Multiple Model Simulations Research Articles

Impacts of Massive Topographies on Heat Waves in Global Drylands

AbstractLarge‐scale topographies affect global extreme weather and climate though dynamic and thermal forcing. However, the impacts of typical topographies on heat waves in different drylands globally remain unclear. In this study, we find that heat waves are mainly occurred in global drylands during 1940–2022. The frequencies and intensities of heat waves in global drylands have significantly increased after 1980s. Multiple numerical model simulations reveal that the impact range of Asian topography on heat waves in global drylands is widest, not only in East Asia, Central, and west Asia locally, but also can reach as far as North Africa and North America. While the impact ranges of topographies in Africa, Arabian Peninsula, North America, and South America on heat waves in drylands are relatively narrow, which are concentrated in localized and their surroundings. These conclusions could provide clues to understand the influence of topography on global weather and climate extremes.

Enhanced atmospheric oxidation toward carbon neutrality reduces methane’s climate forcing

The hydroxyl radical (OH), as the central atmospheric oxidant, controls the removal rates of methane, a powerful greenhouse gas. It is being suggested that OH levels would decrease with reductions of nitrogen oxides and ozone levels by climate polices, but this remains unsettled. Here, we show that driven by the carbon neutrality pledge, the global-mean OH concentration, derived from multiple chemistry-climate model simulations, is projected to be significantly increasing with a trend of 0.071‒0.16% per year during 2015–2100. The leading cause of this OH enhancement is dramatic decreases in carbon monoxide and methane concentrations, which together reduce OH sinks. The OH increase shortens methane’s lifetime by 0.19‒1.1 years across models and subsequently diminishes methane’s radiative forcing. If following a largely unmitigated scenario, the global OH exhibits a significant decrease that would exacerbate methane’s radiative forcing. Thus, we highlight that targeted emission abatement strategies for sustained oxidation capacity can benefit climate change mitigation in the Anthropocene.

Significantly wetter or drier future conditions for one to two thirds of the world’s population

Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios. We show the hotspots of future drier and wetter conditions, including regions already experiencing water scarcity or excess. These patterns are projected to impact a significant portion of the global population, with approximately 3 billion people (38% of the world’s current population) affected under an intermediate emissions scenario and 5 billion people (66% of the world population) under a high emissions scenario by the century’s end (or 35-61% using projections of future population). We undertake a country- and state-level analysis quantifying the population exposed to significant changes in precipitation regimes, offering a robust framework for assessing multiple climate projections.

Strengthened ENSO Amplitude Contributed to Regime Shift in the Hadley Circulation

AbstractUnderstanding the variability in the Hadley circulation (HC) changes is crucial for understanding ocean‐atmosphere interactions. In this study, the variability in the boreal winter HC in the last 4 decades is explored using multiple reanalyzes and model simulations. The results show that regime shift occurred in the leading mode of HC variability. The primary mode of the recent HC is dominated by an equatorially symmetrical pattern, which was considered the second mode in previous studies. The regime shift in HC variability is mainly due to the El Niño–Southern Oscillation (ENSO), which explains both the spatiotemporal variation and formation of HC variability. Moreover, the abilities of the models to reproduce HC variability is subject to their ability to simulate ENSO variability, suggesting that the ENSO has become a more important modulator of the HC variability in recent decades, and additional research is warranted to evaluate future climate changes and potential effects on the HC.

Changes in Stratosphere‐Troposphere Exchange of Air Mass and Ozone Concentration in CCMI Models From 1960 to 2099

AbstractThis study investigates changes in stratosphere‐troposphere exchange (STE) of air masses and ozone concentrations from 1960 to 2099 using multiple model simulations from Chemistry Climate Model Initiative (CCMI) under climate change scenario RCP6.0. We employ a lowermost stratosphere mass budget approach with dynamic isentropic surfaces fitted to the tropical tropopause as the upper boundary of lowermost stratosphere. The multi‐model mean (MMM) trends of air mass STEs are all small over all regions, which are within 0.3 (0.1) % decade−1 for 1960–2000 (2000–2099). The MMM trends of ozone STE for 1960–2000 are 0.3%, −2.7%, 3.4%, −0.9%, and −2.7% decade−1 over the Northern hemisphere (NH) extratropics, Southern hemisphere (SH) extratropics, tropics, extratropics, and globe, respectively. The corresponding ozone STE trends for 2000–2099 are 3.0%, 4.3%, 0.8%, 3.5%, and 4.7% decade−1. Changes in ozone STEs are dominated by ozone concentration changes, driven by climate‐induced changes and ozone‐depleting substance (ODS) changes. For 1960–2000, small changes in ozone STEs in the NH extratropics are due to a cancellation between effects of climate‐induced changes and ODS increases, while the ODS effect dominates in the SH extratropics, leading to a large ozone STE magnitude decrease. Increased ozone transport from tropical troposphere to stratosphere for 1960–2000 is due to increased tropospheric ozone. A decreased global ozone STE magnitude for 1960–2000 was largely caused by ODS‐induced ozone loss that is partly compensated by climate‐induced ozone changes. For 2000–2099, about two‐thirds of global ozone STE magnitude increases are caused by ozone increases in the extratropical lower stratosphere due to climate‐induced changes. The remaining one‐third is caused by ozone recovery due to the phaseout of ODS.

IoT Agricultural Pest Identification Based on Multiple Convolutional Models

<p>This topic focuses on the corresponding research and simulation of multiple convolutional models for the detection methods of leaf pests and disease identification. Currently, crop pest identification in China mainly relies on field observation by farmers or experts, which is less accurate, time-consuming and extremely expensive, and not feasible for millions of small and medium-sized farms. To improve the recognition accuracy, crop pest recognition is performed by a convolutional neural network (CNN) after combining the plant leaf collection dataset, which has the features of automatic image feature extraction, strong generalization ability, and high recognition rate, and combined with the advantage of similarity by transfer learning, a crop pest recognition algorithm based on the comparison of multiple convolutional neural networks is implemented. After comparison experiments, the algorithm has 99.8% accuracy in the test set and can accurately distinguish seven health states of apples and grapes. This algorithm can help agricultural workers to conduct agricultural activities more scientifically, which is important for improving crop yield and agricultural intelligence.</p> <p> </p>

Multiple seepage model and gas production simulation of shale with power law fracture distribution

Changes in the internal microstructure of shale reservoirs have a significant effect on gas production. In this research, based on the double power law model and the triple medium theory, a triple medium model of shale multi-scale power law fracture distribution is constructed. The accuracy of the model is verified by comparing with the actual test results. Then, the model is used to analyze the relationship between shale stress, permeability, and three kinds of pore media as well as the influence of power law parameters and external force on reservoir microstructure and macroscopic gas production. The findings indicate that (1) the microstructure of shale reservoirs is affected by power law parameters and external forces; (2) gas exchange can cause the evolution of reservoir structure; and (3) the stress change of a single system will cause stress rearrangement of the whole system. This model provides a basic and comprehensive understanding for considering the various transport mechanisms of shale matrix and the microstructure change law of gas production process and proposes a new method for optimizing shale permeability simulation.

Construction and Simulation Analysis of Epidemic Propagation Model Based on COVID-19 Characteristics.

This paper proposes the epidemic propagation model SEAIHR to elucidate the propagation mechanism of the Corona Virus Disease of 2019 (COVID-19). Based on the analysis of the propagation characteristics of COVID-19, the hospitalization isolation state and recessive healing state are introduced. The home morbidity state is introduced to consider the self-healing of asymptomatic infected populations, the early isolation of close contractors, and the impact of epidemic prevention and control measures. In this paper, by using the real epidemic data combined with the changes in parameters in different epidemic stages, multiple model simulation comparative tests were conducted. The experimental results showed that the fitting and prediction accuracy of the SEAIHR model was significantly better than the classical epidemic propagation model, and the fitting error was 34.4-72.8% lower than that of the classical model in the early and middle stages of the epidemic.

Greenhouse Gas Emissions Drive Global Dryland Expansion but Not Spatial Patterns of Change in Aridification

Abstract Drylands play an essential role in Earth’s environment and human systems. Although dryland expansion has been widely investigated in previous studies, there is a lack of quantitative evidence supporting human-induced changes in dryland extent. Here, using multiple observational datasets and model simulations from phase 6 of the Coupled Model Intercomparison Project, we employ both correlation-based and optimal fingerprinting approaches to conduct quantitative detection and attribution of dryland expansion. Our results show that spatial changes in atmospheric aridity (i.e., the aridity index defined by the ratio of precipitation to potential evapotranspiration) between the recent period 1990–2014 and the past period 1950–74 are unlikely to have been caused by greenhouse gas (GHG) emissions. However, it is very likely (at least 95% confidence level) that dryland expansion at the global scale was driven principally by GHG emissions. Over the period 1950–2014, global drylands expanded by 3.67% according to observations, and the dryland expansion attributed to GHG emissions is estimated as ∼4.5%. Drylands are projected to continue expanding, and their populations to increase until global warming reaches ∼3.5°C above preindustrial temperature under the middle- and high-emission scenarios. If warming exceeds ∼3.5°C, a reduction in population density would drive a decrease in dryland population. Our results for the first time provide quantitative evidence for the dominant effects of GHG emissions on global dryland expansion, which is helpful for anthropogenic climate change adaptation in drylands. Significance Statement In the past decades, global drylands have been reported to show changes in space and time, based on atmospheric aridity (i.e., aridity index defined by the ratio of precipitation to potential evapotranspiration). Using two detection and attribution methods, the spatial change patterns of atmospheric aridity between 1990–2014 and 1950–74 are unlikely to be driven by greenhouse gas (GHG) emissions, whereas the temporal expansion of global drylands (i.e., 3.67% from 1950 to 2014) is principally attributed to GHG emissions (contribution: ∼122%). Quantitative evidence from the detection and attribution analysis supports the dominant role of greenhouse gas emissions in global dryland expansion, which will increase the population suffering from water shortages under future warming unless climate adaptation is adopted.

A comprehensive model to quantify the effects of additional nano-particles on the printability in laser powder bed fusion of aluminum alloy and composite

At present, it is recognized that nanoparticles can improve the printability of many metals in fusion additive manufacturing, obtain finer microstructures and higher mechanical properties. However, it is unclear how nanoparticles affect the printing process and the extent of the effect. Numerical simulation can realize such a quantitative study. Here, we developed a method to quantify the effects of nano-particles on the printability in laser powder bed fusion with multiple physics simulation model, with the consideration of surface tension, Marangoni effect, phase transition and recoil force via volume of fluid method. The influence of nanoparticles as well as thermal properties on the molten pool morphology, temperature distribution and cooling rate were studied. The results showed that TiB2/AlSi10Mg possessed higher cooling rate and larger melt pool depth, which is consistent with single track experimental result. The G-R solidification map indicated that for the specific points in the mush zone, percentages of them in equiaxed, mixed and columnar region for TiB2/ AlSi10Mg are 14.29%, 76.19% and 9.52%, respectively, revealing that TiB2 can lead to the transition from the columnar region to the equiaxed one, which was also confirmed by the experimental EBSD map. The study on thermophysical properties indicated that thermal conductivity had the largest influence on the characteristic of molten pool, which can be used as a strategy to optimize the quality in practical application. By introducing the printability factor F, we determined that the printability of AlSi10Mg and different particles decorated AlSi10Mg is in the following order: TiB2/AlSi10Mg > TiC/AlSi10Mg > SiC/AlSi10Mg > AlSi10Mg. This work provides a theoretical framework to predict the printability and efficiently screen for promising metal materials with good printability.

Evaluation of Soil Moisture in CMIP6 Multimodel Simulations Over Conterminous China

AbstractThe Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides the long‐term soil moisture (SM) products and this study conducts a comprehensive assessment of SM products of multiple CMIP6 model simulations over conterminous China. Both near‐surface (0–10 cm) SM simulations from 40 models and root‐zone (0–100 cm) SM from 25 models are compared with a set of in situ and a satellite‐based data set in the growing season for 1992–2013. The long‐term trend and interannual variability of near‐surface SM for 1961–2014 are further evaluated with an offline land surface modeling data set. Simulations from most models broadly capture the spatial characteristics of observation, and the multimodel mean (MME) well reproduces seasonal variations over the majority of regions, regardless of the large spread across models. SM from CMIP6 models from the same institution likely manifests similar performances, which indicates that the land‐surface scheme plays a dominant role in the CMIP6 SM reproduction. The majority of CMIP6 models well simulate the overall drying trend in China as a whole and the signs of a SM trend are highly consistent across different models, but the areas with significant wetting/drying trends vary with models. The spatial patterns of SM interannual variability are model‐dependent. MME is overall superior to the simulation of individual model and may have potential applications in future research. The heterogeneity of SM performances across CMIP6 models reveals the complexity and uncertainty in modeling land surface variables, suggesting the need for improving representations of land surface processes in the coupled models.

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R2-score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(104) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(105) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈O(105)−O(107) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

Calendar effects on surface air temperature and precipitation based on model-ensemble equilibrium and transient simulations from PMIP4 and PACMEDY

Abstract. Numerical modeling enables a comprehensive understanding not only of the Earth's system today, but also of the past. To date, a significant amount of time and effort has been devoted to paleoclimate modeling and analysis, which involves the latest and most advanced Paleoclimate Modelling Intercomparison Project phase 4 (PMIP4). The definition of seasonality, which is influenced by slow variations in the Earth's orbital parameters, plays a key role in determining the calculated seasonal cycle of the climate. In contrast to the classical calendar used today, where the lengths of the months and seasons are fixed, the angular calendar calculates the lengths of the months and seasons according to a fixed number of degrees along the Earth's orbit. When comparing simulation results for different time intervals, it is essential to account for the angular calendar to ensure that the data for comparison are from the same position along the Earth's orbit. Most models use the classical calendar, which can lead to strong distortions of the monthly and seasonal values, especially for the climate of the past. Here, by analyzing daily outputs from multiple PMIP4 model simulations, we examine calendar effects on surface air temperature and precipitation under mid-Holocene, Last Interglacial, and pre-industrial climate conditions. We came to the following conclusions. (a) The largest cooling bias occurs in boreal autumn when the classical calendar is applied for the mid-Holocene and Last Interglacial, due to the fact that the vernal equinox is fixed on 21 March. (b) The sign of the temperature anomalies between the Last Interglacial and pre-industrial in boreal autumn can be reversed after the switch from the classical to angular calendar, particularly over the Northern Hemisphere continents. (c) Precipitation over West Africa is overestimated in boreal summer and underestimated in boreal autumn when the classical seasonal cycle is applied. (d) Finally, month-length adjusted values for surface air temperature and precipitation are very similar to the day-length adjusted values, and therefore correcting the calendar based on the monthly model results can largely reduce the artificial bias. In addition, we examine the calendar effects in three transient simulations for 6–0 ka by AWI-ESM, MPI-ESM, and IPSL-CM. We find significant discrepancies between adjusted and unadjusted temperature values over continents for both hemispheres in boreal autumn, while for other seasons the deviations are relatively small. A drying bias can be found in the summer monsoon precipitation in Africa (in the classical calendar), whereby the magnitude of bias becomes smaller over time. Overall, our study underlines the importance of the application of calendar transformation in the analysis of climate simulations. Neglecting the calendar effects could lead to a profound artificial distortion of the calculated seasonal cycle of surface air temperature and precipitation.

Impact of Proxies and Prior Estimates on Data Assimilation Using Isotope Ratios for the Climate Reconstruction of the Last Millennium

AbstractClimate reconstructions by data assimilation need to accommodate for sensitivities to proxies and prior estimates because models are uncertain and proxies are spatiotemporally limited. This study examines these sensitivities using multiple climate model simulations and different combinations of proxies (i.e., corals, ice cores, and tree‐ring cellulose). Experiments were conducted using an offline data assimilation approach; the results showed annual variations in the global distribution of surface air temperature and precipitation amount from 850 to 2000. Standard deviations of surface air temperature and precipitation amount during the entire period differed by up to 36% due to prior estimates. Experiments with different types of proxies showed that the El Niño‐like distribution of positive anomalies in the central to eastern tropical Pacific may only be adequately reproduced in experiments with corals, and not experiments without corals. The correlation coefficient of the NINO.3 index from 1971 to 2000 between experiments with corals and the Japanese 55‐year Reanalysis (JRA‐55) was 0.81 at maximum. By contrast, the correlation coefficient between experiments without corals and JRA‐55 was a maximum of 0.19.

The underappreciated role of anthropogenic sources in atmospheric soluble iron flux to the Southern Ocean

The atmospheric deposition of soluble (bioaccessible) iron enhances ocean primary productivity and subsequent atmospheric CO2 sequestration in iron-limited ocean basins, especially the Southern Ocean. While anthropogenic sources have been recently suggested to be important in some northern hemisphere oceans, the role in the Southern Ocean remains ambiguous. By comparing multiple model simulations with the new aircraft observations for anthropogenic iron, we show that anthropogenic soluble iron deposition flux to the Southern Ocean could be underestimated by more than a factor of ten in previous modeling estimates. Our improved estimate for the anthropogenic iron budget enhances its contribution on the soluble iron deposition in the Southern Ocean from about 10% to 60%, implying a dominant role of anthropogenic sources. We predict that anthropogenic soluble iron deposition in the Southern Ocean is reduced substantially (30‒90%) by the year 2100, and plays a major role in the future evolution of atmospheric soluble iron inputs to the Southern Ocean.

Spatio-Temporal Evolution, Prediction and Optimization of LUCC Based on CA-Markov and InVEST Models: A Case Study of Mentougou District, Beijing.

With the rapid advancement of urbanization and industrialization, the contradiction between the social economy and resources and the environment has become increasingly prominent. On the basis of limited land resources, the way to promote multi-objective comprehensive development such as economic, social development and ecological and environmental protection through structure and layout regulation, so as to maximize regional comprehensive benefits, is an important task of current land spatial planning. Our aim is to obtain land-use-change data in the study area using remote-sensing data inversion and multiple-model simulation. Based on land suitability evaluation, we predict and optimize the land use structure of the study area in 2030 and evaluate and compare ecosystem services. Based on remote-sensing images and eco-environmental data from 1985 to 2014 in the study area, land use/land cover change (LUCC) and future simulation data were obtained by using supervised classification, landscape metrics and the CA-Markov model. The ecosystem services were evaluated by the InVEST model. The analytic hierarchy process (AHP) method was used to evaluate the land suitability for LUCC. Finally, the LUCC in 2030 under two different scenarios, Scenario_1 (prediction) and Scenario_2 (optimization), were evaluated, and the ecosystem service functions were compared. In the last 30 years, the landscape in the study area has gradually fragmented, and the built-up land has expanded rapidly, increased by one-third, mainly at the cost of cropland, orchards and wasteland. According to the suitability evaluation, giving priority to the land use types with higher environmental requirements will ensure the study area has a higher ecosystem service value. The rapid development of urbanization has a far-reaching impact on regional LUCC. Intensive land resources need reasonable and scientific land use planning, and land use planning should be based on the suitability evaluation of land resources, which can improve the regional ecosystem service function.

Uncertainty quantification of large-scale dynamical systems using parametric model order reduction

The finite element method is a widely used tool for the discretization and modeling of complex engineering structures. The output quantities of analyses conducted with finite element models are given as sharp values. This misleads about the fact that model input parameters used to define the finite element model are usually only imprecisely known, either due to aleatory uncertainty and/or due to epistemic uncertainty. Therefore, a meaningful quantification and analysis of these polymorphic uncertainties is indispensable to obtain robust simulation results. Very often, an uncertainty quantification (UQ) scheme involves multiple simulation model evaluations where the imprecisely known model parameters are varied in order to rate the impact of the uncertain model parameters on the simulation results. However, the underlying systems of ordinary differential equations of industrial finite element models usually exhibit hundreds of thousands degrees of freedom making repeated model evaluations in dynamical analyses computationally infeasible. This article proposes a novel simulation workflow for the efficient UQ of large-scale dynamical systems. Its fundamental idea is to combine parametric modeling and parametric model order reduction in order to drastically reduce the number of degrees of freedom of the underlying systems of ordinary differential equations. This allows for a very efficient UQ of large-scale finite element models with almost no loss of accuracy. The workflow is successfully applied to an industrial finite element model of a helicopter airframe structure in a possibilistic UQ. In this contribution, numerical speedups by a factor of 1630 compared to a conventional simulation workflow can be reached.

Beneficial effects of warming on temperate tree carbon storage depend on precipitation and mycorrhizal types

Despite evidence from multiple observation data sets and numerical model simulations that interactions between biotic and abiotic factors control tree carbon (C) storage in the Northern Hemisphere, it remains unclear whether the effect of one factor will be altered by other factors. Here, we used forest inventory data consisting of more than 500,000 trees from 1910 plots to explore the relative importance of these drivers of plant C storage in northeast China. We found that tree C storage was significantly positively associated with mean annual temperature (MAT). After controlling for the role of mean annual precipitation (MAP), directionality in the tree C storage-MAT relationship reversed, indicating that the direction of MAT affecting tree C storage depends on MAP. Accounting for the effects of tree-fungal symbioses on plant resistance to drought and warming, we found that warming increased AM tree C storage even after controlling the role of MAP, but decreased EcM tree C storage after controlling the role of MAP. Our analysis also shows that species richness, especially the relative richness of AM tree species, had a significantly positive relationship with all types of tree C storage. Our findings have implications for improving temperate forest C sink and afforestation strategies: the increasing richness of AM trees has the potential to enhance the tree C sink and reduce the sensitivity of warming-induced tree growth benefits to changes in precipitation.

Using probabilistic solar power forecasts to inform flexible ramp product procurement for the California ISO

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $100 million per year for a CAISO-size system).

A pseudoproxy assessment of why climate field reconstruction methods perform the way they do in time and space

Abstract. Spatiotemporal paleoclimate reconstructions that seek to estimate climate conditions over the last several millennia are derived from multiple climate proxy records (e.g., tree rings, ice cores, corals, and cave formations) that are heterogeneously distributed across land and marine environments. Assessing the skill of the methods used for these reconstructions is critical as a means of understanding the spatiotemporal uncertainties in the derived reconstruction products. Traditional statistical measures of skill have been applied in past applications, but they often lack formal null hypotheses that incorporate the spatiotemporal characteristics of the fields and allow for formal significance testing. More recent attempts have developed assessment metrics to evaluate the difference of the characteristics between two spatiotemporal fields. We apply these assessment metrics to results from synthetic reconstruction experiments based on multiple climate model simulations to assess the skill of four reconstruction methods. We further interpret the comparisons using analysis of empirical orthogonal functions (EOFs) that represent the noise-filtered climate field. We demonstrate that the underlying features of a targeted temperature field that can affect the performance of CFRs include the following: (i) the characteristics of the eigenvalue spectrum, namely the amount of variance captured in the leading EOFs; (ii) the temporal stability of the leading EOFs; (iii) the representation of the climate over the sampling network with respect to the global climate; and (iv) the strength of spatial covariance, i.e., the dominance of teleconnections, in the targeted temperature field. The features of climate models and reconstruction methods identified in this paper demonstrate more detailed assessments of reconstruction methods and point to important areas of testing and improving real-world reconstruction methods.