Geoengineering Research Articles

A comprehensive review of convective heat transfer in humid air and its physical perspectives in climate change

The mechanisms and consequences of convective heat transfer in humid air are examined in this thorough review, which highlights the importance of this process in a variety of physical contexts, such as climate science, engineering, and meteorology. A key component of the thermal dynamics of the atmosphere, convective heat transfer is fueled by temperature gradients and the presence of moisture. It also affects weather patterns, energy distribution, and climate change. This review article summarizes the body of research on the basic ideas of convective heat transfer, including how humidity affects buoyancy, airflow patterns, and thermal conductivity. In addition, it looks at how convective processes affect regional and global climate systems, emphasizing the feedback loops that exist between atmospheric phenomena, heat transfer, and humidity. This review also covers the difficulties and developments in simulating convective heat transfer in humid air. This will ultimately help improve designs and strategies in a variety of fields that depend on thermal management. The purpose of this review article is to summarize the important published articles on the enhancement of convective heat transfer in the presence of humid air and its physical perspectives. Furthermore, this review attempts to close the gap between theoretical knowledge and real-world applications of convective heat transfer in the context of climate change by combining physical viewpoints with empirical data given in previous published literature.

Spatio-Temporal Analysis of Drought with SPEI in the State of Mexico and Mexico City

Climate change and increasing water demand are causing supply problems in Mexico City and the State of Mexico. The lack of complete and up-to-date meteorological information makes it difficult to understand and analyze climate phenomena such as droughts. Climate Engine provides decades of climate data to analyze such changes. These data were used to calculate SPEI (Standardized Precipitation-Evapotranspiration index) at scales of 1, 3, 6, 9, 12, and 24 months between 1981 and 2023 in the study area. The Standard Normal Homogeneity Test (SNHT) indicated greater homogeneity in temperature data, while precipitation data exhibited potential inhomogeneities. The Mann–Kendall test showed no significant trend for precipitation but a clear increasing trend in temperature. Droughts have become more frequent and severe over the last decade, particularly in the western State of Mexico and the southwest of Mexico City. The wettest years within the last 14 years were 2010, 2015, and 2018, while the most severe droughts occurred in 2017, 2019, 2020, 2021, and 2023. The findings suggest intensifying drought conditions, likely driven by rising temperatures and climate variability. These trends emphasize the need for improved water resource management and adaptation strategies to mitigate the growing impact of droughts in central Mexico.

Localized environmental variability within the Hindukush-Himalayan region of Pakistan

The Hindukush-Himalayan (HKH) region, known for its eco-environmental importance, has been witnessing transformations in recent years governed by factors such as climate variability, land use shifts, and population growth. These changes have profound implications for regional sustainability, water resources, and livelihood. This study attempts to explore the spatial and temporal variability in selected environmental parameters including land surface temperature (LST), normalized difference vegetation index (NDVI), precipitation patterns, and normalized difference snow index (NDSI), and land use land cover (LULC) from 1990 to 2022 using Landsat imageries (30 m spatial resolution), CHIRPS precipitation data at 0.05° spatial resolution. The study area spans 32,000 km2 covering two major political/administrative divisions (Malakand and Hazara) in the HKH region of Pakistan. The study area was selected primarily because of the unprecedented changes over the last three decades. For detailed spatial analysis, the area was divided into five elevation zones and LST, NDVI, NDSI, and LULC analyses were conducted utilizing primarily the Google Earth Engine (GEE) platform and climate engine. The study results revealed a notable rise in LST in the lowest elevation zone. The NDVI and LULC analyses revealed a noticeable decline in vegetation cover from 5988 km2 in 1990, to 4225 km2 by 2010, followed by a growth to 7669 km2 in 2022, since 2010 after the launching of the Billion Tree Tsunami Afforestation Project (BTTAP) in 2013. Likewise, the precipitation patterns exhibit transitioning from low to high precipitation levels. However, the most notable finding of the study is the marked decline in snow covered area 7000 km2 to 3800 km2 between 1990 and 2022.

Desert Edens: Colonial Climate Engineering in the Age of Anxiety by Philipp Lehmann (review)

Desert Edens: Colonial Climate Engineering in the Age of Anxiety by Philipp Lehmann (review)

Integrating Environment and Empire into German History. Roundtable on Philipp Lehmann's Desert Edens : Colonial Climate Engineering in the Age of Anxiety .

Integrating Environment and Empire into German History. Roundtable on Philipp Lehmann's Desert Edens : Colonial Climate Engineering in the Age of Anxiety .

Ocean nourishment sequestering carbon dioxide in the deep ocean

Ocean nourishment sequestering carbon dioxide in the deep ocean Phytoplankton have been essential to life on Earth for over 35 billion years. Through photosynthesis, they consume carbon dioxide on a scale comparable to that of forests and other land plants. Edwina Tanner from the WhaleX Foundation shares insights on this and discusses the potential for plankton-based solutions in marine carbon dioxide removal (mCDR) and ocean nourishment. Great whales act as climate engineers, stimulating productivity by providing nutrients to microscopic phytoplankton, the planet’s real climate giants. Millions of tiny phytoplankton are critical components of the Earth’s system, producing at least 50% of the oxygen we breathe and playing a crucial role in the global carbon cycle. They are the invisible forest that represents around 80% of the biomass in the ocean that is eaten or dies and then sinks as marine snow to sequester gigatonnes of atmospheric carbon dioxide annually. Measurements by satellites show that phytoplankton represent the ‘fast’ carbon cycle that operates on the scale of days to weeks compared to decades or centuries, as do the forests on land. The UN Plankton Manifesto recognizes the heavy lifting that phytoplankton do, outlining the triple planetary crisis - biodiversity, climate, and pollution that plankton-based solutions can address (United Nations, 2024).

Navigating systemic risks in low-carbon energy transitions in an era of global polycrisis

Abstract Non-technical summary Accelerating global systemic risks impel as well as threaten low-carbon energy transitions. Polycrises can undermine low-carbon transitions, and the breakdown of low-carbon energy transitions has the potential to intensify polycrises. Identifying the systemic risks facing low-carbon transitions is critical, as is studying what systemic risks could be exacerbated by energy transitions. Given the urgency and scale of the required technological and institutional changes, integrated and interdisciplinary approaches are essential to determine how low-carbon transitions can mitigate, rather than amplify polycrisis. If done deliberately and through deliberation, low-carbon transitions could spearhead the integrative tools, methods, and strategies required to address the broader polycrisis. Technical summary The urgent need to address accelerating global systemic risks impels low-carbon energy transitions, but these same risks also pose a threat. This briefing discusses factors influencing the stability and resilience of low-carbon energy transitions over extended time-frames, necessitating a multidisciplinary approach. The collapse of these transitions could exacerbate the polycrisis, making it crucial to identify and understand the systemic risks low-carbon transitions face. Key questions addressed include: What are the systemic risks confronting low-carbon transitions? Given the unprecedented urgency and scale of required technological and institutional changes, how can low-carbon transitions mitigate, rather than amplify, global systemic risks? The article describes the role of well-designed climate policies in fostering positive outcomes, achieving political consensus, integrating fiscal and social policies, and managing new risks such as those posed by climate engineering. It emphasizes the importance of long-term strategic planning, interdisciplinary research, and inclusive decision-making. Ultimately, successful low-carbon transitions can provide tools and methods to address broader global challenges, ensuring a sustainable and equitable future amidst a backdrop of complex global interdependencies. Social media summary Low-carbon energy transitions must be approached so as to lower the risks of global polycrisis across systems.

Numerical Fit Modeling for Temperature Mitigation in Arid Cities

The purpose of the study is to develop a general method to predict local temperature changes from mitigating the urban heat island effect using local climate engineering. Specifically, the effects of a plume of calcite particles above cities have been found. Previous modeling work has been carried out with supercomputers, but those models have limited geographies and timelines. The main goal of this work is to produce a method that can be applied more generally and more quickly. This overcomes limited modeling data in arid regions. Arid cities show the most effective use of calcite plumes for local solar radiation management, but those areas have limited data. The new method is to use numerical fit techniques using actual weather data. The default heating and cooling rates are fit to historic rates, and then the radiative properties of the calcite are used to predict the change in the heat transfer rates. Air temperatures at a standard height of 2 m are predicted. The key findings are that the numerical fit gives comparable results to the full supercomputer model, but the numerical fit gives predictions of greater temperature change. This was explained as primarily due to how advection is handled differently by the methods. Adjustments in the methods are discussed so that the effect of advection is included. The conclusion is that numerical fit provides a method that can easily be applied to arid regions.

Thermal transport analysis for entropy generated flow of hybrid nanomaterial: modified Cattaneo–Christov heat and Darcy–Forchheimer

PurposeThe heat transport phenomenon in which energy transfers due to temperature differences is an important topic of interest for scientists in recent times. It is because of its wide range of applications in numerous domains such as electronics, heat dispersion, thermoregulation, cooling mechanism, the managing temperature in automotive mobile engines, climate engineering, magnetoresistance devices, etc. On account of such considerations, the magnetohydrodynamic (MHD) entropy rate for nanomaterial (CoFe2O4/C2H6O2) and hybrid nanomaterial (CoFe2O4+MoS4/C2H6O2) is analyzed. The Darcy–Forchheimer relation is utilized to describe the impact of a porous medium on a stretched sheet. Two nanoparticles molybdenum (MoS4) and cobalt ferrite (CoFe2O4) are combined to make hybrid nanomaterial (CoFe2O4+MoS4/C2H6O2). Heat flux corresponds to the Cattaneo–Christov model executed through heat transfer analysis. The influence of dissipation and heat absorption/generation on energy expression for nanomaterial (CoFe2O4+MoS4/C2H6O2) and hybrid nanomaterial (CoFe2O4+MoS4/C2H6O2) is described.Design/methodology/approachNonlinear partial differential expressions have been exchanged into dimensionless ordinary differential expressions using relevant transformations. Newton’s built-in shooting method is employed to achieve the required results.FindingsConcepts of fluid flow, energy transport and entropy optimization are discussed. Computational analysis of local skin friction and Nusselt number against sundry parameters for nanomaterial (CoFe2O4/C2H6O2) and hybrid nanomaterial (CoFe2O4+MoS4/C2H6O2) is engrossed. Larger magnetic field parameters decay fluid flow and entropy generation, while an opposite behavior is observed for temperature. Variation in magnetic field variables and volume fractions causes the resistive force to boost up. Intensification in entropy generation can be seen for higher porosity parameters, whereas a reverse trend follows for fluid flow. Heat and local Nusselt numbers rise with an increase in thermal relaxation time parameters.Originality/valueNo such work is yet published in the literature.

Climate intervention research in the World Climate Research Programme: a perspective

The 2023 World Climate Research Programme (WCRP) Open Science Conference underscored the critical need for increased climate change mitigation and adaptation efforts, along with enhanced climate knowledge and decision-making systems. This Perspective discusses climate intervention (CI) within WCRP’s research framework, emphasizing three main approaches: terrestrial carbon dioxide removal (CDR), marine CDR, and solar radiation modification (SRM). As global anthropogenic greenhouse gas emissions continue to rise, CI strategies are increasingly recognized as potentially critical supplements to traditional mitigation methods. We call for WCRP to take a leadership role in CI research, highlighting the need for inclusivity and collaboration, especially with researchers from the Global South, to establish a firm scientific foundation for an equitable and comprehensive assessment of the benefits and risks of CI approaches relative to the risks of anthropogenic climate change.

A protocol for model intercomparison of impacts of marine cloud brightening climate intervention

Abstract. A modeling protocol (defined by a series of climate model simulations with specified model output) is introduced. Studies using these simulations are designed to improve the understanding of climate impacts using a strategy for climate intervention (CI) known as marine cloud brightening (MCB) in specific regions; therefore, the protocol is called MCB-REG (where REG stands for region). The model simulations are not intended to assess consequences of a realistic MCB deployment intended to achieve specific climate targets but instead to expose responses to interventions in six regions with pervasive cloud systems that are often considered candidates for such a deployment. A calibration step involving simulations with fixed sea surface temperatures (SSTs) is first used to identify a common forcing, and then coupled simulations with forcing in individual regions and combinations of regions are used to examine climate impacts. Synthetic estimates constructed by superposing responses from simulations with forcing in individual regions are considered a means of approximating the climate impacts produced when MCB interventions are introduced in multiple regions. A few results comparing simulations from three modern climate models (CESM2, E3SMv2, and UKESM1) are used to illustrate the similarities and differences between model behavior and the utility of estimates of MCB climate responses that were synthesized by summing responses introduced in individual regions. Cloud responses to aerosol injections differ substantially between models (CESM2 clouds appear much more susceptible to aerosol emissions than the other models), but patterns in precipitation and surface temperature responses were similar when forcing is imposed with similar amplitudes in the same regions. A previously identified La Niña-like response to forcing introduced in the Southeast Pacific is evident in this study, but the amplitude of the response was shown to markedly differ across the three models. Other common response patterns were also found and are discussed. Forcing in the Southeast Atlantic consistently (across all three models) produces weaker global cooling than that in other regions, and the Southeast Pacific and South Pacific show the strongest cooling. This indicates that the efficiency of a given intervention depends on not only the susceptibility of the clouds to aerosol perturbations, but also the strength of the underlying radiative feedbacks and ocean responses operating within each region. These responses were generally robust across models, but more studies and an examination of responses with ensembles would be beneficial.

Effective control mechanisms of research on climate engineering techniques for the public good—The London Protocol regulatory approach as a role model

Effective control mechanisms of research on climate engineering techniques for the public good—The London Protocol regulatory approach as a role model

Enhancing artificial permafrost table predictions using integrated climate and ground temperature data: A case study from the Qinghai-Xizang highway

Enhancing artificial permafrost table predictions using integrated climate and ground temperature data: A case study from the Qinghai-Xizang highway

Desert Edens: Colonial Climate Engineering in the Age of Anxiety by Philipp Lehmann (review)

Desert Edens: Colonial Climate Engineering in the Age of Anxiety by Philipp Lehmann (review)

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices.

Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change

Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change

TEMPERATURE AND PRECIPITATION TRENDS DUE TO CLIMATE CHANGE IN THE HUIXTLA RIVER BASIN IN CHIAPAS, MEXICO

The objective of this work was to analyze whether there is a trend in climate change in the Huixtla River basin on the Mexican Pacific coast in the State of Chiapas. To detect this, temperature and precipitation trends were analyzed using data for the 1960–2014 period and the 27 indices proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI) at Despoblado, Escuintla, Finca Chicharras, Huehuetán, Huixtla, and Motozintla weather stations of the National Meteorological Service (SMN). Missing data were collected where necessary for precipitation using the U.S. National Wheater Service method and for temperatures using the Climate Engine platform. The indices were obtained with RClimDex by conducting the proposed data quality controls and classified on the basis of their statistical significance (0.05) and their increasing or decreasing trend. Among the changes found, higher temperatures were detected in the middle and lower western parts of the basin (TX90p), as well as a longer period of hot days (WSDI). The upper and lower eastern parts of the basin presented a shorter period of hot days. The upper and lower parts of the basin showed a cooling trend, with colder days (TX10p) over longer periods (CSDI). Across the basin, precipitation has increased by one and five days (RX1day and RX5day) as well as in total amount (PRCPTOT). In general, there were shorter dry periods (CDD) and longer wet periods (CWD). Rainfall above the R5mm, R10mm, R20mm, R70mm, and R150mm thresholds across the basin showed an increasing trend.

Incorporating Climate Engineering into Secondary Education: A New Direction for Indiana’s Science Classrooms

Climate change represents a significant existential challenge in modern times, with widespread anxiety over its impacts. There's a growing desire among students to explore climate solutions and identify actions they can personally undertake to address climate change. Despite mitigation efforts, current greenhouse gas emission reduction measures are insufficient, and the development of negative emission technologies is both slow and costly. Consequently, the past two decades have witnessed an escalating interest in alternative strategies to temporarily and intentionally cool the planet. These strategies include injecting reflective particles into the stratosphere or increasing the reflectivity of low-lying ocean clouds. Collectively known as climate engineering, also called geoengineering, these approaches could serve as a temporary shield against the most severe outcomes of climate change, buying time while efforts to mitigate emissions and enhance carbon sequestration reach the required scale.In line with the Indiana state science standards (HS-ESS3-4), this article presents the Climate Engineering Teaching Module (CETM) and recounts firsthand experiences from its application in high school settings. Launched over three years ago, the CETM has been effectively integrated into fifteen Indiana classrooms. As the future citizens and leaders of Indiana, it is crucial that students are well-informed on climate engineering. Educating them about the scientific, ethical, political, and economic facets of climate engineering is imperative for fostering responsible decision-making. By examining the trade-offs associated with climate engineering and encouraging students to conceptualize ways to implement these technologies beneficially while minimizing risks, the CETM offers an innovative and practical approach to teaching climate change and engineering design. This method not only prepares students for active engagement in future discussions on climate engineering but also equips them with a comprehensive understanding of its complexities.

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.

The impact of socioeconomic factors on vegetation restoration in karst regions: A perspective beyond climate and ecological engineering

The impact of socioeconomic factors on vegetation restoration in karst regions: A perspective beyond climate and ecological engineering