Large-scale Variability Research Articles

Beyond MRV: Combining remote sensing and ecosystem modeling for geospatial monitoring and attribution of forest carbon fluxes over Maryland, USA

Abstract Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of forest carbon in climate mitigation plans. These initiatives are generally limited by a lack of relevant data on forest carbon stocks and fluxes past-to-future. Previously, we developed a new forest carbon modeling system that combines high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and projected future forest carbon sequestration potential for the state of Maryland. Here we extended this work to provide a consistent geospatial approach for monitoring changes in forest carbon stocks over time. Utilizing the same data and modeling system developed previously for planning, we integrated historical input data on weather and disturbance to reconstruct the history of vegetation dynamics and forest above-ground carbon stocks annually over the period 1984-2016 at 30 m resolution and provided an extension to 2023. Statewide, forested land had an average annual net above ground carbon sink of 1.37 TgC yr-1, comparable to prior estimates. However, unlike the prior estimates, there was considerable variation around this mean. The statewide net above ground flux ranged interannually from -0.65 - 2.77 Tg C yr-1. At the county scale, the average annual net above ground flux ranged spatially from 0.01 - 0.13 Tg C yr-1 and spatiotemporally from -0.43 - 0.24 Tg C yr -1. Attribution analyses indicate the primary importance of persistent and regrowing forests, vegetation structure, local disturbance, and rising CO2 to the mean flux, and the primary importance of weather to the large-scale interannual variability. These results have important implications for state climate mitigation planning, reporting and assessment. With this approach, it is now possible to monitor changes in forest carbon stocks spatiotemporally over policy relevant domains with a consistent framework that is also enabled for future planning.

Subseasonal-to-seasonal (S2S) prediction of atmospheric rivers in the Northern Winter

Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.

Fish distribution shifts due to climate change in the Northeast Atlantic: Using a hierarchical filtering approach on marine-estuarine opportunist species

Marine-estuarine opportunist (MEO) species are fish that occur in the continental shelf and use estuaries and/or shallow coastal areas as nurseries. These commercially important resources are facing significant environmental modifications caused by direct and/or indirect anthropogenic climate change effects. In this study, we investigated the directionality and the magnitude of the distribution shifts (i.e., range size, gravity centroids, and margins) in marine environment suitability for six main MEO fish species within the Northeast Atlantic expected for the end of the 21st century. In the framework of this study, we have distinguished ‘sub-boreal’ from ‘sub-tropical’ species. The ‘hierarchical filters’ concept was adopted for modelling the potential species distributions and combined the predictions of i) a bioclimatic model with ii) a habitat model. The bioclimatic model is based on large-scale and time-variant variables while variables of the habitat model are fine-grained and time-invariant. Two Intergovernmental Panel on Climate Change (IPCC) scenarios are tested: an intermediate (SSP2-4.5) and a pessimistic one (SSP5-8.5). We applied this framework using international databases of biodiversity occurrences, ensemble forecasting producing consensual predictions, and innovative indices of distribution shifts. A visible north-westward shift was predicted for all six species in our study area. However, the northward expansion was greater for ‘sub-tropical’ than for ‘sub-boreal’ species due to faster gravity centroid displacement shifts and faster margins shifts. These range shifts may lead to major ecological impacts (e.g., changes in recruitment to estuarine and coastal nurseries, as well as changes in spawning grounds) that may alter populations' connectivity.

Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99–100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.

Precipitation variability and environmental change across late Quaternary glacial-interglacial cycles in lowland Central America: Insights from Lake Petén Itzá (Guatemala) sediments

Lowland Central America, a biodiversity hotspot in the northern Neotropics, is a region where the climate is influenced by the location and expansion-contraction of the Intertropical Convergence Zone (ITCZ) on seasonal to millennial timescales. Paleo-records from the Caribbean Sea and the eastern equatorial and subtropical Pacific Ocean illustrate the response of regional precipitation to fluctuations in global temperature, driven by glacial-interglacial cyclicity over the past 500 kyr. Here, we present a paleoclimate and paleoenvironment record from Lake Petén Itzá, lowland Guatemala, which spans the last ∼413 kyr. Sediment in the lake recorded lacustrine and terrestrial ecosystem responses to large-scale climate variability. Precipitation patterns during MIS11-9 (∼413-304 ka BP) align with the latitudinal position of the ITCZ, with superimposed effects from the strength of the Caribbean Low-Level Jet (CLLJ). A sediment hiatus, likely attributable to mass removal processes in the lake's shallower areas, spans the period from MIS8 (starting at 304 ka BP) to the end of MIS6 (at 149 ka BP). MIS6 was characterized by humid conditions, perhaps ascribable to a more southerly extension of cold fronts and intensification of the CLLJ. During MIS5, pronounced fluctuations among all sediment variables, accompanied by an abrupt decline in precipitation, may correspond with cold events inferred from North Atlantic Ocean sediment cores. Although discontinuous, the Lake Petén Itzá sediment record provides a window into late Quaternary climate and environmental change in lowland Central America.

Scale-dependent inflation algorithms for ensemble Kalman filters

Abstract Ensemble-based data assimilation methods often suffer sampling errors due to limited ensemble sizes and model errors, which can result in filter divergence. One method to avoid filter divergence is inflation, which inflates ensemble perturbations to increase ensemble spread and account for model errors. The commonly applied inflation methods, including the multiplicative inflation, relax to prior spread (RTPS), and additive inflation, often use a constant inflation parameter. To capture different error growths at different scales, a scale-dependent inflation is proposed here, which applies different inflation magnitudes for variables associated with different scales. Results from the two-scale Lorenz05 model III show that for the ensemble square-root filter (EnSRF) and integrated hybrid ensemble Kalman filter with ensemble mean updated by hybrid background error covariances (IHEnKF-Mean), scale-dependent inflation is superior to constant inflation. Constant inflation overinflates small-scale variables and results in increased small-scale errors, which then propagate to large-scale variables through the coupling between large- and small-scale variables and lead to increased large-scale errors. Scale-dependent inflation applies larger inflation for large-scale variables and imposes no inflation for small-scale variables, since large-scale errors have larger magnitudes than small-scale ones, and small-scale errors grow faster than large-scale ones. But IHEnKF-Ensemble that updates both the ensemble mean and perturbations with hybrid background error covariances is much less sensitive to scale-dependent inflation, compared to EnSRF and IHEnKF-Mean, because updating ensemble perturbations with hybrid background error covariances can play a role similar to the scale-dependent inflation.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model

Deep-learning-based convection schemes have garnered significant attention for their notable improvements in simulating precipitation distribution and tropical convection in Earth system models. However, these schemes struggle to capture the stochastic nature of moist physics, which can degrade the simulation of large-scale circulations, climate means, and variability. To address this issue, a stochastic parameterization scheme called DIFF-MP, based on a probabilistic diffusion model, is developed. Cloud-resolving data are coarse-grained into resolved-scale variables and subgrid contributions, which serve as conditional inputs and outputs for DIFF-MP. The performance of DIFF-MP is compared with that of generative adversarial networks and variational autoencoders. The results demonstrate that DIFF-MP consistently outperforms these models in terms of prediction error, coverage ratio, and spread–skill correlation. Furthermore, the standard deviation, skewness, and kurtosis of the subgrid contributions generated by DIFF-MP more closely match the test data than those produced by the other models. Interpretability experiments confirm that DIFF-MP’s parameterization of moist physics is physically consistent.

Multi-Objective Hybrid Algorithm Integrating Gradient Search and Evolutionary Mechanisms

The current multi-objective evolutionary algorithm (MOEA) has attracted much attention because of its good global exploration ability, but its local search ability near the optimal value is relatively weak, and for optimization prob lems with large-scale decision variables, the number of populations and iterations required by MOEA are very large, so the optimization efficiency is low. Gradient-based optimization algorithms can overcome these problems well, but they are difficult to be applied to multi-objective problems (MOPs). Therefore, this paper introduced random weight function on the basis of weighted average gradient, developed multi-objective gradient operator, and combined it with non-dominated genetic algorithm based on reference points (NSGA- III) proposed by Deb in 2013 to develop multi-objective optimization algorithm (MOGBA) and multi-objective Hybrid Evolutionary algorithm (HMOEA). The latter greatly enhances the local search capability while retaining the good global exploration capability of NSGA-III. Numerical experiments show that HMOEA has excellent capture capability for various Pareto formations, and the efficiency is improved by times compared with typical multi-objective algorithms. And further HMOEA is applied to the multi-objective aerodynamic optimization problem of the RAE2822 airfoil, and the ideal Pareto front is obtained, indicating that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design.

A simple model linking radiative–convective instability, convective aggregation and large-scale dynamics

Abstract. A simple model is presented which is designed to analyse the relation between the phenomenon of convective aggregation at small scales and larger-scale variability that results from coupling between dynamics and moisture in the tropical atmosphere. The model is based on single-layer dynamical equations coupled to a moisture equation to represent the dynamical effects of latent heating and radiative heating. The moisture variable q evolves through the effect of horizontal convergence, nonlinear horizontal advection and diffusion. Following previous work, the coupling between moisture and dynamics is included in such a way that a horizontally homogeneous state may be unstable to inhomogeneous disturbances, and, as a result, localised regions evolve towards either dry or moist states, with divergence or convergence respectively in the horizontal flow. The time evolution of the spatial structure of the dry and moist regions is investigated using a combination of theory and numerical simulation. One aspect of the evolution is a spatial coarsening that, if moist regions and dry regions are interpreted as convecting and non-convecting respectively, represents a form of convective aggregation. When the weak temperature gradient (WTG) approximation (i.e. a local balance between heating and convergence) applies, and horizontal advection is neglected, the system reduces to a nonlinear reaction–diffusion equation for q, and the coarsening is a well-known aspect of such systems. When nonlinear advection of moisture is included, the large-scale flow that arises from the spatial pattern of divergence and convergence leads to a distinctly different coarsening process. When thermal and frictional damping and f-plane rotation are included in the dynamics, there is a dynamical length scale Ldyn that sets an upper limit for the spatial coarsening of the moist and dry regions. The f-plane results provide a basis for interpreting the behaviour of the system on an equatorial β plane, where the dynamics implies a displacement in the zonal direction of the divergence relative to q and hence to coherent equatorially confined zonally propagating disturbances, comprising separate moist and dry regions. In many cases the propagation speed and direction depend on the equatorial wave response to the moist heating, with the relative strength of the Rossby wave response to the Kelvin wave response determining whether the propagation is eastward or westward. Within this model, the key overall properties of the propagating disturbances, the spatial scale and the phase speed, depend on nonlinearity in the coupling between moisture and dynamics, and any linear theory for such disturbances therefore has limited usefulness. The model described here, in which the moisture and dynamical fields vary in two spatial dimensions and important aspects of nonlinearity are captured, provides an intermediate model between theoretical models based on linearisation and one spatial dimension and general circulation models (GCMs) or convection-resolving models.

An ensemble approach to downscale climatological variables via screening of clustered ANNs

ABSTRACT The statistical downscaling of global circulation models presents a significant challenge in selecting appropriate input variables from a vast pool of predictors. To address the issue, we developed ensemble approach based on the Combining Multiple Clusters via Similarity Graph (COMUSA), which integrates k-means and self-organizing maps (SOMs) methods with the mutual information (MI)-random sampling approach. This innovative feature extraction technique demonstrated a 21% improvement in the classification efficacy of large-scale climatic variables. When comparing feature extraction methods, the combination of MI-random sampling and ensemble clustering yielded more accurate results than SOM clustering alone. The most efficient artificial neural networks (ANNs)-based downscaling model was employed to project near- and mid-future precipitation and temperature (2025–2035, 2035–2045), revealing varied outcomes under different scenarios (SSP3-7.0 and SSP5-8.5). Under SSP3-7.0 and SSP5-8.5, annual mean precipitation values are projected to decrease by 2–3 and by 4–5%. Also, projected annual mean temperature values indicate an increase in 21-27 and 29-35% under SSP3-7.0 and SSP5-8.5 scenarios. Integrating COMUSA ensemble clustering with MI-random sampling enhances the estimation accuracy of the ANN downscaling model, contributing to accurate projections of future precipitation and temperature values.

Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin.

Effects of Turbulence on Upper-Tropospheric Ice Supersaturation

Abstract Effects of turbulence on ice supersaturation at cirrus heights (>8 km) remain unexplored. Small-scale mixing processes become important for high Reynolds number flows, which may develop below the buoyancy length scale (10–100 m). The current study couples a stochastic turbulent mixing model with reduced dimensionality to an entraining parcel model to investigate, in large-ensemble simulations, how supersaturation evolves due to homogeneous turbulence in the stably stratified, cloud-free upper troposphere. The rising parcel is forced by a mesoscale updraft. The perturbation of an initially homogeneous vertical distribution of supersaturation is studied after a 36-m ascent in a baseline case and several sensitivity scenarios. Turbulent mixing and associated temperature fluctuations alone lead to changes in ensemble-mean distributions with standard deviations in the range 0.001–0.006, while mean values are hardly affected. Large case-to-case variability in the supersaturation field is predicted with fluctuation amplitudes of up to 0.03, although such large values are rare. A vertical gradient of supersaturation (≈10−3 m−1) is generated for high turbulence intensities due to the development of a dry-adiabatic lapse rate. Entrainment of slightly warmer (less than 0.1 K) environmental air into the parcel decreases the mean supersaturation by less than 0.01. Supersaturation fluctuations are substantially larger after entrainment events with an additional small offset in absolute humidity (by ±3.5%) between the parcel and environmental air. The predicted perturbations of ice supersaturation are significant enough to motivate studies of turbulence–ice nucleation interactions during cirrus formation that abandon the assumption of instantaneous mixing inherent to traditional parcel models. Significance Statement The purpose of our study is to investigate the effects of microscale turbulence on ice supersaturation in the upper troposphere. The associated variability in temperature and moisture fields is not resolved in cloud models and cannot easily be represented in terms of large-scale flow variables. We specify the conditions in which turbulent mixing and entrainment cause substantial variations in distributions of supersaturation. These include high turbulence intensity, strong atmospheric stability, and large moisture gradients. Our results suggest that turbulence may affect the strongly supersaturation-dependent ice formation processes in high-altitude clouds, pointing to the need to investigate cirrus formation in the presence of turbulence.

Tree growth history, legacy of large-scale forest management and climatic variability in North Finland

Tree growth history was investigated in North Finland to study the recently observed reduction in Scots pine growth. Tree-ring records from the National Forest Inventory (NFI) were compared to changes that have occurred in forest management activities and to variations in the North Atlantic Oscillation (NAO), as similar comparisons have not been previously made with NFI data. A phase of increased growth was dated to the early 1970s when the forest management activities were intensified on a large scale. More recently, growth has notably declined since the early 2000s which coincides with reduced activity to clean the ditches to ensure they are draining the substrate adequately. The growth reduction appeared greater for trees from stands with higher basal area. Trees representing sites where the first cleaning of the stand (thinning) had been carried out did not show reduced growth. NAO indices correlated positively with growth, especially during the cold season. An interpretation on the value of snow conditions in a dynamic interaction with management history/stand density is put forth to explain the growth decline observed in NFI data over the past two decades.

Vegetation Cover Change in the Loess Plateau: Patterns, Driving Factors, and Impacts

This paper reviews the changes in vegetation cover in the Loess Plateau, focusing on patterns, driving factors, and impacts. Over recent decades, significant improvements in vegetation cover have been observed, primarily due to large-scale ecological restoration projects and natural climate variability. Key drivers include precipitation, temperature, land use changes, and human activities such as reforestation. These changes have important ecological, socio-economic, and hydrological implications, including reduced soil erosion, improved biodiversity, and potential trade-offs with water availability. Understanding these dynamics is crucial for sustainable land management and ecological conservation in the region.

Increasing drought frequency in the central Zagros Mountains of western Iran over the past two centuries

The Zagros region in western Iran has experienced prolonged drought, significantly impacting water resource and forest ecosystems over the past few centuries. Understanding historical prolonged drought is crucial for making precise forecasts of shifts in regional drought in the Zagros region. Due to the lack of comprehensive historical records, the use of proxy records is considered a valuable tool for reconstructing past drought variations. We aimed to construct a tree-ring chronology of Juniper (Juniperus polycarpus) in the central Zagros region to comprehend its growth response to climate variables. We cored 25 J. polycarpus trees from the Keygooran forest reserve in western Iran and developed the tree ring chronology (1802–2022) using dplR. The relationships between tree growth and climate variables of monthly mean temperature, precipitation, PDSI (Palmer Drought Severity Index), and SPEI (Standardized Precipitation Evapotranspiration Index) were examined. The analysis revealed a strong positive correlation between tree growth and SPEI March-September of 1990–2018 on a 48-month timescale with a 2-year lag (r = 0.61, p < 0.05) which was included in the transfer model. Consequently, the SPEI-48 March-September was reconstructed for the period 1802–2022. On this reconstruction, several extremely dry years in 1832, 1867, and 1876 and extremely wet years in 1807, 1814, 1838, 1850, 1907, and 1908 years were identified, some of them aligning with historical records in Iran. Furthermore, there was a relationship between SPEI-48 March-September and teleconnection events suggesting that certain drought occurrences in the area were associated with the El Niño-Southern Oscillation (ENSO). Consequently, this reconstruction can serve as a reliable proxy for large-scale drought variability in the Zagros region of western Iran. Moreover, our research underscores the utility of SPEI in reconstructing the history of semi-arid climate conditions.

Large-scale variability in style and magnitude of footwall rift-related unconformities, Northern Carnarvon Basin, offshore NW Australia

Fault-scarp degradation complexes record rift-related erosion of normal fault scarps. At the scale of an individual fault segment, the magnitude and distribution of erosion are related to the total and along-strike variability in fault throw. However, previous studies on degradation complexes focused on one type of rift-related erosional unconformity, with the factors controlling the development, magnitude, and style of footwall erosion at a far larger scale (i.e., several fault blocks) being poorly constrained. This study uses high-resolution subsurface data to constrain the timing, magnitude, and style of footwall erosion across the NW Shelf of Australia. By doing so, we test conceptual and numerical model predictions for rift-related unconformities development. We show that footwall erosion complexes development is dependent on the occurrence and duration of footwall exposure and that fault throw and footwall uplift control the magnitude of erosion. Furthermore, the style of footwall erosion depends on the driving process(es), with gravitational instabilities resulting in cuspate footwall crest erosional surfaces, whereas peneplain-style surfaces develop in response to wave-driven erosion dictated by the base level. The results of this study can help refine rift-related faults evolution and the effects of sea level changes in tectono-stratigraphic models of rift basins.

New Parameters of the Conjugate Gradient Method to Solve Nonlinear Systems of Equations

The conjugated gradient methods can solve smooth functions with large-scale variables in the specified number of iterations for that they are highly important methods compared to concerning other iterative methods. In this paper, we propose two new conjugate gradient methods, namely the PMDL-1 and PMDL-2. However, for non-smooth functions, which are called conjugate gradient-free derivative methods depending on the projection technique. The two methods give great results compared to the basic PDL method. Moreover, we provide theorems that prove the global convergence between these two methods.

Confronting the United Nations’ Pro-growth Agenda

In this article, we enjoin the United Nations (UN) to forge a path out of our plight of multiple environmental and social crises. With other analysts, we identify ‘overshoot’ – the state in which humanity has substantially outpaced Earth’s capacity to regenerate its natural systems and to absorb our waste output – as the root cause of the existential threats we face. This dangerous condition demands rethinking our relationship with Earth and embarking on scaling down the human enterprise within policy frameworks of equity and rights. We argue that when the UN first articulated its international unity and prosperity mission, it did so within a ‘growth’ paradigm that treats Earth and its nonhuman inhabitants as mere resources at humanity’s disposal. The 1994 Cairo Conference on Population and Development reinforced this agenda, with its sharp turn away from the earlier emphasis on population concerns and their link to environmental protection. Today, it is clear that the UN’s foundational goals of peace, human rights and sustainability flounder within a growth-driven framework of human exceptionalism and nature domination. To correct course and reverse our advanced state of ecological overshoot we urge the UN to lead in contracting the large-scale variables of the human enterprise – population, economy, technosphere – and to resist co-optation by political, ideological and special interest pressures that would derail this mandate.

Dynamic variable analysis guided adaptive evolutionary multi-objective scheduling for large-scale workflows in cloud computing

Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm, namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential resources, thereby alleviating the negative impact of redundant cloud resources on evolutionary search efficiency. Then, a variable analysis strategy is designed to dynamically measure the contribution of each decision variable in pushing the population towards Pareto-optimal fronts. Moreover, AESA embraces an adaptive strategy to reward more evolutionary opportunities for decision variables with higher contributions to handle large-scale decision variables in a targeted manner, further improving the efficiency of evolutionary search. Finally, extensive experiments are performed based on real-world cloud platforms and workflow traces to verify the effectiveness of the proposed AESA. The comparison results validate its superior performance by significantly outperforming five representative baselines in optimizing makespan and energy consumption. Also, the results of ablation experiments demonstrate that all three components contribute to AESA’s overall performance, with the adaptive reward mechanism being the most significant.