Modes Of Variability Research Articles

The Impact of Interactive Ocean Dynamics on Atlantic Sea Surface Temperature Variability

Abstract The impact of interactive ocean dynamics on internal variations of Atlantic sea surface temperature (SST) is investigated by comparing preindustrial control simulations of a fully coupled atmosphere–ocean–ice model to the same atmosphere–ice model with the ocean replaced by a motionless slab layer (henceforth slab ocean model). Differences in SST variability between the two models are diagnosed by an optimization technique that finds components whose variance differs as much as possible. This technique reveals that Atlantic SST variability differs significantly between the two models. The two components with the most extreme enhancement of SST variance in the slab ocean model resemble the tripole SST pattern associated with the North Atlantic Oscillation (NAO) and the Atlantic multidecadal variability (AMV) pattern. This result supports previous claims that ocean dynamics are not necessary for the AMV, although ocean dynamics lead to slight increases in the memory of both the AMV and the NAO tripole. The component with the most extreme enhancement of SST variance in the fully coupled model resembles the Atlantic Niño pattern, confirming the ability of our technique to isolate physical modes known to require ocean dynamics. The second component with more variance in the fully coupled model is a mode of subpolar SST variability. Both the reemergence of SST anomalies and changes in ocean heat transport lead to increased SST variance and memory in the subpolar Atlantic. Despite large differences in the mean and variability of SST, atmospheric variability is quite similar between the two models, confirming that most atmospheric variability is generated by internal atmospheric dynamics.

Atmospheric teleconnections responsible for the dominant patterns of interannual variability in summer drought over northern Asia

Abstract Summer drought over northern Asia (NA) seriously threatens the local fragile ecological environment and social economy development. In this study, using the standardized precipitation evapotranspiration index (SPEI), we firstly identify the dominant modes of interannual variability in summer drought condition over NA, and then explore the atmospheric patterns responsible for the formation of the modes. The results show that the first empirical orthogonal function mode (EOF1) of summer SPEI over NA exhibits a meridional dipole pattern, which is influenced primarily by the Polar–Eurasian teleconnection (POL) and Circumglobal teleconnection (CGT) patterns. Under the influence of negative POL and positive CGT patterns, there is an anomalous anticyclone (cyclone) over northwestern Siberia (Lake Baikal to Northeast China). Such atmospheric circulations lead to meridional dipole patterns in air temperature, moisture condition, vertical motion, and cloud cover over NA, favoring decreased (increased) precipitation and increased (decreased) potential evapotranspiration over northern (southern) NA, finally contributing to the formation of EOF1. The EOF2 shows an approximate zonal dipole pattern, which is influenced by the British-Baikal Corridor (BBC) and Scandinavia teleconnection (SCA) patterns. The positive BBC and SCA patterns can lead to an anomalous anticyclone over the Ural Mountains and cyclone over the Lake Baikal. Such atmospheric circulations result in a zonal dipole pattern in precipitation and potential evapotranspiration over NA through changing the local moisture condition, air temperature, and radiation, consequently favoring the formation of EOF2. Fitting analysis indicates that the aforementioned atmospheric factors can explain 76% (55%) of the interannual variability of EOF1 (EOF2).

Visualising Daily PM10 Pollution in an Open-Cut Mining Valley of New South Wales, Australia—Part I: Identification of Spatial and Temporal Variation Patterns

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

Analysis of sea level variability and its contributions in the Bohai, Yellow Sea, and East China Sea

Understanding the sea level variability of the Bohai, Yellow Sea, and East China Sea (BYECS) is crucial for the socio-cultural and natural ecosystems of the coastal regions. In this study, based on satellite altimetry data, selected time range from 1993 to 2020, using the cyclostationary empirical orthogonal function (CSEOF) analysis method distinguishes the primary sea level variability modes. The analysis encompasses the seasonal signal, trend, and El Niño-Southern Oscillation (ENSO) associated mode of sea level anomaly. The amplitude of the annual cycle demonstrates a non-stationary signal, fluctuating between -15% and 15% from the average. Monsoons, atmospheric forcing, ocean circulation, wind-driven Ekman transport, and the Kuroshio emerge as the primary factors influencing BYECS variability on seasonal scales. The satellite altimetry sea level exhibits an average trend within the range of 3-4 mm/year, while the steric sea level trend is generally smaller, falling within the range of 0-2 mm/year. Throughout the entire period, the contribution of steric sea level to the mean sea level trend consistently remains below 25%. Furthermore, BYECS sea level variations have a sensitive response to strong El Niño years, with a clear regionalization of the response, which is related to the intricate atmospheric circulation and local wind pressures, as well as the influence of ocean circulation. In conclusion, we gained a more comprehensive understanding of sea level variability in the BYECS, especially the annual cycle of sea level amplitude and the response of ENSO. However, more studies still need to be done to differentiate the various factors in sea level variations.

Using Shelf‐Edge Transport Composition and Sensitivity Experiments to Understand Processes Driving Sea Level on the Northwest European Shelf

AbstractVariability in ocean currents, temperature and salinity drive dynamic sea level (DSL) variability on the Northwest European Shelf (NWES). It is dominated by mass variations, with steric signals relatively small. A mechanistic explanation of how ocean dynamics relates to the mass component of NWES sea level variability is required. We use regional ocean model experiments to isolate sources of variability and then investigate the effect on monthly to‐interannual DSL variability together with the simulated momentum budgets along the shelfbreak. Regional (local) wind and non‐regional (remote) forcing are important on the NWES. For the local wind forcing, the net mass flux onto the shelf, which drives a shelf‐mean mode of DSL variability, results from a combination of surface Ekman, bottom Ekman and geostrophic flows and explains 73% of the variance in transport across the shelf‐edge. The geostrophic flow is closely related to wind stress with a flow about half that of surface Ekman transport but in the opposite direction. For the remotely forced mass‐flux across the shelf‐edge, the geostrophic component explains 62% of the variance and bottom friction plays an important indirect role. The remotely forced variability, while relatively spatially uniform, is more important for explaining DSL variance over the western NWES. This mode of variability is sensitive to signals propagating northward via a thin strip of the southern boundary near the Portuguese coast, consistent with coastal trapped wave signals. It also appears to drive steric height in the Bay of Biscay, which is related to DSL on the shelf.

Comprehensive assessment of global spinal sagittal alignment and related normal spinal loads in a healthy population

Abnormal postoperative global sagittal alignment (GSA) is associated with an increased risk of mechanical complications after spinal surgery. Typical assessment of sagittal alignment relies on a few selected measures, disregarding global complexity and variability of the sagittal curvature. The normative range of spinal loads associated with GSA has not yet been considered in clinical evaluation. The study objectives were to develop a new GSA assessment method that holistically describes the inherent relationships within GSA and to estimate the related spinal loads.Vertebral endplates were annotated on radiographs of 85 non-pathological subjects. A Principal Component Analysis (PCA) was performed to derive a Statistical Shape Model (SSM). Associations between identified GSA variability modes and conventional alignment measures were assessed. Simulations of respective Shape Modes (SMs) were performed using an established musculoskeletal AnyBody model to estimate normal variation in cervico-thoraco-lumbar loads.The first six principal components explained 97.96 % of GSA variance. The SSM provides the normative range of GSA and a visual representation of the main variability modes. Normal variation relative to the population mean in identified alignment features was found to influence spinal loads, e.g. the lower bound of the second shape mode (SM2-2σ) corresponds to an increase in L4L5-compression by 378.64 N (67.86 %).Six unique alignment features were sufficient to describe GSA almost entirely, demonstrating the value of the proposed method for an objective and comprehensive analysis of GSA. The influence of these features on spinal loads provides a normative biomechanical reference, eventually guiding surgical planning of deformity correction in the future.

The Hybrid Recharge Delayed Oscillator: A More Realistic El Niño Conceptual Model

Abstract El Niño–Southern Oscillation (ENSO) is the leading mode of climate interannual variability, with large socioeconomical and environmental impacts, potentially increasing with climate change. Improving its understanding may shed further light on its predictability. Here we revisit the two main conceptual models for explaining ENSO cyclic nature, namely, the recharge oscillator (RO) and the advective–reflective delayed oscillator (DO). Some previous studies have argued that these two models capture similar physical processes. Yet, we show here that they actually capture two distinct roles of ocean wave dynamics in ENSO’s temperature tendency equation, using observations, reanalyses, and Climate Model Intercomparison Project (CMIP) models. The slow recharge/discharge process mostly influences central-eastern Pacific by favoring warmer equatorial undercurrent and equatorial upwelling, while the 6-month delayed advective–reflective feedback process dominates in the western-central Pacific. We thus propose a hybrid recharge delayed oscillator (RDO) that combines these two distinct processes into one conceptual model, more realistic than the RO or DO alone. The RDO eigenvalues (frequency and growth rate) are highly sensitive to the relative strengths of the recharge/discharge and delayed negative feedbacks, which have distinct dependencies to mean state. Combining these two feedbacks explains most of ENSO frequency diversity among models. Thanks to the two different spatial patterns involved, the RDO can even capture ENSO spatiotemporal diversity and complexity. We also develop a fully nonlinear and seasonal RDO, even more robust and realistic, investigating each nonlinear term. The great RDO sensitivity may explain the observed and simulated richness in ENSO’s characteristics and predictability. Significance Statement El Niño and La Niña events, and the related Southern Oscillation, cause the largest year-to-year variations of Earth’s climate. Yet the theories behind them are still debated, with two main conceptual models being the recharge oscillator and the delayed oscillator. Our purpose here is to address this debate by developing a more realistic theory, a hybrid recharge delayed oscillator. We show how simple yet realistic it is, with equivalent contributions from the slow recharge process and from the faster delayed feedback. It even captures the observed El Niño and La Niña diversity in space and in frequency. Future studies could use the simple theoretical framework provided here to investigate El Niño–Southern Oscillation (ENSO) in observations, theories, climate models diagnostics and forecasts, and global warming projections.

North Atlantic Ocean–Originated Multicentennial Oscillation of the AMOC: A Coupled Model Study

Abstract Using a CESM1 control simulation, we conduct a follow-up study to advance our earlier theoretical research on the multicentennial oscillation (MCO) of the Atlantic meridional overturning circulation (AMOC). The modeled AMOC MCO primarily arises from internal oceanic processes in the North Atlantic, potentially representing a North Atlantic Ocean–originated mode of AMOC multicentennial variability (MCV) in reality. Specifically, this AMOC MCO is mainly driven by salinity variation in the subpolar upper North Atlantic, which dominates local density variation. Salinity anomaly in the subpolar upper ocean is enhanced by the well-known positive salinity advection feedback that is realized through anomalous advection in the subtropical to subpolar upper ocean. Meanwhile, mean advection moves salinity anomaly in the subtropical intermediate ocean northward, weakening the subpolar upper salinity anomaly and leading to its phase change. The salinity anomalies have a clear three-dimensional life cycle around the North Atlantic. The mechanism and time scale of the modeled AMOC MCO are consistent with our earlier theoretical studies. In the theoretical model, artificially deactivating either the anomalous or mean advection in the AMOC upper branch prevents it from exhibiting AMOC MCO, underscoring the indispensability of both the anomalous and mean advections in this North Atlantic Ocean–originated AMOC MCO. In our coupled model simulation, the South Atlantic and Southern Oceans do not exhibit variabilities synchronous with the AMOC MCO; the Arctic Ocean’s contribution to the subpolar upper salinity anomaly is much weaker than the North Atlantic. Hence, this North Atlantic Ocean–originated AMOC MCO is distinct from the previously proposed Southern Ocean–originated and Arctic Ocean–originated AMOC MCOs.

Holocene Variability of the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation Inferred From Chinese Speleothem δ18O Records

AbstractThe current reconstructions of the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) primarily rely on marine sediment proxies. However, the limited resolution of these records makes it difficult to precisely understand the short‐term dynamics of the PDO and AMO, and consequently, their influence on global climate. This study unveils two new Holocene reconstructions of AMO and PDO derived from a large‐scale compilation of speleothem isotope records (δ18Os) from Chinese monsoon region, in which spatial patterns of summer rainfall are generally accepted as good indicators of internal variability. Principal component analysis applied to Chinese δ18Os records identifies the first principal component as representative of Holocene Asian monsoon (AM) variation. This data‐driven approach reveals a significant shift in hydroclimatic conditions after 8.2 ka, potentially providing valuable insights into the underlying forcing mechanisms driving these changes. The remanent components, which show spatial rainfall patterns, are effectively validated through independent records of AMO and PDO derived from the previous paleo‐proxy based reconstructions and model simulations. The present reconstructions suggest a significantly enhanced stability of the AMO and PDO in the middle Holocene, which may explain the concurrent more stable and optimum climate observed in the AM region. These findings also imply that the Holocene AM intensity recorded in Chinese cave records is largely controlled by external forcing, whereas the regionally heterogenous rainfall is regulated by internal variability. The successful attempt on the Holocene AMO and PDO reconstructions indicates an effective isolation between different modes of climate variability from paleoclimate records.

Sources of low-frequency variability in observed Antarctic sea ice

Abstract. Antarctic sea ice has exhibited significant variability over the satellite record, including a period of prolonged and gradual expansion, as well as a period of sudden decline. A number of mechanisms have been proposed to explain this variability, but how each mechanism manifests spatially and temporally remains poorly understood. Here, we use a statistical method called low-frequency component analysis to analyze the spatiotemporal structure of observed Antarctic sea ice concentration variability. The identified patterns reveal distinct modes of low-frequency sea ice variability. The leading mode, which accounts for the large-scale, gradual expansion of sea ice, is associated with the Interdecadal Pacific Oscillation and resembles the observed sea surface temperature trend pattern that climate models have trouble reproducing. The second mode is associated with the central Pacific El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode and accounts for most of the sea ice variability in the Ross Sea. The third mode is associated with the eastern Pacific ENSO and Amundsen Sea Low and accounts for most of the pan-Antarctic sea ice variability and almost all of the sea ice variability in the Weddell Sea. The third mode is also related to periods of abrupt Antarctic sea ice decline that are associated with a weakening of the circumpolar westerlies, which favors surface warming through a shoaling of the ocean mixed layer and decreased northward Ekman heat transport. Broadly, these results suggest that climate model biases in long-term Antarctic sea ice and large-scale sea surface temperature trends are related to each other and that eastern Pacific ENSO variability is a key ingredient for abrupt Antarctic sea ice changes.

Predicting carbon dioxide emissions in the United States of America using machine learning algorithms.

Carbon dioxide (CO2) emissions result from human activities like burning fossil fuels. CO2 is a greenhouse gas, contributing to global warming and climate change. Efforts to reduce CO2 emissions include transitioning to renewable energy. Monitoring and reducing CO2 emissions are crucial for mitigating climate change. Strategies include energy efficiency and renewable energy adoption. In the past few decades, several nations have experienced air pollution and environmental difficulties because of carbon dioxide (CO2) emissions. One of the most crucial methods for regulating and maximizing CO2 emission reductions is precise forecasting. Four machine learning algorithms with high forecasting precision and low data requirements were developed in this study to estimate CO2 emissions in the United States (US). Data from a dataset covering the years 1973/01 to 2022/07 that included information on different energy sources that had an impact on CO2 emissions were examined. Then, four algorithms performed the CO2 emissions forecast from the layer recurrent neural network with 10 nodes (L-RNN), a feed-forward neural network with 10 nodes (FFNN), a convolutional neural network with two layers with 10 and 5 filters (CNN1), and convolutional neural network with two layers and with 50 and 25 filters (CNN2) models. Each algorithm's forecast accuracy was assessed using eight indicators. The three preprocessing techniques used are (1) without any processing techniques, (2) processed using max-min normalization technique, and (3) processed using max-min normalization technique and decomposed by variation mode decomposition (VMD) technique with 7 intrinsic mode functions and 1000 iterations. The latter with L-RNN algorithm gave a high accuracy between the forecasting and actual values. The results of CO2 emissions from 2011/05 to 2022/07 have been forecasted, and the L-RNN algorithm had the highest forecast accuracy. The L-RNN model has the lowest value of 1.187028078, 135.5668592, and 11.64331822 for MAPE, MSE, and RMSE, respectively. The L-RNN model provides precise and timely forecasts that can help formulate plans to reduce carbon emissions and contribute to a more sustainable future. Moreover, the results of this investigation can improve our comprehension of the dynamics of carbon dioxide emissions, resulting in better-informed environmental policies and initiatives targeted at lowering carbon emissions.

Multi-Source Dataset Assessment and Variation Characteristics of Snow Depth in Eurasia from 1980 to 2018

Snow is an indicator of climate change. Its variation can affect surface energy, water balance, and atmospheric circulation, providing important feedback on climate change. There is a lack of assessment of the spatial characteristics of multi-source snow data in Eurasia, and these data exhibit high spatial variability and other differences. Therefore, using data obtained from the Global Historical Climatology Network Daily (GHCND) from 1980 to 2018, snow depth information from ERA5, MERRA2, and GlobSnow is assessed in this study. The spatiotemporal variation characteristics and the primary spatial modes of seasonal variations in snow depth are analyzed. The results show that the snow depth, according to GlobSnow data, is closer to that of the measured site data, while the ERA5_Land and MERRA2 data are overestimated. The annual variations in snow depth are consistent with seasonal variations in winter and spring, with an increasing trend in the mountains of Central Asia and Siberia and a decreasing trend in most of the rest of Eurasia. The dominant patterns of snow depth in late autumn, winter, and spring are all north–south dipole patterns, and there is overall consistency in summer.

Gottesman-Kitaev-Preskill Encoding in Continuous Modal Variables of Single Photons.

GKP states, introduced by Gottesman, Kitaev, and Preskill, are continuous variable logical qubits that can be corrected for errors caused by phase space displacements. Their experimental realization is challenging, in particular, using propagating fields, where quantum information is encoded in the quadratures of the electromagnetic field. However, traveling photons are essential in many applications of GKP codes involving the long-distance transmission of quantum information. We introduce a new method for encoding GKP states in propagating fields using single photons, each occupying a distinct auxiliary mode given by the propagation direction. The GKP states are defined as highly correlated states described by collective continuous modes, as time and frequency. We analyze how the error detection and correction protocol scales with the total photon number and the spectral width. We show that the obtained code can be corrected for displacements in time-frequency phase space, which correspond to dephasing, or rotations, in the quadrature phase space and to photon losses. Most importantly, we show that generating two-photon GKP states is relatively simple, and that such states are currently produced and manipulated in several photonic platforms where frequency and time-bin biphoton entangled states can be engineered.

A novel applied climate classification method for assessing atmospheric influence on anomalous coastal water levels

AbstractClimate classification is a commonly used tool to simplify, visualize and make sense of an otherwise unwieldy amount of climate data in applied climate science research. Typically, these classifications have stemmed from one of two perspectives, either a circulation‐to‐environment (C2E) approach, or an environment‐to‐circulation approach (E2C), each with advantages and drawbacks. This research discusses a novel environment‐to‐circulation‐to‐environment (ECE) perspective to applied climate classification, and develops a specific ECE methodology that utilizes canonical correlation and discriminant analysis—the CANDECE method. The benefits of the ECE approach generally, and the CANDECE methodology specifically, are demonstrated in applying climate classification to aid in modelling anomalous water levels (AWLs) along portions of the East and West coasts of the United States. Results show that the CANDECE method performs better than two traditional classification methods (k‐means and self‐organizing maps [SOMs]) at relating AWLs to their broad‐scale atmospheric setups, especially with regard to both high and low extreme AWLs. It is further demonstrated that, compared with the West coast, the CANDECE method is particularly advantageous along the southeastern US coast, where the primary modes of atmospheric variability (which drive the classifications produced by SOMs and k‐means) do not align with the relevant circulation‐based factors driving AWL variability. While AWLs were utilized for demonstrating the ECE proof‐of‐concept herein, ECE and CANDECE are designed to be useful for any climate application.

Skill assessment and sources of predictability for the leading modes of sub-seasonal Eastern Africa short rains variability

Skill assessment and sources of predictability for the leading modes of sub-seasonal Eastern Africa short rains variability

Cytochrome b gene as a potential DNA barcoding marker in ecoraces of tropical Tasar silkworm Antheraea mylitta Drury

BackgroundTropical Tasar silkworm Antheraea mylitta Drury plays a very crucial role in the economic development of the country employing around 9.76 million people in rural and semi-urban areas. Since the population of this species has spread over many states with varying environmental factors, the population has been isolated into many pockets, specifically adapted to that niche, and are called as “ecoraces”. Even though phenotypically these ecoraces may look similar to some level but the intra-generic genotypic variability between them is correlated to the economically important yield traits of their cocoons. The purpose of our study was to evaluate the effectiveness of Cytochrome b (Cyt b) gene of mitochondrial genome as a potential DNA barcoding marker for identification of these ecoraces as to are they really ecoraces, and attempts to establish the first country report of DNA barcode reference library of this silkworm species. Methods and resultsDNA was extracted from pupae of pure ecoraces collected from core ecozones. PCR amplicon was bead purified and further subjected to Sanger Sequencing. The parsimony tree was also created using the MEGA 11 software. The genetic distance, gene flow and relation among the ecoraces was evaluated. The conserved regions of Cyt b gene were identified along with the documentation of the mode and tempo of nucleotide variation and evolution. ConclusionThe present study confirms the efficiency of Cyt b gene as a potential DNA marker for identification of ecoraces of Tasar silkworm and is the first country report on barcoding of A. mylitta Drury. Apart from being used as an early-warning system for ecological degradation, this knowledge will help in assessing the evolutionary relationship of these economically important ecoraces and identify their position in the process of evolution aiding in the assessment of conservation priorities.

The dominant modes of recent sea level variability from 1993 to 2020 in the China Seas

A better understanding of internal climatic variability is essential for estimating and predicting regional sea level rise in the coming decades. Based on the satellite altimeter data, the cyclostationary empirical orthogonal function (CSEOF) analysis is used to extract and examine the seasonal signal, trend, El Niño-Southern Oscillation (ENSO)-related, and low-frequency mode of sea level anomaly in the China Seas from 1993 to 2020. The interannual changes in the annual cycle of sea level were significantly influenced by the mean surface pressure and the 10-m zonal wind component. Furthermore, strong El Niño events (1997/1998) can significantly affect seasonal fluctuations through precipitation and runoff. By recombining the loading vector and principal component time series of the trend mode, we estimate the sea level trends to be about 3.63 ± 0.37 mm/yr. The South China Sea, followed by the Yellow Sea, is where ENSO's effects on sea level fluctuations are most noticeable. The 2015–2016 El Niño, which was a combination of the Central Pacific (CP) ENSO Eastern Pacific (EP) ENSO, had average sea levels that were much lower than those of the 1997–1998 (CP ENSO) and 2009–2010 (CP ENSO). The Pacific Decadal Oscillation (PDO) can significantly affect the low frequency mode, but other sources also contribute to this mode. The South China Sea, especially to the west of Luzon Island, exhibits the highest level of variability in the low-frequency mode. Finally, our work shows that, in contrast to global or oceanic scales, regional CSEOF analysis does, in fact, capture regional sea level variability more accurately and detailed.

The Role of Cloud Radiative Effects in the Propagating Southern Annular Mode

AbstractThe Southern Annular Mode (SAM) is the most dominant natural mode of variability in the mid‐latitudes of the Southern Hemisphere (SH). However, both the sign and magnitude of the feedbacks from the diabatic processes, especially those associated with clouds, onto the SAM remain elusive. By applying the cloud locking technique to the Energy Exascale Earth System Model (E3SM) atmosphere model, this study isolates the positive feedback from the cloud radiative effect (CRE) to the SAM. Feedback analysis based on a wave activity‐zonal momentum interaction framework corroborates this weak but positive feedback. While the magnitude of the CRE feedback appears to be secondary compared to the feedbacks from the dry and other diabatic processes, the indirect CRE effects through the interaction with other dynamical and thermodynamical processes appear to play as important a role as the direct CRE in the life cycle of the SAM. The cross‐EOF analysis further reveals the obstructive effect of the interactive CRE on the propagation mode of the SH zonal wind directly through the CRE wave source and/or indirectly through modulating other diabatic processes. As a result, the propagation mode becomes more persistent and the SAM it represents becomes more predictable when the interactive CRE is disabled by cloud locking. Future efforts on inter‐model comparisons of CRE‐denial experiments are important to build consensus on the dynamical feedback of CRE.

Different PCA approaches for vector functional time series with applications to resistive switching processes

This paper is motivated by modeling the cycle-to-cycle variability associated with the resistive switching operation behind memristors. Although the data generated by this stochastic process are by nature current–voltage curves associated with the creation (set process) and destruction (reset process) of a conductive filament, the statistical analysis is usually based on analyzing only the scalar time series related to the reset and set voltages/currents in consecutive cycles. As the data are by nature curves, functional principal component analysis is a suitable candidate to explain the main modes of variability associated with these processes. Taking into account this data-driven motivation, in this paper we propose two new forecasting approaches based on studying the sequential cross-dependence between and within a multivariate functional time series in terms of vector autoregressive modeling of the most explicative functional principal component scores. The main difference between the two methods lies in whether a univariate or multivariate PCA is performed so that we have a different set of principal component scores for each functional time series or the same one for all of them. Finally, the sample performance of the proposed methodologies is illustrated by an application on a bivariate functional time series of reset/set curves.

Weakened western Indian Ocean dominance on Antarctic sea ice variability in a changing climate.

Patterns of sea surface temperature (SST) anomalies of the Indian Ocean Dipole (IOD) exhibit strong diversity, ranging from being dominated by the western tropical Indian Ocean (WTIO) to the eastern tropical Indian Ocean (ETIO). Whether and how the different types of IOD variability patterns affect the variability of Antarctic sea ice is not known, nor is how the impact may change in a warming climate. Here, we find that the leading mode of austral spring Antarctic sea ice variability is dominated by WTIO SST variability rather than ETIO SST or El Niño-Southern Oscillation. WTIO warm SST anomalies excite a poleward-propagating Rossby wave, inducing a tri-polar anomaly pattern characterized by a decrease in sea ice near the Amundsen Sea but an increase in regions on both sides. Such impact has been weakening in the two decades post-2000, accompanied by weakened WTIO SST variability. Under greenhouse warming, climate models project a decrease in WTIO SST variability, suggesting that the reduced impact on Antarctic sea ice from the IOD will likely to continue, facilitating a fast decline of Antarctic sea ice.