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The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated 1 – 3 . Although various physical processes 4 – 8 have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern 9 , 10 . In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area 9 . As a consequence, the contribution of Arctic warming to global SAT changes may have been underestimated, leading to an uncertainty in the hiatus debate. Here, we constructed a new Arctic SAT dataset using the most recently updated global SATs 2 and a drifting buoys based Arctic SAT dataset 11 through employing the ‘data interpolating empirical orthogonal functions’ method 12 . Our estimate of global SAT rate of increase is around 0.112 °C per decade, instead of 0.05 °C per decade from IPCC AR5 1 , for 1998–2012. Analysis of this dataset shows that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown. The Arctic is under-represented in surface temperature datasets and this could affect estimates of global warming. A new dataset with greater coverage of the Arctic shows a higher warming rate of 0.112 °C per decade compared to 0.005 °C from IPCC AR5.

We analyse the impact of the North Atlantic Oscillation (NAO) on the climate of East Asia at subseasonal time scales during both winter and summer. These teleconections have mainly been investigated at seasonal and longer time scales, while higher-frequency links are largely unexplored. The NAO is defined using extended empirical orthogonal functions on pentad-mean observations, which allows to elucidate the oscillation’s spatial and temporal evolution and clearly separate the development and decay phases. The downstream dynamical imprint and associated temperature and precipitation anomalies are quantified by means of a linear regression analysis. It is shown that the NAO generates a significant climate response over East Asia during both the dry and wet seasons, whose spatial pattern is highly dependent on the phase of the NAO’s life cycle. Temperature and precipitation anomalies develop concurrently with the NAO mature phase, and reach maximum amplitude 5–10 days later. These are shown to be systematically related to mid and high-latitude teleconnections across the Eurasian continent via eastward-propagating quasi-stationary Rossby waves instigated over the Atlantic and terminating in the northeastern Pacific. These findings underscore the importance of rapidly evolving dynamical processes in governing the NAO’s downstream impacts and teleconnections with East Asia.

Zonal wave 3 (ZW3) is an important feature of the Southern Hemisphere extratropical atmospheric circulation and has strong impacts on meridional heat and momentum transport, regional Antarctic sea ice extent, and Southern Hemisphere blocking events. Attempts have been made in the past to define an index that quantifies the variability in the ZW3 pattern; however, existing methods are based on fixed geographical locations and fail to capture certain ZW3 events because of strong variability in phase. In addition, a fixed spatial index poorly characterizes ZW3 in CMIP models, which can exhibit biases in the mean phase of the ZW3 pattern. In this study, we introduce a new way to characterize ZW3 variability by incorporating two indices, one each for magnitude and phase, based on the combination of the first two empirical orthogonal functions (EOFs) of the 500-hPa meridional wind anomalies. We show that the new ZW3 index provides a clear advantage over past indices because it captures a substantially higher proportion of variance (∼40% compared to ∼16%), and it can be used for both reanalysis datasets and coupled climate models regardless of model biases. A composite analysis associated with the new index reveals a strong relationship between the ZW3 defined by our index and sea ice fraction around Antarctica, with significant regional sea ice anomalies during strong ZW3 events with different phases.

Some supercellular tornado outbreaks are composed almost entirely of tornadic supercells, while most consist of both tornadic and nontornadic supercells sometimes in close proximity to each other. These differences are related to a balance between larger-scale environmental influences on storm development as well as more chaotic, internal evolution. For example, some environments may be potent enough to support tornadic supercells even if less predictable intrastorm characteristics are suboptimal for tornadogenesis, while less potent environments are supportive of tornadic supercells given optimal intrastorm characteristics. This study addresses the sensitivity of tornadogenesis to both environmental characteristics and storm-scale features using a cloud modeling approach. Two high-resolution ensembles of simulated supercells are produced in the near- and far-field environments observed in the inflow of tornadic supercells during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). All simulated supercells evolving in the near-field environment produce a tornado, and 33% of supercells evolving in the far-field environment produce a tornado. Composite differences between the two ensembles are shown to address storm-scale characteristics and processes impacting the volatility of tornadogenesis. Storm-scale variability in the ensembles is illustrated using empirical orthogonal function analysis, revealing storm-generated boundaries that may be linked to the volatility of tornadogenesis. Updrafts in the near-field ensemble are markedly stronger than those in the far-field ensemble during the time period in which the ensembles most differ in terms of tornado production. These results suggest that storm-environment modifications can influence the volatility of supercellular tornadogenesis.

The summer spatial structure and sub-monthly temporal evolution of one of the key dynamical features of Central American climate, the Caribbean Low-Level Jet (CLLJ), is investigated by means of extended empirical orthogonal functions (EEOFs). The Caribbean 925-hPa zonal wind from the CFSR reanalysis for the period 1979 – 2010 is used for the analysis. This approach reveals new insights into the dynamical processes and spatio-temporal evolution of the CLLJ summer intensification, and through lead and lag linear regressions, significant climate links in the broader Caribbean region are identified. The results show that the CLLJ generates significant precipitation and temperature responses with a distinct temporal evolution over the Caribbean-Atlantic domain to that over the tropical Pacific, which hints at different underlying controlling mechanisms over these two large-scale regions. These anomalies are linked with the mid and upper tropospheric circulation, where a vertical cell over the Caribbean (ascending at the jet exit and subsiding at its entrance) varies in phase with large-scale divergence over the Pacific Ocean. Extratropical hemispheric-wide waves and the weakening of a thermal low in northeast Mexico-central US are identified as potential triggering factors for the CLLJ summer intensification. Two leading modes of tropical variability, El Niño Southern Oscillation and the Madden-Julian Oscillation, are found to modulate the CLLJ by intensifying it and prolonging its life cycle. Details of the underlying mechanisms are provided. These results help to advance the understanding of the processes that modulate local climate variations, which is an important issue in view of the rapid climate change the region is undergoing.

A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

The analysis revisits the calculation of the empirical orthogonal functions (EOFs) and principal components (PCs) of sea surface temperature (SST) in the North Pacific from 1950 to 2021. The first EOF and PC of SST has proven to be such a useful metric of variability in the North Pacific that it is called the Pacific Decadal Oscillation (PDO). We find that the period of persistent marine heatwaves beginning in 2014 caused a fundamental change to the first EOF and PC of SST (calculated using data from 1950 to 2021) as compared to the established PDO spatial pattern (calculated using data from 1950 to 1993). The second EOF of SST has also changed during this period, both in spatial pattern and in the amount of variance explained. A conclusion is that the PDO and other EOF based metrics may not be as useful in the future as climate continues to change. Plain Language Summary The Pacific Decadal Oscillation (PDO) is a widely used measure of the temperature variability in the North Pacific Ocean. The PDO is the result of a well‐known technique called empirical orthogonal function (EOF) analysis that isolates the most energetic modes of variability of the analyzed variable. The first time EOF analysis was applied to oceanographic data was in the 1970's when it was used to identify the most energetic modes of North Pacific sea surface temperature (SST). The first EOF of North Pacific SST has proved so useful as a measure that it received the moniker PDO. Our analysis suggests that a period of persistent marine heatwaves in the North Pacific since 2014 has been so powerful that this first mode of variability of SST has fundamentally changed and the PDO may not be as useful an indicator as it once was. Key Points The calculation of empirical orthogonal functions and principal components of North Pacific sea surface temperature is revisited The period of persistent marine heatwaves since 2014 has caused most energetic modes to change A conclusion is that indices based on empirical orthogonal function analysis may not be as useful as climate continues to change

The computational burden could be an issue of traditional model predictive control (MPC) in real-time applications. There are different procedures to cope with this problem such as using explicit MPC or approximation of the control inputs using orthogonal-basis functions. This paper presents the design procedure and experimental validation of a generalized discrete-time Malmquist orthogonal functions-based model predictive control (MbMPC) whose usage can decrease calculation concerns. To the authors’ knowledge, this is the first time the Malmquist functions are introduced into the MPC design. The discrete-time orthogonal Malmquist functions properties are used within this approach for approximating the future control input increments with a smaller number of parameters than in traditional MPC approaches. It is shown that the satisfactory controller and closed-loop system performances can be achieved using smaller number of tuning parameters. The performance of the proposed MbMPC is shown and compared with discrete-time Laguerre orthogonal functions based MPC (LbMPC) applied to a DC motor servo system. The simulation and experimental results demonstrate better IAE, ITAE, ISE and ITSE controller performance indices as well as faster average calculation time in comparison with the LbMPC approach. Additionally, the advantage of using the proposed method is emphasized by showing how the length of the prediction horizon affects the computational burden in comparison to the traditional MPC approaches.

The orthogonal basis functions (OBFs) method is a prevailing choice for the detection of undersurface magnetic anomaly targets. However, it requires the detecting platform or target to move uniformly along a straight path. To circumvent the restrictions, a new adaptive basis functions (ABFs) approach is proposed in this article. It permits the detection platform to search for a possible target at different speeds along any course. The ABFs are constructed using the real-time data of the onboard triaxial fluxgate, GPS module, and attitude gyro. Based on the pseudo-energy of an apparent target signal, the constant false alarm rate (CFAR) method is employed to judge whether a target is present. Moreover, by defining the pixel as a relative possibility for a target at a geographic location, a magnetic anomaly target imaging scheme is introduced by displaying the pixels onto the searching area. On-site experimental data are utilized to demonstrate the proposed approach. Compared with the traditional OBFs method, the present ABFs approach can substantially improve the detection possibility and reduce false alarms.

The thermodynamic processes associated with convection in tropical African and northeastern Pacific easterly waves (AEWs and PEWs, respectively) are examined on the basis of empirical orthogonal functions (EOFs) and a plume buoyancy framework. Linear regression analysis reveals the relationship between temperature, moisture, buoyancy, and precipitation in EWs. Plume buoyancy is found to be highly correlated with rainfall in both AEWs and PEWs, and a near 1:1 relationship is found between a buoyancy-based diagnostic of rainfall and rainfall rates from ERA5. Close inspection of the contribution of moisture and temperature to plume buoyancy reveals that temperature and moisture contribute roughly equally to the buoyancy in AEWs, while moisture dominates the distribution of buoyancy in PEWs. A scale analysis is performed in order to understand the relative amplitudes of temperature and moisture in easterly waves. It is found that the smaller contribution of temperature to the thermodynamics of PEWs relative to AEWs is related to their slower propagation speed, which allows PEWs to more robustly adjust to weak temperature gradient (WTG) balance. The consistency of the buoyancy analysis and the scale analysis indicates that PEWs are moisture modes: waves in which water vapor plays a dominant role in their thermodynamics. AEWs, on the other hand, are mixed waves in which temperature and moisture play similar roles in their thermodynamics.