have performed numerical simulations of the LGM to better understand coupled climate–ice sheet systems. However, the results of such simulations are sensitive to many model parameters. Here, we perform a 200-member ensemble of simulations of the North American and Greenland ice sheets and climate of the LGM with a coupled ice sheet–atmosphere–slab ocean model (FAMOUS-BISICLES) to explore sensitivities of the coupled climate–ice system to 16 uncertain parameters. In the ensemble of simulations, the global mean surface temperature is primarily controlled by the combination of parameters in the large-scale condensation scheme and the cumulus convection scheme. In simulations with plausible LGM global mean surface temperatures, we find that the albedo parameters have only a small impact on the Greenland ice volume due to the limited area of surface ablation associated with the cold climate. Instead, the basal sliding law controls the ice volume by affecting ice transport from the interior to the margin. On the other hand, like the Greenland ice sheet in future climate change, the LGM North American ice sheet volume is controlled by parameters in the snow and ice albedo scheme. Few of our simulations produce an extensive North American ice sheet when the global temperature is above 12 °C. Based on constraints on the LGM global mean surface temperature, the ice volume and the southern extent of the North American ice sheet, we select 16 acceptable simulations. These simulations lack the southern extent of ice compared to reconstructions, but they show reasonable performance on the ice sheet configuration and ice streams facing Baffin Bay and the Arctic Ocean. The strong sensitivities of the North American ice sheet to albedo at the LGM may imply a potential constraint on the future Greenland ice sheet by constraining the albedo schemes.

Mechanical Properties and Durability of Composite Cement Pastes Containing Phase-Change Materials and Nanosilica.

Escalating global surface temperatures are highlighting the urgent need for energy-saving solutions. Phase-change materials (PCMs) have emerged as a promising avenue for enhancing thermal comfort in the construction sector. This study assessed the impact of incorporating PCMs ranging from 1% to 10% by mass into composite Portland cement partially replaced by fly ash (FA) and nanosilica particles (NS). Mechanical and electrochemical techniques were utilized to evaluate composite cements. The results indicate that the presence of PCMs delayed cement hydration, acting as a filler without chemically interacting within the composite. The combination of FA and PCMs reduced compressive strength at early ages, while thermal conductivity decreased after 90 days due to the melting point and the latent heat of PCMs. Samples with FA and NS showed a significant reduction in the CO2 penetration, attributed to their pozzolanic and microfiller effects, as well as reduced water absorption due to the non-absorptive nature of PCMs. Nitrogen physisorption confirmed structural changes in the cement matrix. Additionally, electrical resistivity and thermal behavior assessments revealed that PCM-containing samples could reduce temperatures by an average of 4 °C. This suggested that PCMs could be a viable alternative for materials with thermal insulation capacity, thereby contributing to energy efficiency in the construction sector.

Detecting climate milestones on the path to climate stabilization

The era of anthropogenic climate change can be described by defined climate milestones. These milestones mark changes in the historic trajectory of change, and include peak greenhouse gas emissions, peak greenhouse gas concentration, deceleration of warming, net-zero emissions, and a transition to global cooling. However, given internal variability in the Earth system and measurement uncertainty, definitively saying that a milestone has passed requires rigour. Here CMIP6 simulations of peak-and-decline scenarios are used to examine the time needed to robustly detect three climate milestones: (1) the slowdown of global warming; (2) the end of global surface temperature increase; and (3) peak concentration of CO2. It is estimated that it will take 40 to 60 years after a simulated slowdown in warming rate, to robustly detect ( >95% change) the signal in the global average temperature record. Detecting when warming has stopped will also be difficult and it takes until the mid 22nd century to have enough data to conclude warming has stopped. Detecting that CO2 concentration has peaked is far easier and a drop in CO2 concentration of 3 ppm is consistent with a greater than 99% chance that CO2 has peaked in all scenarios examined. Thus it is likely that as the rate of CO2 emissions is reduced, and net-zero emissions is approached, interpreting the global temperature record will become difficult—with a high potential to create confusion amongst policy makers and the general public.

Neural Networks to Find the Optimal Forcing for Offsetting the Anthropogenic Climate Change Effects

Abstract Of great relevance to climate engineering is the systematic relationship between the radiative forcing to the climate system and the response of the system, a relationship often represented by the linear response function (LRF) of the system. However, estimating the LRF often becomes an ill-posed inverse problem due to high-dimensionality and nonunique relationships between the forcing and response. Recent advances in machine learning make it possible to address the ill-posed inverse problem through regularization and sparse system fitting. Here, we develop a convolutional neural network (CNN) for regularized inversion. The CNN is trained using the surface temperature responses from a set of Green’s function perturbation experiments as imagery input data together with data sample densification. The resulting CNN model can infer the forcing pattern responsible for the temperature response from out-of-sample forcing scenarios. This promising proof of concept suggests a possible strategy for estimating the optimal forcing to negate certain undesirable effects of climate change. The limited success of this effort underscores the challenges of solving an inverse problem for a climate system with inherent nonlinearity. Significance Statement Predicting the climate response for a given climate forcing is a direct problem, while inferring the forcing for a given desired climate response is often an inverse, ill-posed, problem, posing a new challenge to the climate community. This study makes the first attempt to infer the radiative forcing for a given target pattern of global surface temperature response using a deep learning approach. The resulting deeply trained convolutional neural network inversion model shows promise in capturing the forcing pattern corresponding to a given surface temperature response, with a significant implication on the design of an optimal solar radiation management strategy for curbing global warming. This study also highlights the technical challenges that future research should prioritize in seeking feasible solutions to the inverse climate problem.

Ethical And Moral Dimension of Climate Change; A Path to Climate Justice Case Study: Susan Bay’s Community, Freetown

2023 has been recorded as the hottest year ever. Sierra Leone is ranked among the 10% of countries that are prone and most vulnerable to climate impacts globally and Susan Bay is bearing the brunt of climate change impacts, including rising sea levels and flooding. Biodiversity is being impacted by human unethical behavior, wrong perceptions, and unhealthy activities. This has resulted to global surface temperatures reached 1.1°C. However, to address the situation UNESCO and COMEST proposed guidelines for Governments in their mitigation and adaptation strategies an ethical tool to achieve climate justice. An integrative review was employed to examine existing literatures. Also, key words were used to search popular databases, including Web of Science, Google Scholar, and PubMed, from the years 2010 to 2023 with a total of 53 articles. We argued that the most affected population should receive financial compensation from G20 countries responsible for majority of the Green House Gas Emissions. Therefore, the paper proposed for the Government of Sierra Leone to ensure policy regulation and enforcement, community development and empowerment, and community protection and resilience in their actions, missions, and visions from our analytical framework. The paper recommends for the GoSL to integrate ethical and moral values into climate mitigation and adaptation strategies, in line with UNESCO and COMEST recommendations with emphasis on: Prevention of harm; scientific integrity; and justice and equity. Inadequate financial resources and technical expertise hinders the GoSL in implementing these recommendations. To address these challenges, the study suggests that G20 nations should provide climate finance and scientific support to help the GoSL efficiently develop and execute its climate mitigation and adaptation strategies a path to climate justice.

INVESTIGATION OF CYCLIC PROPERTIES OF SOLAR AND GEOMAGNETIC ACTIVITIES: IMPLICATION FOR GLOBAL SURFACE TEMPERATURE VARIABILITY

The cyclic properties of solar-geomagnetic activity and global surface temperature have been investigated using trend, frequency and time-frequency analyses. Results reveal that on decadal-to-centennial timescales, low-frequency cycles in the half-century (~49 to 56 years per cycle) and Gleisberg (~99 to 114 years per cycle) range run in the background of the Schwabe (~9 to 11 years per cycle) as the dominant cycle of solar-geomagnetic activity. The only dominant cycle in the global surface temperature series is the half-century cycle and suggests a possible causal link between it and solar-geomagnetic activity phenomena. Evolution of the amplitudes of the cycles is such that the Schwabe and Gleisberg periods have increased in power from the beginning of the series until the mid-20th century after which they declined. The half-century cycle decreased in amplitude after 1800 to the present. For geomagnetic activity, the amplitude of Gleisberg cycle increased from the beginning of the series around 2000 while the amplitudes of Schwabe and half-century cycles declined within the same interval, except the Schwabe cycle which amplitude increased rapidly after 1980 to the present. For the global surface temperature, the amplitudes of Gleisberg and half-century cycles have continuously increased from the beginning of the series while Schwabe cycle oscillates with very low amplitudes until 1980 after which it increased slightly. Evolution of the amplitudes of cycles of solar-gemagnetic activity suggests a recovery from Maunder Minimum and decline into a grand epsode most likely to be a minimum.

How much methane removal is required to avoid overshooting 1.5 ∘C?

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide. With an atmospheric lifetime of around a decade, methane mitigation starting immediately has the potential to avoid substantial levels of additional warming by mid-century. In addition to the methane emissions reductions that are necessary to limit warming, we address the question of whether technological methane removal can provide additional benefits by avoiding global mean surface temperatures exceeding 1.5 ∘C above pre-industrial—the high-ambition Paris Agreement climate goal. Using an adaptive emissions methane removal routine in a simple climate model, we successfully limit peak warming to 1.5 ∘C for overshoots of up to around 0.3 ∘C. For substantially higher overshoots, methane removal alone is unable to limit warming to 1.5 ∘C, but in an extreme scenario could limit peak warming by an ensemble median 0.7 ∘C if all atmospheric methane was removed, requiring huge levels of net removal on the order of tens of petagrams cumulatively. The efficacy of methane removal depends on many emergent properties of the climate system, including climate sensitivity, aerosol forcing, and the committed warming after net zero CO2 (zero emissions commitment). To avoid overshooting 1.5 ∘C in the low-overshoot, strong-mitigation SSP1-1.9 scenario, a median cumulative methane removal of 1.2 PgCH4 is required, though this may be much higher if climate sensitivity is high or the zero emissions commitment is positive, and in these cases may require ongoing methane removal long after peak warming in order to stabilise warming below 1.5 ∘C.

Human activities and increased anthropogenic emissions: A remote sensing study in Cyprus

BackgroundThe effects of human activity on the environment are widespread and widely known. Human activities on most continents can be generalized as shelters and food chains due to the basic requirements of human life. Most of these activities require Land-use Land-cover (LULC) transformations, and their effects can be seen as changes such as increases in the global Land Surface Temperature (LST) and air pollutant concentrations. The present research aims to use remote sensing to monitor LULC transformations in Cyprus. MethodThis research uses Sentinel-3 data, Python programming, geographical information systems, and remote sensing to develop a moving average research method for a case study of Cyprus. Importantly, this work eliminates all political and ethnic boundaries to produce a unified analysis. ResultBased on the research outcomes, the highest mean LST and sulfur dioxide, nitrogen dioxide, and particulate matter (with a diameter of 10 μm or less) emissions occur in the Limassol, Famagusta, Nicosia, and Larnaca districts. These emissions are mainly attributable to artificial surfaces, agricultural areas, and forested and semi-natural areas. These trends may relate to electric power plants, a cement factory, an airport, and intensified agricultural activities in the research area.

Arctic summer sea ice loss will accelerate in coming decades

The Arctic sea ice (ASI) is expected to decrease with further global warming. However, considerable uncertainty remains regarding the temperature range that would lead to a completely ice-free Arctic. Here, we combine satellite data and a large suite of models from the latest phase of the Coupled Model Intercomparison Project (CMIP6) to develop an empirical, observation-based projection of the September ASI area for increasing global mean surface temperature (GMST) values. This projection harnesses two simple linear relationships that are statistically supported by both observations and model data. First, we show that the September ASI area is linearly proportional to the area inside a specific northern hemisphere January–September mean temperature contour T c . Second, we use observational data to show how zonally averaged temperatures have followed a positive linear trend relative to the GMST, consistent with Arctic amplification. To ensure the reliability of these observations throughout the rest of the century, we validate this trend by employing the CMIP6 ensemble. Combining these two linear relationships, we show that the September ASI area decrease will accelerate with respect to the GMST increase. Our analysis of observations and CMIP6 model data suggests a complete loss of the September ASI (area below 10 6 km 2 ) for global warming between 1.5∘ C and 2.2∘ C above pre-industrial GMST levels.

Eco-evolutionary processes shaping floral nectar sugar composition

Floral nectar sugar composition is assumed to reflect the nutritional demands and foraging behaviour of pollinators, but the relative contributions of evolutionary and abiotic factors to nectar sugar composition remain largely unknown across the angiosperms. We compiled a comprehensive dataset on nectar sugar composition for 414 insect-pollinated plant species across central Europe, along with phylogeny, paleoclimate, flower morphology, and pollinator dietary demands, to disentangle their relative effects. We found that phylogeny was strongly related with nectar sucrose content, which increased with the phylogenetic age of plant families, but even more strongly with historic global surface temperature. Nectar sugar composition was also defined by floral morphology, though it was not related to our functional measure of pollinator dietary demands. However, specialist pollinators of current plant-pollinator networks predominantly visited plant species with sucrose-rich nectar. Our results suggest that both physiological mechanisms related to plant water balance and evolutionary effects related to paleoclimatic changes have shaped floral nectar sugar composition during the radiation and specialisation of plants and pollinators. As a consequence, the high velocity of current climate change may affect plant-pollinator interaction networks due to a conflicting combination of immediate physiological responses and phylogenetic conservatism.

Cloud Responses to Abrupt Solar and CO2 Forcing: 1. Temperature Mediated Cloud Feedbacks

AbstractThere are many uncertainties in future climate, including how the Earth may react to different types of radiative forcing, such as CO2, aerosols, and even geoengineered changes in the amount of sunlight absorbed by Earth's surface. Here, we analyze model simulations where the climate system is subjected to an abrupt change of the solar constant by ±4%, and where the atmospheric CO2 concentration is abruptly changed to quadruple and half its preindustrial value. Using these experiments, we examine how clouds respond to changes in solar forcing, compared to CO2, and feedback on global surface temperature. The total cloud response can be decomposed into those responses driven by changes in global surface temperature, called the temperature mediated cloud feedbacks, and responses driven directly by the forcing that are independent of the global surface temperature. In this paper, we study the temperature mediated cloud changes to answer two primary questions: (a) How do temperature mediated cloud feedbacks differ in response to abrupt changes in CO2 and solar forcing? And (b) Are there symmetrical (equal and opposite) temperature mediated cloud feedbacks during global warming and global cooling? We find that temperature mediated cloud feedbacks are similar in response to increasing solar and increasing CO2 forcing, and we provide a short review of recent literature regarding the physical mechanisms responsible for these feedbacks. We also find that cloud responses to warming and cooling are not symmetric, due largely to non‐linearity introduced by phase changes in mid‐to‐high latitude low clouds and sea ice loss/formation.

Factors determining tropical upper-level cloud radiative effect in the radiative-convective equilibrium framework

Investigation of the major factors determining tropical upper-level cloud radiative effect (TUCRE) is crucial for understanding cloud feedback mechanisms. We examined the TUCRE inferred from the outputs of historical runs and AMIP runs from CMIP6 models employing a radiative-convective equilibrium (RCE). In this study, we incorporated the RCE model configurations of atmospheric dynamics and thermodynamics from the climate models, while simplifying the intricate systems. Using the RCE model, we adjusted the global mean surface temperature to achieve energy balance, considering variations in tropical cloud fraction, regional reflectivity, and emission temperature corresponding to each climate model. Subsequently, TUCRE was calculated as a unit of K/%, representing the change in global mean surface temperature (K) in response to an increment in the tropical upper-level clouds (%). Our RCE model simulation indicates that the major factors determining the TUCRE are the emission temperatures of tropical moist-cloudy and moist-clear regions, as well as the fraction of tropical upper-level clouds. The higher determination coefficients between TUCRE and both the emission temperature of tropical moist regions and the upper-level cloud fraction are attributable to their contribution to the trapping effect on the outgoing longwave radiations, which predominantly determines TUCRE. Consequently, the results of this study underscore the importance of accurately representing the upper-level cloud fraction and emission temperature in tropical moist regions to enhance the representation of TUCRE in climate models.

Effective attention model for global sea surface temperature prediction

Sea Surface Temperature (SST) is an essential parameter of the ocean–atmosphere system, so accurately predicting the future SST is of great significance. To address the low prediction accuracy due to insufficient consideration of spatiotemporal correlations in existing machine learning models, this paper proposes an Effective Attention Model (EAM) to achieve daily global SST prediction (predicting the SST of the next 15 days using those of the past 15 days) and monthly global SST prediction (predicting the SST of the next 12 months using those of the past 12 months). The EAM adopts an attention mechanism to avoid information redundancy and improve prediction accuracy. This is the first work to realize high-accuracy global SST prediction with a high spatial resolution of 0.25° on two time scales (daily and monthly) based on deep learning. Experiments show that (1) the prediction results have a consistent global spatiotemporal distribution with the ground truth, with the Root Mean Squared Difference (RMSD) of daily prediction ranging from 0.212 to 0.932 ℃ and the RMSD of monthly prediction ranging from 0.666 to 0.782 ℃. The EAM is superior to the deep learning models (ConvLSTM, 3D-CNN, and LSTM) and the numerical model (HYCOM); (2) Besides global prediction, the EAM also achieves good performance in a climate change sensitive area (Niño 3.4 sea area), with the lowest RMSD of 0.231 ℃. This study may not only benefit the research on climate events (El Niño and La Niña) but also serve to predict other marine disasters (such as heat waves and storms). In the future, the proposed attention module can also be adapted to other prediction scenarios such as meteorology.

An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling.

Sea surface temperature (SST), with its complex and dynamic behavior, is a major driver of ocean-atmosphere interactions. The purpose of this study is to investigate the behavior of SST and its prediction using a chaotic approach. Average mutual information (AMI) and Cao methods were used to reconstruct the phase space. The Lyapunov exponent and correlation dimension were used to investigate chaos. The Lyapunov exponent index was used to predict SST with a 5-year average prediction horizon using the local prediction method between 2023 and 2027. The results showed a 3-month delay time for the Pacific and Antarctic Oceans, and a 2-month delay time for the Atlantic, Indian, and Arctic Oceans. The optimal embedding dimension for all oceans is between 6 and 7. Our analysis reveals that the dynamics of SST in all oceans exhibit varying degrees of chaos, as indicated by the correlation dimension. The local prediction method achieves relatively accurate short-term SST predictions due to the clustering of SST points around specific attractors in the phase space. However, in the long term, the accuracy of this method decreases as the points in the phase space of SST can spread randomly. The model performance ranking with a Percent Mean Relative Absolute Error shows that the Indian Ocean has the best performance compared to other oceans, while the Atlantic, Pacific, and Antarctic and Arctic Oceans are in the next ranks. This study contributes to understanding the dynamics of SST and has practical value for use in the development of climate models.

Impact of seasonal global land surface temperature (LST) change on gross primary production (GPP) in the early 21st century

Understanding the impact of global land surface temperature (LST) changes on gross primary production (GPP) is crucial for addressing global sustainability challenges effectively. This article explores the effects of winter and summer temperature variations on GPP from 2001 to 2020 on a global scale, employing a range of modeling techniques to investigate the complex relationship between temperature changes and GPP. The study employs various modeling approaches, including linear regression models, artificial neural networks, Random Forest (RF) models, and the XGBoost algorithm to examine both linear and non-linear relationships between global temperature changes and GPP. The results reveal that the RF and XGBoost models effectively capture the non-linear relationship between LST and GPP during both summer and winter, demonstrating a high level of statistical significance (P < 0.01) and achieving R2 values of 0.598. Furthermore, the study applies a Sen+MK trend analysis model to identify six distinct trend patterns in LST and GPP during both summer and winter seasons. In summer, the area exhibiting a non-significant increasing trend (NSI) for GPP covers 65,066,274.77 km2, whereas in winter, a strong significant decreasing trend (SSD) for GPP spans 64,537,108.31 km2. Notably, GPP patterns closely mirror those of LST, suggesting that rising high-temperature conditions during summer lead to reduced GPP, while increased low temperatures during winter promote GPP growth. Robust correlations between LST and GPP are observed under various trend conditions in both summer and winter, displaying strong statistical significance (P < 0.01), with SSD achieving a maximum R2 of 0.594. These findings contribute significantly to our comprehensive understanding of the dynamic relationship between LST and GPP and offer valuable insights for addressing the sustainable development of global climate change challenges.

FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation

AbstractEmulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation.

Exploring the contribution of low‐frequency internal variability modes to global mean sea surface temperature variability based on large ensembles

AbstractBased on three large ensemble (LE) historical simulations, this study explored the contribution of low‐frequency internal variability (IV) modes to global mean sea surface temperature anomaly (GmSST) during 1920–2010. The dominant contributions of low‐frequency IV modes to GmSST in the three large ensembles are quite different. The low‐frequency IV modes in the Indo‐Pacific Ocean (i.e., IPO and IOBW) dominate the low‐frequency GmSST signal for the CESM2 and MPI LEs (the ensemble median contribution value reaches 70%–80%), while the low‐frequency IV modes in the North Atlantic dominate for the FGOALS‐g3 LE (contribute more than 50%). CESM2 LE and MPI LE can reasonably reproduce the impact of low‐frequency modes in the Indo‐Pacific ocean as observed, but FGOALS‐g3 LE underestimates this impact and overestimates the extratropical impact (on GmSST) of the North Atlantic. After removing the external forcing, it still retains the excessive signs in low‐frequency IV modes in FGOALS‐g3 LE over the North Atlantic. Furthermore, we used a pattern adjustment approach to revise the surplus effect in the North Atlantic. After revision, the contribution of decadal IVs in the North Atlantic to GmSST is reduced by 30%. Meanwhile, the tropical contribution in the Indo‐Pacific Ocean is increased and that is closer to the observed one. This approach can be employed to revise the weak or strong IV signals in other regions or LEs, which is meaningful for reducing the uncertainty of IV signals.

Cellulase-assisted refining in a paperboard mill: Avoided emissions from energy savings. A case study of a Finnish paperboard mill

The 2023 being on its way to be the warmest year in the 174-year global surface temperature history it is becoming more and more evident how crucial it is to break the linkage between economic growth and greenhouse gas emissions.The pulp and paper industry is highly energy intensive industry sector. It is the fourth largest industrial energy user and currently produces approximately 1.3% of global greenhouse gas emissions. While the demand for paper and paperboard products keeps rising, it is essential for the industry to find energy efficient solutions for reducing the environmental impact of the pulp and paper products. 80% of the progress in energy efficiency in Finnish paper mills is due to improved technology and more optimal operational modes. Optimizing existing processes has bigger effect on energy efficiency than closing old and starting new paper mills.Pulp refining is one of the most energy intense steps in the paperboard production, representing 15%–18% of the total energy consumption of a paper mill. The effect of a commercial cellulase enzyme product was evaluated for energy savings and avoided emissions in the refining process of a paperboard mill. Data from a paperboard line using Bleached Softwood Kraft Pulp (BSKP) from a separate pulp mill was provided by a Finnish paperboard mill containing production runs both with and without commercial cellulase product ECOPULP® R. Data was collected from the mill automation system to evaluate and compare a refining capacity restricted run and a run that was not refining capacity restricted.Analysis of the data showed a reduction of refining energy by approximately 16%–17% when the cellulase enzyme was used. This corresponds to 2 kg of CO2 emissions avoided per tonne of pulp refined when calculated using the Finnish average electricity emissions intensity in 2022. Use of the cellulase enzyme enabled the refining results that could not have been reached with the bare mechanical refining capacity of the mill.Cellulases can be efficient processing aids in pulp refining. The reduction of required mechanical refining and electrical power to run the refiners to achieve the required freeness can significantly affect the carbon footprint of the paperboard products, whereas the negative impact of emissions from enzyme production is negligible. Reduction of CO2 emissions is especially significant in the areas where the electricity mix used for the production is based heavily on fossil fuels.

Rethinking the Susceptibility‐Based Strategy for Marine Cloud Brightening Climate Intervention: Experiment With CESM2 and Its Implications

AbstractPrevious modeling studies indicate that even though marine cloud brightening under a susceptibility‐based strategy is effective in reducing the global average surface temperature, it triggers a La Niña‐like sea‐surface temperature response with cooling mostly confined within lower latitudes. Here we explore a different cloud seeding strategy involving seeding of regions with low susceptibility, primarily over the ocean in midlatitudes during the winter. Simulations with the Community Earth System Model, version 2 reveal that because the regional forcing is weaker and more widespread, cooling is more evenly distributed over the globe. This new strategy also does not result in the La Niña‐like state seen in the other strategies.

Spatiotemporal Expansion of Algal Blooms in Coastal China Seas.

The coastal seas of China are increasingly threatened by algal blooms, yet their comprehensive spatiotemporal mapping and understanding of underlying drivers remain challenging due to high turbidity and heterogeneous water conditions. We developed a singular value decomposition-based algorithm to map these blooms using two decades of MODIS-Aqua satellite data, spanning from 2003 to 2022. Our findings indicate significant algal activity along the Chinese coastline, impacting an average annual area of approximately 1.8 × 105 km2. The blooms exhibit peak intensity in August, while the maximum affected area occurs in September, featuring multifrequency outbreaks in spring, and pronounced large-scale events in summer and autumn. Notably, our analysis demonstrates a robust 67% increase in bloom occurrences over the study period. This expansion is primarily attributed to increased nutrient inflow from terrestrial sources linked to human activity and precipitation, compounded by rising global sea surface temperatures. These spatiotemporal insights are critical for devising effective management strategies and policies to mitigate the impacts of algal blooms.