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1. Subseasonal Variability of U.S. Coastal Sea Level from MJO and ENSO Teleconnection Interference

   https://doi.org/10.1175/WAF-D-23-0002.1  

   Arcodia, Marybeth C. ; Becker, Emily ; Kirtman, Ben P. ( February 2024 , Weather and Forecasting)

   Climate variability affects sea levels as certain climate modes can accelerate or decelerate the rising sea level trend, but subseasonal variability of coastal sea levels is underexplored. This study is the first to investigate how remote tropical forcing from the MJO and ENSO impact subseasonal U.S. coastal sea level variability. Here, composite analyses using tide gauge data from six coastal regions along the U.S. East and West Coasts reveal influences on sea level anomalies from both the MJO and ENSO. Tropical MJO deep convection forces a signal that results in U.S. coastal sea level anomalies that vary based on MJO phase. Further, ENSO is shown to modulate both the MJO sea level response and background state of the teleconnections. The sea level anomalies can be significantly enhanced or weakened by the MJO-associated anomaly along the East Coast due to constructive or destructive interference with the ENSO-associated anomaly, respectively. The West Coast anomaly is found to be dominated by ENSO. We examine physical mechanisms by which MJO and ENSO teleconnections impact coastal sea levels and find consistent relationships between low-level winds and sea level pressure that are spatially varying drivers of the variability. Two case studies reveal how MJO and ENSO teleconnection interference played a role in notable coastal flooding events. Much of the focus on sea level rise concerns the long-term trend associated with anthropogenic warming, but on shorter time scales, we find subseasonal climate variability has the potential to exacerbate the regional coastal flooding impacts.

   Coastal flooding due to sea level rise is increasingly threatening communities, but natural fluctuations of coastal sea levels can exacerbate the human-caused sea level rise trend. This study assesses the role of tropical influences on coastal subseasonal (2 weeks–3 months) sea level heights. Further, we explore the mechanisms responsible, particularly for constructive interference of signals contributing to coastal flooding events. Subseasonal signals amplify or suppress the lower-frequency signals, resulting in higher or lower sea level heights than those expected from known climate modes (e.g., ENSO). Low-level onshore winds and reduced sea level pressure connected to the tropical phenomena are shown to be indicators of increased U.S. coastal sea levels, and vice versa. Two case studies reveal how MJO and ENSO teleconnection interference played a role in notable coastal flooding events. Much of the focus on sea level rise concerns the long-term trend associated with anthropogenic warming, but on shorter time scales, we find subseasonal climate variability has the potential to exacerbate the regional coastal flooding impacts.

    

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2. Drivers of Variability in the Position of the Subtropical Jet Over Nepal in the Last Millennium Ensemble

   Wiggins, C. ( January 2022 , Transactions American Geophysical Union)

   Nepal is positioned at the intersection of the Indian Summer Monsoon (ISM) and Subtropical Jet (SJ). Although the ISM is responsible for ~two thirds of annual precipitation, the SJ supplies precipitation in the winter and spring, with the jet migrating southwards to the subcontinent beginning in October and reaching its most southerly position in May before moving northward in June. Using the state-of-the-art Community Earth System Model Last Millennium Ensemble, we investigated potential drivers of the latitudinal position of the SJ over Nepal (referred to as the Himalayan Jet) between 850-2005 CE. The Himalayan Jet Latitude [HJL] is defined as the latitude with the highest wind speed at 200 mb for every longitude containing Nepal (Thapa et al., 2022). In order to identify dominant periodicities in HJL positioning, power-spectral-density analyses were used. For the purpose of evaluating drivers of HJL position, we identified years with a northward or southward displaced HJL, defined as being two standard deviations above or below the average annual HJL position, and used anomaly composites of precipitation, winds (upper- and lower-level), sea surface temperature, moisture transport (lower-level at 850mb), and geopotential height (upper-level at 200mb). Our analyses seem to point toward a link between HJL and the phases of the El Niño Southern Oscillation and Indian Ocean Dipole (IOD): Southerly HJL years often occur during years with an El Niño and a positive IOD event. Northerly HJL years often occur when a Rossby wave train appears to be present over Nepal, indicative of a remote teleconnection. We provide an initial quantification of the physical mechanics of how these climate modes in the Pacific, Indian, and Atlantic Oceans, including remote teleconnections transmitted via atmospheric Rossby Waves, affect HJL. These climate model simulation results are also compared with a sub-decadally-resolved, precisely-dated, composite stalagmite isotope record of ISM variability from Siddha Baba cave, central Nepal. 

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3. Drivers of Variability in the Position of the Subtropical Jet Over Nepal in the Last Millennium Ensemble

   Wiggins, C. ; Ummenhofer, C.C. ; Tiger, B.H. ; Denniston, R.F. ( January 2022 , Transactions American Geophysical Union)

   Nepal is positioned at the intersection of the Indian Summer Monsoon (ISM) and Subtropical Jet (SJ). Although the ISM is responsible for ~two thirds of annual precipitation, the SJ supplies precipitation in the winter and spring, with the jet migrating southwards to the subcontinent beginning in October and reaching its most southerly position in May before moving northward in June. Using the state-of-the-art Community Earth System Model Last Millennium Ensemble, we investigated potential drivers of the latitudinal position of the SJ over Nepal (referred to as the Himalayan Jet) between 850-2005 CE. The Himalayan Jet Latitude [HJL] is defined as the latitude with the highest wind speed at 200 mb for every longitude containing Nepal (Thapa et al., 2022). In order to identify dominant periodicities in HJL positioning, power-spectral-density analyses were used. For the purpose of evaluating drivers of HJL position, we identified years with a northward or southward displaced HJL, defined as being two standard deviations above or below the average annual HJL position, and used anomaly composites of precipitation, winds (upper- and lower-level), sea surface temperature, moisture transport (lower-level at 850mb), and geopotential height (upper-level at 200mb). Our analyses seem to point toward a link between HJL and the phases of the El Niño Southern Oscillation and Indian Ocean Dipole (IOD): Southerly HJL years often occur during years with an El Niño and a positive IOD event. Northerly HJL years often occur when a Rossby wave train appears to be present over Nepal, indicative of a remote teleconnection. We provide an initial quantification of the physical mechanics of how these climate modes in the Pacific, Indian, and Atlantic Oceans, including remote teleconnections transmitted via atmospheric Rossby Waves, affect HJL. These climate model simulation results are also compared with a sub-decadally-resolved, precisely-dated, composite stalagmite isotope record of ISM variability from Siddha Baba cave, central Nepal. 

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4. The climate variability trio: stochastic fluctuations, El Niño, and the seasonal cycle

   https://doi.org/10.1186/s40562-023-00305-7  

   Stuecker, Malte F. ( November 2023 , Geoscience Letters)

   Climate variability has distinct spatial patterns with the strongest signal of sea surface temperature (SST) variance residing in the tropical Pacific. This interannual climate phenomenon, the El Niño-Southern Oscillation (ENSO), impacts weather patterns across the globe via atmospheric teleconnections. Pronounced SST variability, albeit of smaller amplitude, also exists in the other tropical basins as well as in the extratropical regions. To improve our physical understanding of internal climate variability across the global oceans, we here make the case for a conceptual model hierarchy that captures the essence of observed SST variability from subseasonal to decadal timescales. The building blocks consist of the classic stochastic climate model formulated by Klaus Hasselmann, a deterministic low-order model for ENSO variability, and the effect of the seasonal cycle on both of these models. This model hierarchy allows us to trace the impacts of seasonal processes on the statistics of observed and simulated climate variability. One of the important outcomes of ENSO’s interaction with the seasonal cycle is the generation of a frequency cascade leading to deterministic climate variability on a wide range of timescales, including the near-annual ENSO Combination Mode. Using the aforementioned building blocks, we arrive at a succinct conceptual model that delineates ENSO’s ubiquitous climate impacts and allows us to revisit ENSO’s observed statistical relationships with other coherent spatio-temporal patterns of climate variability—so called empirical modes of variability. We demonstrate the importance of correctly accounting for different seasonal phasing in the linear growth/damping rates of different climate phenomena, as well as the seasonal phasing of ENSO teleconnections and of atmospheric noise forcings. We discuss how previously some of ENSO’s relationships with other modes of variability have been misinterpreted due to non-intuitive seasonal cycle effects on both power spectra and lead/lag correlations. Furthermore, it is evident that ENSO’s impacts on climate variability outside the tropical Pacific are oftentimes larger than previously recognized and that accurately accounting for them has important implications. For instance, it has been shown that improved seasonal prediction skill can be achieved in the Indian Ocean by fully accounting for ENSO’s seasonally modulated and temporally integrated remote impacts. These results move us to refocus our attention to the tropical Pacific for understanding global patterns of climate variability and their predictability.

    

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5. Fully Bayesian Spectral Methods for Imaging Data

   https://doi.org/10.1111/biom.12782  

   Reich, Brian J. ; Guinness, Joseph ; Vandekar, Simon N. ; Shinohara, Russell T. ; Staicu, Ana-Maria ( September 2017 , Biometrics)

   Medical imaging data with thousands of spatially correlated data points are common in many fields. Methods that account for spatial correlation often require cumbersome matrix evaluations which are prohibitive for data of this size, and thus current work has either used low-rank approximations or analyzed data in blocks. We propose a method that accounts for nonstationarity, functional connectivity of distant regions of interest, and local signals, and can be applied to large multi-subject datasets using spectral methods combined with Markov Chain Monte Carlo sampling. We illustrate using simulated data that properly accounting for spatial dependence improves precision of estimates and yields valid statistical inference. We apply the new approach to study associations between cortical thickness and Alzheimer's disease, and find several regions of the cortex where patients with Alzheimer's disease are thinner on average than healthy controls.

    

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