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1. What Caused Recent Shifts in Tropical Pacific Decadal Sea‐Level Trends?

   https://doi.org/10.1029/2019JC015339  

   Piecuch, Christopher G. ; Thompson, Philip R. ; Ponte, Rui M. ; Merrifield, Mark A. ; Hamlington, Benjamin D. ( November 2019 , Journal of Geophysical Research: Oceans)

   Satellite altimetry reveals substantial decadal variability in sea levelζacross the tropical Pacific during 1993–2015. An ocean state estimate that faithfully reproduces the observations is used to elucidate the origin of these low‐frequency tropical Pacificζvariations. Analysis of the hydrostatic equation reveals that recent decadalζchanges in the tropical Pacific are mainly thermosteric in nature, related to changes in upper‐ocean heat content. A forcing experiment performed with the numerical model suggests that anomalous wind stress was an important driver of the relevant heat storage and thermosteric variation. Closed budget diagnostics further clarify that the wind‐stress‐related thermostericζvariation resulted from the joint actions of large‐scale ocean advection and local surface heat flux, such that advection controlled the budget over shorter, intraseasonal to interannual time scales, and local surface heat flux became increasingly influential at longer decadal periods. In particular, local surface heat flux was important in contributing to a recent reversal of decadalζtrends in the tropical Pacific. Contributions from local surface heat flux partly reflect damping latent heat flux tied to wind‐stress‐driven sea‐surface‐temperature variations.

    

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2. 100 Years of Progress in Understanding the Dynamics of Coupled Atmosphere–Ocean Variability

   https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0025.1  

   Battisti, David S. ; Vimont, Daniel J. ; Kirtman, Benjamin P. ( October 2019 , Meteorological Monographs)

   In situ observation networks and reanalyses products of the state of the atmosphere and upper ocean show well-defined, large-scale patterns of coupled climate variability on time scales ranging from seasons to several decades. We summarize these phenomena and their physics, which have been revealed by analysis of observations, by experimentation with uncoupled and coupled atmosphere and ocean models with a hierarchy of complexity, and by theoretical developments. We start with a discussion of the seasonal cycle in the equatorial tropical Pacific and Atlantic Oceans, which are clearly affected by coupling between the atmosphere and the ocean. We then discuss the tropical phenomena that only exist because of the coupling between the atmosphere and the ocean: the Pacific and Atlantic meridional modes, the El Niño–Southern Oscillation (ENSO) in the Pacific, and a phenomenon analogous to ENSO in the Atlantic. For ENSO, we further discuss the sources of irregularity and asymmetry between warm and cold phases of ENSO, and the response of ENSO to forcing. Fundamental to variability on all time scales in the midlatitudes of the Northern Hemisphere are preferred patterns of uncoupled atmospheric variability that exist independent of any changes in the state of the ocean, land, or distribution of sea ice. These patterns include the North Atlantic Oscillation (NAO), the North Pacific Oscillation (NPO), and the Pacific–North American (PNA) pattern; they are most active in wintertime, with a temporal spectrum that is nearly white. Stochastic variability in the NPO, PNA, and NAO force the ocean on days to interannual times scales by way of turbulent heat exchange and Ekman transport, and on decadal and longer time scales by way of wind stress forcing. The PNA is partially responsible for the Pacific decadal oscillation; the NAO is responsible for an analogous phenomenon in the North Atlantic subpolar gyre. In models, stochastic forcing by the NAO also gives rise to variability in the strength of the Atlantic meridional overturning circulation (AMOC) that is partially responsible for multidecadal anomalies in the North Atlantic climate known as the Atlantic multidecadal oscillation (AMO); observations do not yet exist to adequately determine the physics of the AMO. We review the progress that has been made in the past 50 years in understanding each of these phenomena and the implications for short-term (seasonal-to-interannual) climate forecasts. We end with a brief discussion of advances of things that are on the horizon, under the rug, and over the rainbow.

    

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3. Development of the Yearly Mode-1 M2 Internal Tide Model in 2019

   https://doi.org/10.1175/JTECH-D-21-0116.1  

   Zhao, Zhongxiang ( April 2022 , Journal of Atmospheric and Oceanic Technology)

   The yearly mode-1 M₂internal tide model in 2019 is constructed using sea surface height measurements made by six concurrent satellite altimetry missions:Jason-3,Sentinel-3A,Sentinel-3B,CryoSat-2,Haiyang-2A, andSARAL/AltiKa. The model is developed following a three-step procedure consisting of two rounds of plane wave analysis with a spatial bandpass filter in between. Prior mesoscale correction is made on the altimeter data using AVISO gridded mesoscale fields. The model is labeled Y2019, because it represents the 1-yr-coherent internal tide field in 2019. In contrast, the model developed using altimeter data from 1992 to 2017 is labeled MY25, because it represents the multiyear-coherent internal tide field in 25 years. Thanks to the new mapping technique, model errors in Y2019 are as low as those in MY25. Evaluation using independent altimeter data confirms that Y2019 reduces slightly less variance (∼6%) than MY25. Further analysis reveals that the altimeter data from five missions (withoutJason-3) can yield an internal tide model of almost the same quality. Comparing Y2019 and MY25 shows that mode-1 M₂internal tides are subject to significant interannual variability in both amplitude and phase, and their interannual variations are a function of location. Along southward internal tides from Amukta Pass, the energy flux in Y2019 is 2 times larger and the phase speed is about 1.1% faster. This mapping technique has been applied successfully to 2017 and 2018. This work demonstrates that yearly internal tides can be observed by concurrent altimetry missions and their interannual variations can be determined.

   This work is motivated to study the interannual variations of internal tides using observation-based yearly internal tide models from satellite altimetry. Previous satellite observations of internal tides are usually based on 25 years of altimeter data from 1993 to 2017. The yearly subsetted altimeter data are short, so that the resultant yearly models are overwhelmed by noise. A new mapping technique is developed and demonstrated in this paper. It paves a path to study the interannual and decadal variations of internal tides on a global scale and monitor the global ocean changes by tracking long-range internal tides.

    

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4. A Hydroclimatological Analysis of Precipitation in the Ganges–Brahmaputra–Meghna River Basin

   https://doi.org/10.3390/w10101359  

   Curtis, Scott ; Crawford, Thomas ; Rahman, Munshi ; Paul, Bimal ; Miah, M. ; Islam, M. ; Patel, Mohin ( October 2018 , Water)

   Understanding seasonal precipitation input into river basins is important for linking large-scale climate drivers with societal water resources and the occurrence of hydrologic hazards such as floods and riverbank erosion. Using satellite data at 0.25-degree resolution, spatial patterns of monsoon (June-July-August-September) precipitation variability between 1983 and 2015 within the Ganges–Brahmaputra–Meghna (GBM) river basin are analyzed with Principal Component (PC) analysis and the first three modes (PC1, PC2 and PC3) are related to global atmospheric-oceanic fields. PC1 explains 88.7% of the variance in monsoonal precipitation and resembles climatology with the center of action over Bangladesh. The eigenvector coefficients show a downward trend consistent with studies reporting a recent decline in monsoon rainfall, but little interannual variability. PC2 explains 2.9% of the variance and shows rainfall maxima to the far western and eastern portions of the basin. PC2 has an apparent decadal cycle and surface and upper-air atmospheric height fields suggest the pattern could be forced by tropical South Atlantic heating and a Rossby wave train stemming from the North Atlantic, consistent with previous studies. Finally, PC3 explains 1.5% of the variance and has high spatial variability. The distribution of precipitation is somewhat zonal, with highest values at the southern border and at the Himalayan ridge. There is strong interannual variability associated with PC3, related to the El Nino/Southern Oscillation (ENSO). Next, we perform a hydroclimatological downscaling, as precipitation attributed to the three PCs was averaged over the Pfafstetter level-04 sub-basins obtained from the World Wildlife Fund (Gland, Switzerland). While PC1 was the principal contributor of rainfall for all sub-basins, PC2 contributed the most to rainfall in the western Ganges sub-basin (4524) and PC3 contributed the most to the rainfall in the northern Brahmaputra (4529). Monsoon rainfall within these two sub-basins were the only ones to show a significant relationship (negative) with ENSO, whereas four of the eight sub-basins had a significant relationship (positive) with sea surface temperature (SST) anomalies in the tropical South Atlantic. This work demonstrates a geographic dependence on climate teleconnections in the GBM that deserves further study. 

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5. 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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