The scientific challenge of understanding and estimating climate change

Neural general circulation models for weather and climate

General circulation models (GCMs) are the foundation of weather and climate prediction1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting3,4. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.

Climate Change and Aging Health in Developing Countries

The climate of the Earth has changed throughout history. Climate change negatively impacts human rights in a wide range of ways. The study aims to find out the impact of climate change on aging health in developing countries. The study found that public health will be devastated if climate change continues unabated. Countries that are least responsible for global warming are most susceptible to the effects of higher temperatures, such as death and disease. In low- and middle-income countries, disasters are more likely to happen to people aged 60 and over. Although climate change affects all of us, older people are especially at risk from it, as evidenced by a growing body of research. The study also offers countermeasures and suggestions to develop aging health in developing countries affected by climate change.

A review contribution to emission trading schemes and low carbon growth

In this study, the researchers focus on policy instruments that employ a market-based strategy to promote emission reduction, find the key spots and recent changing aspects in the field of Eemission Trading Systems (ETS) and Low Carbon Growth, and make suggestions for future studies. Making use of the bibliometric analysis, the researchers examine a sample of 1,390 research from the ISI Web of Science database to find research activity on ETS and low carbon growth between 2005 and 2022. Also, the researchers visualized the knowledge domains in this field using software like CiteSpace and R-Biblioshiny. The research unravels the most influential published articles and authors on their citations and publications and their location and significance within the network. The researchers further examined the recent themes, identified the barriers to developing literature in this field, and made recommendations for future research. Research on ETS and low carbon growth globally lack cross-border collaborations between emerging and developed economies. The researchers concluded the study by recommending three future research directions.

An Update on Eukaryotic Viruses Revived from Ancient Permafrost

One quarter of the Northern hemisphere is underlain by permanently frozen ground, referred to as permafrost. Due to climate warming, irreversibly thawing permafrost is releasing organic matter frozen for up to a million years, most of which decomposes into carbon dioxide and methane, further enhancing the greenhouse effect. Part of this organic matter also consists of revived cellular microbes (prokaryotes, unicellular eukaryotes) as well as viruses that have remained dormant since prehistorical times. While the literature abounds on descriptions of the rich and diverse prokaryotic microbiomes found in permafrost, no additional report about "live" viruses have been published since the two original studies describing pithovirus (in 2014) and mollivirus (in 2015). This wrongly suggests that such occurrences are rare and that "zombie viruses" are not a public health threat. To restore an appreciation closer to reality, we report the preliminary characterizations of 13 new viruses isolated from seven different ancient Siberian permafrost samples, one from the Lena river and one from Kamchatka cryosol. As expected from the host specificity imposed by our protocol, these viruses belong to five different clades infecting Acanthamoeba spp. but not previously revived from permafrost: Pandoravirus, Cedratvirus, Megavirus, and Pacmanvirus, in addition to a new Pithovirus strain.

Climatic threshold of crop production and climate change adaptation: A case of winter wheat production in China

Global climate change has adversely affected agricultural production. Identifying the climatic threshold is critical to judge the impact and risk of climate change and proactively adapt agriculture. However, the climatic threshold of agriculture, especially crop production, remains unclear. To bridge this gap, taking winter wheat production from 1978 to 2017 in China as an example, this study clarified the definition of the climatic threshold of crop production and calculated it based on a mechanism model considering multiple factors and their synergies. The results showed that (1) the climate presented a warmer and wetter trend from 1978 to 2017, especially after 1996. (2) Water, fertilizer, and winter wheat yields increased significantly (22.4 mm/decade, 96.4 kg/ha·decade, and 674.2 kg/ha·decade, respectively, p < 0.01). (3) The average optimal temperature and water thresholds for winter wheat were 7.3°C and 569 mm, respectively. The temperature rise was unfavorable for winter wheat production, and the water supply increase was beneficial to winter wheat production. (4) Increasing irrigation and fertilization could raise the optimal temperature threshold and adapt to climate warming in most provinces, while Shandong and Shaanxi both needed to reduce fertilization. We established a generalized method for calculating the climatic threshold of agricultural production and found that multifactor synergistic effects could influence the climatic threshold. The climatic threshold of winter wheat changed with different adaptation levels. However, considering the limitations in resource availability and environmental capacity, increasing the use efficiency of water and fertilizer is more important for adapting to climate change in the future.

An Optoelectronic thermometer based on microscale infrared-to-visible conversion devices

Thermometric detectors are crucial in evaluating the condition of target objects spanning from environments to the human body. Optical-based thermal sensing tools have received extensive attention, in which the photon upconversion process with low autofluorescence and high tissue penetration depth is considered as a competent method for temperature monitoring, particularly in biomedical fields. Here, we present an optoelectronic thermometer via infrared-to-visible upconversion, accomplished by integrated light receiving and emission devices. Fully fabricated thin-film, microscale devices present temperature-dependent light emission with an intensity change of 1.5% °C^-1 and a spectral shift of 0.18 nm °C^-1. The sensing mechanism is systematically characterized and ascribed to temperature dependent optoelectronic properties of the semiconductor band structure and the circuit operation condition. Patterned device arrays showcase the capability for spatially resolved temperature mapping. Finally, in vitro and in vivo experiments implemented with integrated fiber-optic sensors demonstrate real-time thermal detection of dynamic human activity and in the deep brain of animals, respectively.

Projecting the Impacts of a Changing Climate: Tropical Cyclones and Flooding

PURPOSE OF REVIEW

There is clear evidence that the earth's climate is changing, largely from anthropogenic causes. Flooding and tropical cyclones have clear impacts on human health in the United States at present, and projections of their health impacts in the future will help inform climate policy, yet to date there have been few quantitative climate health impact projections.

RECENT FINDINGS

Despite a wealth of studies characterizing health impacts of floods and tropical cyclones, many are better suited for qualitative, rather than quantitative, projections of climate change health impacts. However, a growing number have features that will facilitate their use in quantitative projections, features we highlight here. Further, while it can be difficult to project how exposures to flood and tropical cyclone hazards will change in the future, climate science continues to advance in its capabilities to capture changes in these exposures, including capturing regional variation. Developments in climate epidemiology and climate science are opening new possibilities in projecting the health impacts of floods and tropical cyclones under a changing climate.

Combining global climate models using graph cuts

Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine individual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00382-022-06213-4.

Application of Guar Gum and its Derivatives as Green Binder/Separator for Advanced Lithium-Ion Batteries

Since their first commercialization in the 1990s,lithium-ion batteries (LIBs) have become an indispensible part of our everyday life in particular for portable electronic devices. LIBs have been considered as the most promising sustainable high energy density storage device. In recent years, there is a strong demand of LIBs for hybrid electric and electric vehicles to lower carbon footprint and mitigate climate change. However, LIBs have several issues, for example, high cost and safety issues such as over discharge, intolerance to overcharge, high temperature operation etc. To address these issues several new types of electrodes are being studied. Traditional binder PVDF is costly, difficult to recyle, undergoes side reactions at high temperature and cannot stabilize high energy density electrodes. To overcome these challenges, diiferent binders have been introduced with these electrodes. This minireview is focused on the application of guar gum as a binder for different electrodes and separator. The electrochemical performance of electrodes with guar gum has been compared with other binders.

Using a climate attribution statistic to inform judgments about changing fisheries sustainability

Sustainability—maintaining catches within the historical range of socially and ecologically acceptable values—is key to fisheries success. Climate change may rapidly threaten sustainability, and recognizing these instances is important for effective climate adaptation. Here, we present one approach for evaluating changing sustainability under a changing climate. We use Bayesian regression models to compare fish population processes under historical climate norms and emerging anthropogenic extremes. To define anthropogenic extremes we use the Fraction of Attributable Risk (FAR), which estimates the proportion of risk for extreme ocean temperatures that can be attributed to human influence. We illustrate our approach with estimates of recruitment (production of young fish, a key determinant of sustainability) for two exploited fishes (Pacific cod Gadus macrocephalus and walleye pollock G. chalcogrammus) in a rapidly warming ecosystem, the Gulf of Alaska. We show that recruitment distributions for both species have shifted towards zero during anthropogenic climate extremes. Predictions based on the projected incidence of anthropogenic temperature extremes indicate that expected recruitment, and therefore fisheries sustainability, is markedly lower in the current climate than during recent decades. Using FAR to analyze changing population processes may help fisheries managers and stakeholders to recognize situations when historical sustainability expectations should be reevaluated.

Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction

Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems.

Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI

In September 2019, a workshop was held to highlight the growing area of applying machine learning techniques to improve weather and climate prediction. In this introductory piece, we outline the motivations, opportunities and challenges ahead in this exciting avenue of research. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Climbing down Charney's ladder: machine learning and the post-Dennard era of computational climate science

The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational climate science, studying the behaviour of those same numerical equations over intervals much longer than weather events, and changes in external boundary conditions. Several subsequent decades of exponential growth in computational power have brought us to the present day, where models ever grow in resolution and complexity, capable of mastery of many small-scale phenomena with global repercussions, and ever more intricate feedbacks in the Earth system. The current juncture in computing, seven decades later, heralds an end to what is called Dennard scaling, the physics behind ever smaller computational units and ever faster arithmetic. This is prompting a fundamental change in our approach to the simulation of weather and climate, potentially as revolutionary as that wrought by John von Neumann in the 1950s. One approach could return us to an earlier era of pattern recognition and extrapolation, this time aided by computational power. Another approach could lead us to insights that continue to be expressed in mathematical equations. In either approach, or any synthesis of those, it is clearly no longer the steady march of the last few decades, continuing to add detail to ever more elaborate models. In this prospectus, we attempt to show the outlines of how this may unfold in the coming decades, a new harnessing of physical knowledge, computation and data. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Climate Change Effects on Aquaculture Production: Sustainability Implications, Mitigation, and Adaptations

Aquaculture continues to significantly expand its production, making it the fastest-growing food production sector globally. However, the sustainability of the sector is at stake due to the predicted effects of climate change that are not only a future but also a present reality. In this paper, we review the potential effects of climate change on aquaculture production and its implications on the sector's sustainability. Various elements of a changing climate, such as rising temperatures, sea-level rise, diseases and harmful algal blooms, changes in rainfall patterns, the uncertainty of external inputs supplies, changes in sea surface salinity, and severe climatic events have been discussed. Furthermore, several adaptation options have been presented as well as some gaps in existing knowledge that require further investigations. Overall, climate change effects and implications on aquaculture production sustainability are expected to be both negative and positive although, the negative effects outweigh the positive ones. Adapting to the predicted changes in the short-term while taking mitigation measures in the long-term could be the only way toward sustaining the sector's production. However, successful adaptation will depend on the adaptive capacity of the producers in different regions of the world.

The digital revolution of Earth-system science

Computational science is crucial for delivering reliable weather and climate predictions. However, despite decades of high-performance computing experience, there is serious concern about the sustainability of this application in the post-Moore/Dennard era. Here, we discuss the present limitations in the field and propose the design of a novel infrastructure that is scalable and more adaptable to future, yet unknown computing architectures.

New priorities for climate science and climate economics in the 2020s

Climate science and climate economics are critical sources of expertise in our pursuit of the Sustainable Development Goals. Effective use of this expertise requires a strengthening of its epistemic foundations and a renewed focus on more practical policy problems.

Should All Electrochemical Energy Materials Be Isomaterially Heterostructured to Optimize Contra and Co-varying Physicochemical Properties?

Sustainable energy and chemical/material transformation constrained by limited greenhouse gas generation impose a grand challenge and posit outstanding opportunities to electrochemical material devices. Dramatic advancements in experimental and computational methodologies have captured detailed insights into the working of these material devices at a molecular scale and have brought to light some fundamental constraints that impose bounds on efficiency. We propose that the coupling of molecular events in the material device gives rise to contra-varying or co-varying properties and efficiency improving partial decoupling of such properties can be achieved via introducing engineered heterogeneities. A specific class of engineered heterogeneity is in the form of isomaterial heterostructures comprised of non-native and native polymorphs. The non-native polymorph differs from their native/ground state bulk polymorph in terms of its discrete translational symmetry and we anticipate specific symmetry relationships exist between non-native and native structures that enable the formation of interfaces that enhance efficiency. We present circumstantial evidence and provide speculative mechanisms for such an approach with the hope that a more comprehensive delineation of proposed material design will be undertaken.

Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model

Global Climate Models are key tools for predicting the future response of the climate system to a variety of natural and anthropogenic forcings. Here we show how to use statistical mechanics to construct operators able to flexibly predict climate change. We perform our study using a fully coupled model - MPI-ESM v.1.2 - and for the first time we prove the effectiveness of response theory in predicting future climate response to CO2 increase on a vast range of temporal scales, from inter-annual to centennial, and for very diverse climatic variables. We investigate within a unified perspective the transient climate response and the equilibrium climate sensitivity, and assess the role of fast and slow processes. The prediction of the ocean heat uptake highlights the very slow relaxation to a newly established steady state. The change in the Atlantic Meridional Overturning Circulation (AMOC) and of the Antarctic Circumpolar Current (ACC) is accurately predicted. The AMOC strength is initially reduced and then undergoes a slow and partial recovery. The ACC strength initially increases due to changes in the wind stress, then undergoes a slowdown, followed by a recovery leading to a overshoot with respect to the initial value. Finally, we are able to predict accurately the temperature change in the North Atlantic.

The physics of numerical analysis: a climate modelling case study

The case is made for a much closer synergy between climate science, numerical analysis and computer science. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.