Modes Of Variability Research Articles

Differences Between Resting State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults.

Resting state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and mean based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches. 34 healthy participants were separated into a younger adult group (age 25-35, n=17) and an older adult group (age 65+, n=17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network. Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity. The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.

Subseasonal variability of sea level pressure and its influence on snowpack over mid-high-latitude Eurasia during boreal winter

The atmospheric circulation significantly influences the snowpack over mid-high-latitude Eurasia. This study examines the characteristics of the leading subseasonal variability mode of boreal winter sea level pressure (SLP) with 20-80-day period and its relationship with snowpack over mid-high-latitude Eurasia, using the fifth generation of European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data and different snowpack datasets. The SLP leading mode, characterized by a monopole pattern with a strong surface anomalous high centered near the Ural Mountains, exhibits a barotropic structure and extends from the surface to the tropopause. Above SLP and geopotential height anomalies propagate southeastward from the Barents-Kara Sea to East Asia. This leading SLP mode contributes to surface air temperature (SAT) and snowfall circulation anomalies over mid-high-latitude Eurasia. The latter two both directly influence on snowpack anomalies in situ. Over high latitude region, snowfall circulation anomaly is the dominant factor to control the snow depth anomaly. Over middle latitude region, both SAT and snowfall circulation anomalies lead to the snowpack anomaly. Furthermore, the response of snow depth to the leading subseaonal SLP mode occurs 2–5 days earlier than the response of snow cover to the same mode over middle latitude region. In addition, it is suggested that the Arctic Oscillation (AO), East Atlantic/West Russia (EAWR) and Polar/Eurasia (PEU) pattern may contribute to the development of the leading SLP mode and subsequently influence snowpack anomalies.

Extreme summer temperature anomalies over Greenland largely result from clear-sky radiation and circulation anomalies

The polar regions have been undergoing amplified warming in recent years. In particular, Greenland has experienced anomalously warm summers with intense melt rates. We employ a surface radiation budget framework to examine the causes for positive and negative summer temperature anomaly events over Greenland from 1979 to 2021. We found a dominant contribution of the clear-sky downwelling longwave radiation and the surface albedo feedback to temperature anomalies. Atmospheric temperature perturbations dominate the effect of anomalous emissivity on clear-sky downwelling longwave radiation. In warm years, enhanced turbulent heat exchange due to increased surface temperature and diabatic warming in the troposphere induces adiabatic heating of the atmosphere, enhanced moisture advection, and a high-pressure anomaly with a blocking-like anti-cyclonic circulation anomaly following peak temperature days. Different modes of natural climate variability, in particular, related to blocking over Greenland, can further amplify or dampen the ongoing warming trend, causing extreme temperature events.

The impact of El Niño–Southern Oscillation on the total column ozone over the Tibetan Plateau

Abstract. The Tibetan Plateau (TP; approximately 27.5–37.5° N, 75.5–105.5° E) is the highest and largest plateau on Earth with a mean elevation of over 4 km. This special geography causes strong surface solar ultraviolet radiation (UV), with potential risks to human and ecosystem health, which is mainly controlled by the local stratospheric ozone concentration. The El Niño–Southern Oscillation (ENSO), the dominant mode of interannual variability on Earth, is characterised by the tropical Pacific Ocean sea surface temperature anomalies (SSTAs) and sea level pressure change for the warm-phase El Niño and cold-phase La Niña events. Although some studies have suggested the existence of positive correlation between ENSO and the total column ozone (TCO) over the TP, the mechanism underlying this effect is not fully understood. Here we use the Copernicus Climate Change Service (C3S) merged satellite dataset, the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset and the TOMCAT three-dimensional (3D) offline chemical transport model forced by ERA5 meteorological reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) over the period 1979–2021 to investigate the influence of ENSO on the TCO over the TP. We find that the El Niño (La Niña) events favour positive (negative) TCO anomalies over the TP from wintertime of its mature phase to springtime of its decaying phase. Through studying the ozone profile, we attribute the positive (negative) TCO anomalies mainly to the increased (decreased) ozone at the 200–70 hPa levels, i.e. in the upper troposphere and lower stratosphere (UTLS) regions. Our results suggest that the El Niño events impact the TP TCO via the following potential processes: (1) a negative upper-level geopotential height anomaly associated with El Niño is responsible for a decrease in air column thickness; (2) the thickness decrease modulates reduced tropospheric temperature and thus favours a decrease in the tropopause height (TH); and (3) such a TH decrease tends to induce a change in the relative amounts of ozone-poor tropospheric and ozone-rich stratospheric air in the profile, which increases the partial column ozone in the UTLS and hence corresponds to the TP TCO increase. The La Niña events affect TP TCO in a manner resembling the El Niño events, except with anomalies of opposite sign. This work provides a systematic understanding of the influence of ENSO on ozone over the TP, which has implications for the interannual variability of ozone.

On the need to integrate interannual natural variability into coastal multihazard assessments

The co-occurrence of multiple hazards can either exacerbate or mitigate risks. The interrelationships between multiple hazards greatly depend on the spatiotemporal scale and can be difficult to detect from large to local scales. In this paper, we identified coastal regions worldwide where the leading tropical (El Niño-Southern Oscillation, ENSO) and polar (Arctic Oscillation, AO; Southern Annular Mode, SAM) modes of climate variability simultaneously modify the seasonal conditions of multiple hazards, including the near-surface wind speed and swell and wind-sea wave powers. We classified the results at the national and municipal levels, with a focus on multiple hazards simultaneously occurring in space and time. The results revealed that the ENSO modulates multiple hazards, affecting approximately 40% of coastal countries, while the polar annular modes affect approximately 30% of coastal countries. The ENSO induced a greater diversity of multiple hazards, with Asian countries (e.g., Indonesia experienced increases of + 2% in wind and + 7% in swell) and countries in the Americas (e.g., Peru exhibited increases of + 1.5% in wind and + 6% in wind-sea) the most notably affected. The SAM imposed a greater influence on swells in the eastern countries of ocean basins (+ 2.5% in Chile) than in other countries, while the influence of the AO was greater in Norway and the UK (+ 12% for wind-sea and 8% for swell). Low-lying islands exhibited notable variations in pairwise hazards between phases and seasons. Our results could facilitate the interpretation of multihazard interactions and pave the way for a wide range of potential implementations of different coastal industries.

The signature of the main modes of climatic variability as revealed by the Jenkinson‐Collison classification over Europe

AbstractThe Jenkinson‐Collison Weather Typing (JC‐WT) method uses sea‐level pressure gradients to create 27 types based on the geostrophic flow and vorticity around any extratropical target location. Typically, JC‐WTs are applied over specific locations or limited domains, thus hampering the understanding of the impact of large‐scale mechanisms on regional climate. This study explores the links between regional climate variability, as represented by the JC‐WTs, and large‐scale phenomena, to describe the synoptic‐scale variability in the North Atlantic‐European region and evaluate the JC‐WT methodology. Large‐scale circulation is here characterized by major atmospheric low‐frequency modes, namely the North Atlantic Oscillation, the East Atlantic and the Scandinavian teleconnection indices, and by atmospheric blockings. Results show that JC‐WTs coherently capture the spatial and temporal variability of the large‐scale modes and yields a characteristic response to blocking events. Overall, our results underpin the exploratory potential of this method for the analysis of the near‐surface circulation. These findings endorse the use of JC‐WTs and support the reliability and utility of the JC‐WT classification for process‐based model assessments and model selection, a crucial task for climate impact studies.

The relationship between Antarctic sea-ice extent change and the main modes of sea-ice variability in austral winter

Accompanying global warming, Antarctic sea-ice extent shows a somewhat increasing trend from 1979 to 2014, followed by an abrupt decrease after 2016. Our previous study examined the change of Antarctic sea-ice extent in austral summer, autumn and spring. In this study, we turn our attention to the austral winter, relating the main modes of sea-ice variability to sea-ice extent in the Pacific, Atlantic and Indian sectors of the Southern Ocean. We find that the modes with the strongest correlation with the sea-ice extent are the third, first and first modes in the Pacific, Atlantic and Indian sectors, respectively. Atmospheric circulation anomalies of zonal wavenumber three over the Southern Ocean, related to planetary wave trains induced by the SST anomalies over the south-western Pacific and the southern Indian oceans, can explain sea-ice concentration anomalies of the third mode in the Pacific sector through thermodynamic and dynamic processes. Sea-ice anomalies of the first modes in the Atlantic and Indian sectors result from atmospheric circulation anomalies of a positive and negative phases of the Southern Annular Mode, respectively. The anomalous Southern Annular Mode is also associated with wave trains over the Southern Ocean excited by SST anomalies over the southern Indian Ocean and the south-western Pacific Ocean. The relationship between SST anomalies and Antarctic sea-ice anomalies can provide a reference for the prediction of Antarctic sea-ice anomalies in austral winter on interannual and decadal timescales.

Empirical orthogonal function analysis of lightning flashes over India

Lightning studies are highly focused on spatial and temporal variability in various scales but very limited studies are focused on dominant spatial modes of variability. This study intends to identify the possible spatial modes of climate variability of lightning over India during different seasons and relate them to regional and large-scale climate modes. Empirical orthogonal function analysis of lightning has been carried out and the first three orthogonally independent modes are considered in order to retrieve the maximum variance explained by each mode. To understand the role of remote and local teleconnections on the lightning flash rate (LFR) variability, we have analyzed two Pacific Ocean modes (El Niño Southern Oscillation; ENSO, Pacific Decadal Oscillation; PDO) and two Indian Ocean modes (Indian Ocean Dipole; IOD and Bay of Bengal (BOB) meridional Sea Surface Temperature (SST) gradient). First mode is positively correlated with the warm phase of ENSO and PDO whereas second and third modes are negatively correlated with the warm phase of ENSO and PDO during pre-monsoon, post-monsoon and winter. Reverse is true for the monsoon season due to the shift in walker cell caused by the changes in the location of the heat sources and sinks. A strong positive correlation of IOD and BOB meridional SST gradient with first mode, suggests the vital role of nearby Indian Ocean in explaining the typical lightning flashes over India due to the enhanced zonal and meridional circulation, thereby moisture supply to the Indian subcontinent. The impact of Nino-3.4, IOD and BOB meridional SST gradient on lightning over India further suggest the role of SST in local and remote influence on lightning variability through the distribution and transport of heat and moisture.

Assessing Multi-Scale Atmospheric Circulation Patterns for Improvements in Sub-Seasonal Precipitation Predictability in the Northern Great Plains

This study leverages the relationships between the Great Plains low-level jet (GP-LLJ) and the circumglobal teleconnection (CGT) to assess the enhancement of 30-day rainfall forecast in the Northern Great Plains (NGP). The assessment of 30-day simulated precipitation using the Climate Forecast System (CFS) is contrasted with the North American Regional Reanalysis, searching for sources of precipitation predictability associated with extended wet and drought events. We analyze the 30-day sources of precipitation predictability using (1) the characterization of dominant statistical modes of variability of 900 mb winds associated with the GP-LLJ, (2) the large-scale atmospheric patterns based on 200 mb geopotential height (HGT), and (3) the use of GP-LLJ and CGT conditional probability distributions using a continuous correlation threshold approach to identify when and where the forecast of NGP precipitation occurs. Two factors contributing to the predictability of precipitation in the NGP are documented. We found that the association between GP-LLJ and CGT occurs at two different scales—the interdiurnal and the sub-seasonal, respectively. The CFS reforecast suggests that the ability to forecast sub-seasonal precipitation improves in response to the enhanced simulation of the GP-LLJ and CGT. Using these modes of climate variability could improve predictive frameworks for water resources management, governance, and water supply for agriculture.

Variability in flood frequency in sub-Saharan Africa: The role of large-scale climate modes of variability and their future impacts

Sub-Saharan Africa (SSA) is strongly affected by flood hazards, endangering human lives and economic stability. However, the role of internal climate modes of variability in driving fluctuations in SSA flood occurrence remains poorly documented and understood. To address this gap, we quantify the relative and combined contribution of large-scale climate drivers to seasonal and regional flood occurrence using a new 65-year daily streamflow dataset, sea-surface temperatures derived from observations, and 12 Single Model Initial-condition Large Ensembles (SMILEs) from the Coupled Model Intercomparison Project Phases 5 and 6. We find significant relationships between floods and large-scale climate variability across SSA, with climatic drivers accounting for 30–90 % of the variability in floods. Notably, western, central, and the summer-rain region of southern Africa display stronger teleconnections to large-scale climate variability in comparison to East Africa and the winter-rain region of South Africa, where regional circulation patterns and human activities may play a more important role. In southern and eastern Africa, floods are mainly influenced by teleconnections with the Pacific and Indian Oceans, while in western and central Africa, teleconnections with the Atlantic Ocean and Mediterranean Sea play a larger role. We also find that the number of floods is projected to fluctuate by ± 10–50 % during the 21st century in response to different sequences of key modes of climate variability. We also note that the relative contributions of large-scale climate variability to future flood risks are generally consistent across all SMILEs. Our findings thus provide valuable information for long-term disaster risk reduction and management.

Non-stationary models for hydrological extremes in the mountain rivers of the Argentinean Central Andes

ABSTRACT The assessment of extreme hydrological events relies on the assumption that time series are independent and identically distributed. However, empirical evidence contradicts this assumption, indicating the presence of nonstationarity, including trends, step changes, or both, in the river records of the Argentinean Central Andes. This study proposes the use of generalized additive models in location, scale and shape (GAMLSS) to model the annual maximum and minimum flow of seven rivers. Firstly, the presence of trends and step changes was assessed. Subsequently, the models were formulated considering time and various climatic indices as covariates. The findings reveal declining trends and negative jumps over the past 50 to 70 years. The location parameters of the models exhibit linear or smoothing dependence with time or climate indices covariates. Different modes of climate variability are associated with hydrological extremes. The nonstationary models provide a novel complementary framework for water resource planning in the region.

Statistical shape modeling of the geometric morphology of the canine femur, tibia, and patella.

Bone morphometry varies among dogs of different sizes and breeds. Studying these differences may help understand the predisposition of certain breeds for specific orthopedic pathologies. This study aimed to develop a statistical shape model (SSM) of the femur, patella, and tibia of dogs without any clinical orthopeadic abnormalities to analyze and compare morphological variations based on body weight and breed. A total of 97 CT scans were collected from different facilities and divided based on breed and body weight. The 3D models of the bones were obtained and aligned to a coordinate system. The SSM was created using principal component analysis (PCA) to analyze shape variations. The study found that the first few modes of variation accounted for a significant percentage of the total variation, with size/scale being the most prominent factor. The results provide valuable insights into normal anatomical variations and can be used for future research in understanding pathological bone morphologies and developing 3D imaging algorithms in veterinary medicine.

Accuracy tradeoffs between individual bone and joint-level statistical shape models of knee morphology

Statistical shape models (SSMs) are useful tools in evaluating variation in bony anatomy to assess pathology, plan surgical interventions, and inform the design of orthopaedic implants and instrumentation. Recently, by considering multiple bones spanning a joint or the whole lower extremity, SSMs can support studies investigating articular conformity and joint mechanics. The objective of this study was to assess tradeoffs in accuracy between SSMs of the femur or tibia individually versus a combined joint-level model. Three statistical shape models were developed (femur-only, tibia-only, and joint-level) for a training set of 179 total knee arthroplasty (TKA) patients with osteoarthritis representing both genders and several ethnicities. Bone geometries were segmented from preoperative CT scans, meshed with triangular elements, and registered to a template for each SSM. Principal component analysis was performed to determine modes of variation. The statistical shape models were compared using measures of compactness, accuracy, generalization, and specificity. The generalization evaluation, assessing the ability to describe an unseen instance in a leave-one-out analysis, showed that errors were consistently smaller for the individual femur and tibia SSMs than for the joint-level model. However, when additional modes were included in the joint-level model, the errors were comparable to the individual bone results, with minimal additional computational expense. When developing more complex SSMs at the joint, lower limb, or whole-body level, the use of an error threshold to inform the number of included modes, instead of 95 % of the variation explained, can help to ensure accurate representations of anatomy.

Morphology of the scaphotrapeziotrapezoid joint: A multi-domain statistical shape modeling approach.

The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three-dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi-domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM-generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three-dimensional triangular surface meshes. A multi-domain, particle-based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface-normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities-a proxy for joint space-were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging-derived STT joint space.

Zonal Wavenumber 3 Forces Extreme Precipitation in South America

Abstract The Southern Hemisphere climate and weather are affected by several modes of variability and climate phenomena across different time and spatial scales. An additional key component of the atmosphere dynamics that greatly influences weather is quasi-stationary Rossby waves, which attract particular interest as they are often associated with synoptic-scale extreme events. In the Southern Hemisphere extratropical circulation, the most prominent quasi-stationary Rossby wave pattern is the zonal wavenumber 3 (ZW3), which has been shown to have impacts on meridional heat and momentum transport in mid- to high latitudes and on the Antarctic sea ice extent. However, little is known about its impacts outside of polar regions. In this work, we use ERA5 reanalysis data on monthly time scales to explore the influence of phase and amplitude of ZW3 on temperature and precipitation across the Southern Hemisphere midlatitudes. Our results show a significant impact in various regions for all seasons. One of the most substantial effects is observed in precipitation over southeastern Brazil during austral summer, where different phases of the ZW3 force opposite anomalies. When using the ZW3 phase and amplitude as prior information, the probability of occurrence of precipitation extremes in this region increases up to three times. Additionally, we find that this ZW3 weather signature is largely independent of the zonally symmetric Southern Annular Mode (SAM); neither does it seem to be linked to El Niño–Southern Oscillation (ENSO) or Indian Ocean dipole (IOD) signal.

A novel stabilized artificial neural network model enhanced by variational mode decomposing

Existing artificial neural networks (ANNs) have attempted to efficiently identify underlying patterns in environmental series, but their structure optimization needs a trial-and-error process or an external optimization effort. This makes ANNs time consuming and more complex to be applied in practice. To alleviate these issues, we propose a stabilized ANNs, called SANN. The SANN efficiently optimizes ANN structure via incorporation of an additional numeric parameter into every layer of the ANN. To exemplify the efficacy and efficiency of the proposed approach, we provided two practical case studies involving meteorological drought forecasting at cities of Burdur and Isparta, Türkiye. To enhance SANN forecasting accuracy, we further suggested the hybrid VMD-SANN that integrated variation mode decomposition (VMD) with SANN. To validate the new hybrid model, we compared its results with those obtained from hybrid VMD-ANN and VMD-Radial Base Function (VMD-RBF) models. The results showed superiority of the VMD-SANN to its counterparts. Regarding Nash Sutcliffe Efficiency measure, the VMD-SANN achieves accurate forecasts as high as 0.945 and 0.980 in Burdur and Isparta cities, respectively.

Formation Mechanisms of the Decadal Indian Ocean Dipole Driven by Remote Forcing from the Tropical Pacific

Abstract On decadal time scales, a zonal SST dipole dominates the tropical Indian Ocean in boreal late summer and fall, called the decadal Indian Ocean dipole (D-IOD). The D-IOD has a spatial pattern different from the traditional interannual IOD, with its eastern pole located off Java, rather than the whole Sumatra–Java coasts as the latter. Here, we show that the D-IOD is generated by both the remote tropical Pacific decadal variability (TPDV) forcing and the decadal modulation of interannual IODs, but with its distinctive spatial pattern and seasonality mainly shaped by the former. In August–September (AS), due to the seasonal strengthening of trade winds, the descending branch of TPDV-induced Walker circulation moves westward into the eastern Indian Ocean relative to June–July, which stimulates equatorial easterly anomalies and oceanic upwelling Kelvin waves, causing subsurface cooling off Java. The subsurface cooling just occurs within the time window of climatological coastal upwelling so that subsurface cold anomalies are brought into the surface by mean upwelling and further transported offshore by mean flows, forming the D-IOD eastern pole. The subsurface cooling is only generated near Java but not Sumatra, because the former is closer to the exit of the Indonesian Throughflow (ITF). Weakened ITF during positive TPDV inhibits the growth of subsurface warming off Java prior to the establishment of AS equatorial easterly anomalies, whereas this ITF effect is not observed off Sumatra. Moreover, warming of the D-IOD western pole might be associated with off-equatorial Rossby waves induced by TPDV-related wind stress curls. Significance Statement The decadal Indian Ocean dipole (D-IOD) is one of the leading decadal modes of the tropical Indian Ocean SST variations. Its formation mechanisms, especially those related to its spatiotemporal characteristics, are not well understood. Based on observations and reanalysis, we show that the tropical Pacific decadal variability (TPDV) mainly accounts for the distinctive spatial pattern and seasonality of the D-IOD via the seasonal migration of the anomalous Walker circulation and ensuing oceanic subsurface dynamics. Our results highlight that the TPDV is an important source of D-IOD’s predictability, and it might be beneficial for operational decadal predictions for the tropical Indian Ocean.

Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection

AbstractWe train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics‐based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1,200 ppm CO2 global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network. Integrations with all three data‐driven emulators successfully capture the Quasi‐Biennial Oscillation (QBO) and sudden stratospheric warmings, key modes of stratospheric variability, with the boosted forest more accurate than the random forest in replicating their statistics across our range of carbon dioxide perturbations. The boosted forest and neural network capture the sign of the QBO period response to increased CO2, though both struggle with the magnitude of this response under the more extreme 1,200 ppm scenario. To investigate the connection between performance in the control climate and the ability to generalize, we use techniques from interpretable machine learning to understand how the data‐driven methods use physical information. We leverage this understanding to develop a retraining procedure that improves the coupled performance of the boosted forest in the control climate and under the 800 ppm CO2 scenario.

Distinct propagating and standing modes of tropical intraseasonal variability as represented by the two principal oscillation patterns

Although the tropical intraseaonal variability (TISV), as the most important predictability sources for subseasonal-to-seasonal (S2S) prediction, is dominated by Madden-Julian oscillation (MJO), its significant fraction does not always share the canonical MJO features, especially when the convective activity arrives at Maritime Continent. In this study, using principal oscillation pattern (POP) analysis on the combined fields of daily equatorial convection and zonal wind, two distinct leading TISV modes with relatively slower e-folding decay rates are identified. One is an oscillatory mode with the period of 51 days and e-folding time of 19 days, capturing the eastward propagating (EP) feature of the canonical MJO. The other is a non-oscillatory damping mode with e-folding time of 13.6 days, capturing a standing dipole (SD) with convection anomalies centered over the Maritime Continent and tropical central Pacific, respectively. Compared to the EP mode, the leading moisture anomalies at low level to the east of convection center are diminish for the SD mode, and instead, the strong negative anomalies of moisture and subsidence motion emerge in the tropical central Pacific area, which may be responsible for the distinct propagation features. Without filtering methods used, timeseries of the two POPs could be applied to the real-time monitoring of EP and SD events in the phase-space diagram. The two modes can serve as the simple and objective approach for a better characterization for diverse natures of TISV beyond the canonical MJO description, which may further shed light on dynamics of the TISV and its predictability.

Near-surface wind speed trends and variability over the Antarctic Peninsula, 1979–2022

Near-Surface Wind Speed (SWS) is a crucial but less studied climate variable in the northern Antarctic Peninsula (AP). This research evaluates, for the first time, 44 years (i.e., 1979–2022) of SWS trends and variability across the AP using two data sources: (i) observational data from quality-controlled and homogenized meteorological stations, and (ii) reanalysis data from ERA5; the accuracy of this product strongly depends on each station with an overall underestimation of observed SWS. Annual trends in observed SWS exhibit a positive trend, being statistically significant in autumn and spring, with a marked intraanual and spatial variability in the sign and magnitudes across the AP. In addition, the multidecadal variability of observed SWS showed a general positive trend until ∼2001 (varying between 1993 and 2007 depending on each season), followed by a period of slowdown in the last two decades. Over the AP, SWS changes are mainly driven by two principal modes of atmospheric variability: i.e., mainly the Southern Annular Mode (SAM) and, secondarily, by the El Niño-Southern Oscillation (ENSO). Overall, positive trends in SWS could be partly associated with the increase and poleward shift of the westerlies due to the positive trend of the SAM index. However, as previous studies pointed out for air temperature and precipitation, we found a non-stationary and complex relationship of these modes with SWS changes. This research addresses the gap in SWS changes and variability in the AP and surrounding Southern Ocean and the influence of the atmospheric circulation, a hotspot area in climate change research.