A document by Chaithanya B P. Click on the document to view its contents.

A document by Shreyaa Sanjay. Click on the document to view its contents.

Evaporation decreases the mass and increases the isotope composition of falling drops. Combining and integrating the dependence of the evaporation on the drop diameter and on the drop-environment humidity difference, the square of drop diameter is found to decrease with the square of vertical distance below cloud base. Drops smaller than 0.5 mm evaporate completely before falling 700 m in typical subtropical marine boundary layer conditions. The effect on the isotope ratio of equilibration with the environment, evaporation, and kinetic molecular diffusion is modeled by molecular and eddy diffusive fluxes after Craig and Gordon (1965), with a size-dependent parameterization of diffusion that enriches small drops more strongly, and approaches the rough aerodynamic limit for large drops. Rain shortly approaches a steady state with the subcloud vapor by exchange with a length scale of 40 m. Kinetic molecular diffusion enriches drops up to as they evaporate by up to +5~\permil~for deuterated water (HDO) and +3.5~\permil~for H$_2$$^{18}$O. Rain evaporation enriches undiluted subcloud vapor by +12~\permil~per mm rain, explaining enrichment of vapor in evaporatively cooled downdrafts that contribute to cold pools. Microphysics enriches the vapor lost by the early and complete evaporation of smaller drops in the distribution. Vapor from hydrometeors is more enriched than it would be by Rayleigh distillation or by mixtures of liquid rain and vapor in equilibrium with rain.

Flooding has been a significant problem over the past century in the United States (US), causing growing threats to human lives and socioeconomic damage. In the state of Kansas, since 1996, more than 1,500 flood events were recorded, resulting in an economic loss of between US$2b and US$5b. Many factors influence flood-susceptibility at a local scale. It may be helpful and timely to improve community resilience to flood disasters in Kansas. Our initial step was to assess factors that trigger flooding using Machine Learning (ML). Six ML algorithms: 1) Logistic Regression (LR); 2) Random Forest (RF); 3) Support Vector Machine (SVM); 4) K-nearest neighbor (KNN); 5) Adaptive Boosting (Ada Boost); 6) Extreme Gradient Boosting (XG boost) were used to evaluate their ability to classify locations in terms of floodsusceptibility. The learning data for these ML algorithms comprised a geo-spatial database of twelve floodsusceptibility factors from 1,528 flood inventories since 1996. The susceptibility factors comprised: rainfall, elevation, slope, aspect, flow direction, flow accumulation, Topographic Wetness Index (TWI), distance from the nearest stream, evapotranspiration, land cover, land surface temperature, and hydrographic soil type. The ML algorithms were compared, and the best algorithm was selected to estimate floodsusceptibility for each location in the geodatabase resulting in a flood-susceptibility map. A sensitivity analysis of floodsusceptibility factors indicated that the intensity or magnitude of the rainfall, land cover and soil type were the most significant factors for Kansas during this period.

Regional patterns of sea level rise are affected by a range of factors including glacial melting, which has occurred in recent decades and is projected to increase in the future, perhaps dramatically. Previous modeling studies have typically included fluxes from melting glacial ice only as a surface forcing of the ocean or as an offline addition to the sea surface height fields produced by climate models. However, observational estimates suggest that the majority of the meltwater from the Antarctic Ice Sheet actually enters the ocean at depth through ice shelf basal melt. Here we use simulations with an ocean general circulation model in an idealized configuration. The results show that the simulated global sea level rise pattern is sensitive to the depth at which Antarctic meltwater enters the ocean. Further analysis suggests that the response is dictated primarily by the steric response to the depth of the meltwater flux.

To limit global warming below 2°C, a drastic overall reduction from current CO2 emissions is needed. We argue that scientists should also participate in this effort in their professional activity and especially Earth scientists, on the grounds of maintaining credibility and leading by example. The strategies and measures to reach a low-carbon scientific activity require detailed estimates of the current footprint of laboratories. Here, we present the footprint of six laboratories in Earth, environmental and space sciences, representative of the AGU community, with a comprehensive scope also including international research infrastructures. We propose a novel method to attribute the footprint of any research infrastructure to any given research laboratory. Our results highlight that most laboratories have annual footprints reaching 10-20 tonnes CO2 equivalent per person (tCO2e.p-1), dominated by infrastructures and specifically satellites in three cases (with footprints up to 11 tCO2e.p-1 or 60%), while air-travels and purchases remain within the top three sources in all cases (2-4 tCO2e p-1 or 10-30% each). Consequently, footprints related to commuting and laboratory functioning, about 2 tCO2e.p-1 (20%) or less, are relatively modest compared to infrastructures, purchases and air-travels. Thus, reduction measures ignoring infrastructures may not be able to achieve reductions larger than 20 to 35% even with flight quotas and a substantial reduction of purchases. Finally, we also discuss how a deeper transformation of scientific practices, away from a fast science ideal, could make Earth and environmental sciences more sustainable and at the forefront of a rapid and drastic social bifurcation.

The occurrence of tropical cyclones (TC) in the Horn of Africa and nearby areas is for the first time examined to document their contribution to local rainfall and their trends over the period 1990-2020. An average 1.5 TC (of any intensity) per year was observed over the Western Arabian Sea, with two asymmetrical seasons, namely May-June (30% of cyclonic days) and September-December (70%). Case studies reveal that in many instances, TC-related rainfall extends beyond 500 km from the TC center, and that substantial rains occur one to two days after the lifecycle of the TC. Despite their rarity, in the otherwise arid to semi-arid context characteristic of the region, TCs contribute in both seasons to a very high percentage of total rainfall (up to 30 to 60%) over the northwestern Arabian Sea, the Gulf of Aden and their coastlines. Over inland northern Somalia, contributions are much lower. TCs disproportionately contribute to some of the most intense daily falls, which are often higher than the mean annual rainfall. A strong increase in the number of TCs is found from 1990 to 2020, hence their enhanced contribution to local rainfall. This increase is associated with a warmer eastern / southern Arabian Sea, a decrease in vertical wind shear, and a strong increase in tropospheric moisture content.

The United States is facing two major issues: climate change and the loss of agricultural land. Various legislative measures, such as the Bipartisan Infrastructure Law of 2021, the Inflation Reduction Act of 2022, and the Protecting Future Farmland Act of 2023, have been enacted to tackle these challenges. Virginia’s 2020 Clean Energy Act aims for 100% renewable power by 2045. However, solar and wind power currently make up only 3% of the state’s energy. Since 1982, the U.S. has been losing a significant amount of farmland, with around 25 million acres lost, equivalent to 2,000 acres per day between 2001 and 2016. In Virginia, 339,800 acres of farmland were converted to urban or residential use from 2001 to 2016. This conversion negatively affects the potential renewable energy supply and undermines food production. This research aims to explore agrivoltaics as an innovative and potential solution to preserving farmland around the urban fringe, linking agricultural productivity with solar energy. The results reveal that approximately 37,000 acres of farmland within a 5-mile buffer of the Richmond metropolitan area could potentially produce 209,851,792 pounds of food annually, satisfying about 30% of the area's grain and vegetable requirements. Additionally, allocating 10% of this farmland for bifacial solar panels could fulfill the entire metropolitan area's energy needs solely with solar power. However, limitations such as data scarcity on local food consumption and detailed crop yields were encountered, necessitating the use of estimates and secondary data sources. Future directions of this research include identifying optimal locations for solar panel installation on farmlands, evaluating the agricultural productivity impacts of agrivoltaics, and selecting suitable solar technologies for specific locations.

The CNRM-Cerfacs Climate Prediction System (C3PS) is a new research modeling tool for performing climate reanalyses and seasonal-to-multiannual predictions for a wide array of earth system variables. C3PS is based on the CNRM-ESM2-1 model including interactive aerosols and stratospheric chemistry schemes as well as terrestrial and marine biogeochemistry enabling a comprehensive representation of the global carbon cycle. C3PS operates through a seamless coupled initialization for the atmosphere, land, ocean, sea ice and biogeochemistry components that allows a continuum of predictions across seasonal to interannual time-scales. C3PS has also contributed to the Decadal Climate Prediction Project (DCPP-A) as part of the sixth Coupled Model Intercomparison Project (CMIP6). Here we describe the main characteristics of this novel earth system-based prediction platform, including the methodological steps for obtaining initial states to produce forecasts. We evaluate the entire C3PS initialisation procedure with the most up-to-date observations and reanalysis over 1960-2021, and we discuss the overall performance of the system in the light of the lessons learnt from previous and actual prediction platforms. Regarding the forecast skill, C3PS exhibits comparable seasonal predictive skill to other systems. At the decadal scale, C3PS shows significant predictive skill in surface temperature during the first two years after initialisation in several regions of the world. C3PS also exhibits potential predictive skill in net primary production and carbon fluxes several years in advance. This expands the possibility of applications of forecasting systems, such as the possibility of performing multi-annual predictions of marine ecosystems and carbon cycle.

This study introduces an ocean-based carbon dioxide removal (CDR) approach: Nearshore Macroalgae Aquaculture for Carbon Sequestration (N-MACS). By cultivating macroalgae in nearshore ocean surface areas, N-MACS aims to sequester CO2 with subsequent carbon storage. Utilizing an Earth System Model with intermediate complexity (EMIC), we explore the CDR potential of N-MACS alongside its impacts on the global carbon cycle, marine biogeochemistry and marine ecosystems. Our investigations unveil that coastal N-MACS could potentially sequester 0.7 to 1.1 GtC yr-1. However, it also significantly suppresses marine phytoplankton net primary productivity because of nutrient removal and canopy shading, counteracting approximately 30% of the N-MACS CDR capacity. This suppression of surface NPP, in turn, reduces carbon export out of the euphotic zone to the ocean interior, leading to elevated dissolved oxygen levels and diminished denitrification in present-day oxygen minimum zones. Effects due to harvesting-induced phosphorus removal continue for centuries even beyond the cessation of N-MACS.

State-of-the-art climate models simulate a large spread in the mean-state Atlantic meridional overturning circulation (AMOC), with strengths varying between 12 and 25 Sv. Here, we introduce a framework for understanding this spread by assessing the balance between the thermal-wind expression and surface water mass transformation in the North Atlantic. The intermodel spread in the mean-state AMOC strength is shown to be related to the overturning scale depth: climate models with a larger scale depth tend to also have a stronger AMOC. Intermodel variations in the overturning scale depth are also related to intermodel variations in North Atlantic surface buoyancy loss and stratification. We present a physically-motivated scaling relationship that links the scale-depth variations to buoyancy forcing and stratification in the North Atlantic, and thus connects North Atlantic surface processes to the interior ocean circulation. These results offer a framework for reducing mean-state AMOC biases in climate models.

Existing research has employed various methods to quantify urban heat island (UHI) effects, but the ideal method for individual cities remains unclear. This study investigated how different methods influence UHI understanding in Addis Ababa, a tropical city facing UHI challenges. Three methods were compared: dynamic urbanization, natural and built-up fractions, and urban center vs. surrounding rural areas. Satellite data and spatial analyses revealed maximum daytime UHIs of 4°C and 3.1°C in summer and autumn, respectively. Examining the mean temperature differences between urban and rural areas across methods yielded diverse results. This suggests that while the ‘dynamic urbanization’ method is statistically favorable in this specific case, averaging results from multiple methods produced a more robust and generalizable approach to understanding UHIs in different urban contexts. Ultimately, this study highlights the importance of context-specific method selection for accurately understanding the complex interplay between urban and rural environments.

Arctic clouds play a key role in Arctic climate variability and change; however, contemporary climate models struggle to simulate cloud properties accurately. Model-simulated cloud properties are determined by the physical parameterizations and their interactions within the model configuration. Quantifying effects of individual parameterization changes on model-simulated clouds informs efforts to improve cloud properties in models and provides insights on climate system behavior. This study quantities the influence of individual parameterization schemes on Arctic low cloud properties within the Hadley Centre Global Environmental Model 3 atmospheric model using a suite of experiments where individual parameterization packages are changed one-at-a-time between two configurations: GA6 and GA7.1. The results indicate that individual parameterization changes explain most of the cloud property differences, whereas multiple parameterizations, including non-cloud schemes, contribute to cloud radiative effect differences. The influence of a parameterization change on cloud properties is found to vary by meteorological regime. We employ a three-term decomposition to quantify contributions from (1) regime independent, (2) regime dependent, and (3) the regime frequency of occurrence changes. Decomposition results indicate that each term contributes differently to each cloud property change and that non-cloud parameterization changes make a substantial contribution to the LW and SW cloud radiative effects by modifying clear-sky fluxes differently across regimes. The analysis provides insights on the role of non-cloud parameterizations for setting cloud radiative effects, a model pathway for cloud-atmosphere circulation interactions, and raises questions on the most useful observational approaches for improving models.

We revisit the nature of the ocean bottom pressure (OBP) seasonal cycle by leveraging the mounting GRACE-based OBP record and its assimilation in the ocean state estimates produced by the project for Estimating the Circulation and Climate of the Ocean (ECCO). We focus on the mean seasonal cycle from both data and ECCO estimates, examining their similarities and differences and exploring the underlying causes. Despite substantial year-to-year variability, the 21-year period studied (2002–2022) provides a relatively robust estimate of the mean seasonal cycle. Results indicate that the OBP annual harmonic tends to dominate but the semi-annual harmonic can also be important (e.g., subpolar North Pacific, Bellingshausen Basin). Amplitudes and short-scale phase variability are enhanced near coasts and continental shelves, emphasizing the importance of bottom topography in shaping the seasonal cycle in OBP. Comparisons of GRACE and ECCO estimates indicate good qualitative agreement, but considerable quantitative differences remain in many areas. The GRACE amplitudes tend to be higher than those of ECCO typically by 10%–50%, and by more than 50% in extensive regions, particularly around continental boundaries. Phase differences of more than 1 (0.5) months for the annual (semiannual) harmonics are also apparent. Larger differences near coastal regions can be related to enhanced GRACE data uncertainties and also to the absence of gravitational attraction and loading effects in ECCO. Improvements in both data and model-based estimates are still needed to narrow present uncertainties in OBP estimates.

Our water resources have changed over the last century through a combination of water management evolutions and climate change. Understanding and decomposing these drivers of discharge changes is essential to preparing and planning adaptive strategies. We propose a methodology combining a physical-based model to reproduce the natural behavior of river catchments and a parsimonious model to serve as a framework of interpretation, comparing the physical-based model outputs to observations of discharge trends. We show that over Europe, especially in the South, the dominant explanations for discharge trends are non-climatic factors. Still, in some catchments of Northern Europe, climate change seems to be the dominating driver of change. We hypothesize that the dominating non-climatic factors are irrigation development, groundwater pumping and other human water usage, which need to be taken into account in physical-based models to understand the main drivers of discharge and project future changes.

A document by Ehsan Erfani. Click on the document to view its contents.

A document by Ehsan Erfani. Click on the document to view its contents.

A document by Alamgir Hossan. Click on the document to view its contents.

Peatlands are fundamental deposits of organic carbon. Thus, their protection is of crucial importance to avoid emissions from their degradation. Peat is a mixture of organic soil that originates from the accumulation of wetland plants under continuous or cyclical anaerobic conditions for long periods. Hence, a precise quantification of peat deposits is extremely important; for that, remote- and proximal-sensing techniques are excellent candidates. Unfortunately, remote-sensing can provide information only on the few shallowest centimeters, whereas peatlands often extend to several meters in depth. In addition, peatlands are usually characterized by difficult (flooded) terrains. So, frequency-domain electromagnetic instruments, as they are compact and contactless, seem to be the ideal solution for the quantitative assessment of the extension and geometry of peatlands. Generally, electromagnetic methods are used to infer the electrical resistivity of the subsurface. In turn, the resistivity distribution can, in principle, be interpreted to infer the morphology of the peatland. Here, to some extent, we show how to shortcut the process and include the expectation and uncertainty regarding the peat resistivity directly into a probabilistic inversion workflow. The present approach allows for retrieving what really matters: the spatial distribution of the probability of peat occurrence, rather than the mere electrical resistivity. To evaluate the efficiency and effectiveness of the proposed probabilistic approach, we compare the outcomes against the more traditional deterministic fully nonlinear (Occam's) inversion and against some boreholes available in the investigated area.  

Climate change is posing unprecedented challenges, necessitating the development of effective climate adaptation. Conventional computational models of climate adaptation frameworks inadequately account for our capacity to learn, update, and enhance decisions as exogenous information is collected. Here we investigate the potential of reinforcement learning (RL), a machine learning technique that exhibits efficacy in acquiring knowledge from the environment and systematically optimizing dynamic decisions, to model and inform adaptive climate decision-making. To illustrate, we derive adaptive stratigies for coastal flood protections for Manhattan, New York City, considering continuous observations of sea-level rise throughout the 21st century. We find that, when designing adaptive seawalls to protect Manhattan, the RL-derived strategy leads to a significant reduction in the expected cost, 6% to 36% under the moderate emissions scenario SSP2-4.5 (9% to 77% under the high emissions scenario SSP5-8.5), compared to previous methods. When considering multiple adaptive policies (buyout, accommodate, and dike), the RL approach leads to a further 5% (15%) reduction in cost, showcasing RL’s flexibility in addressing complex policy design problems when multiple policies interact. RL also outperforms conventional methods in controlling tail risk (i.e., low probability, high impacts) and avoiding losses induced by misinformation (e.g., biased sea-level projections), demonstrating the importance of systematic learning and updating in addressing extremes and uncertainties related to climate adaptation. The analysis also reveals that, given the large uncertainty and potential misjudgment about climate projection, “preparing for the worst” is economically more beneficial when adaptive strategies, such as those supported by the RL approach, are applied.

The Ocean Heat Content (OHC) anomaly has become an increasingly important climate parameter for the Intergovernmental Panel on Climate Change (IPCC) assessment and evaluation of climate change. One good reason is that the OHC appears to be less prone to climate variability, typically experienced with surface temperature and other climate parameters. Therefore, a reasonable estimate of OHC increase is important for research and climate related policies. Levitus et al. (2012) (https://doi.org/10.1029/2012GL051106) is a relevant ocean heat content related paper, and their analysis and estimate of OHC increase between 1955 and 2010 is high, about four to seven times greater than what the law of conservation of energy may allow. The source of discrepancy is analyzed in this commentary and it appears to be a result of using corrected ocean data sources. Therefore, verification of the observed increase in OHC using alternative ocean data sources is recommended.

Today the world is tackling climate change. The global threat of energy poverty along with the growing need for energy has escalated this crisis. The promotion of renewable energy sources is widely known as the main solution to this challenge. Many International and regional agreements address various aspects of renewable energy development such as trade, transit, security, and investment. Foreign investment is recognized as a crucial prerequisite for the global deployment of renewable energy since not all States have the financial and technological potential to develop this sector. Various investment agreements are signed to facilitate and promote investments. These instruments contain a mixture of obligations that have direct or indirect effects. Expropriation provisions which are often crystallized in the form of 'a duty not to expropriate' are among these obligations. This article analytically describes the legal aspects of this standard and proposes the trends that can better protect the foreign investments in this sector; a factor without which the foreign investors would normally be reluctant to invest. It concludes that restricted police power, guarantees of transfer, and a full compensation standard that entails the payment of compound interest are the prominent legal features that can best perform this task.

It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence (AI) algorithms, however, is challenged with its vagueness, non-quantitiveness, computational inefficiency, etc. During the past 18 years, these challenges have been essentially resolved, with the establishment of a rigorous formalism of causality analysis initially motivated from atmospheric predictability. This not only opens a new field in the atmosphere-ocean science, namely, information flow, but also has led to scientific discoveries in other disciplines, such as quantum mechanics, neuroscience, financial economics, etc., through various applications. This note provides a brief review of the decade-long effort, including a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications in geoscience pertaining to this journal, such as those on the anthropogenic cause of global warming, the decadal prediction of El Niño Modoki, the forecasting of an extreme drought in China, among others.

The higher-order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models. This study investigates an underexplored aspect of CLUBB: its capacity to simulate near-surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux. Using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB’s stability-driven substepping performed at each atmospheric time step. The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB-computed zonal and meridional wind speed tendencies. Our 30-year present-day climate simulations (1980-2010) show that static coupling overestimates 10-m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10-m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind-speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near-surface wind speeds and associated turbulent momentum transport structure. Notably, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns.