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1. Inter‐Basin Versus Intra‐Basin Sea Surface Temperature Forcing of the Western North Pacific Subtropical High's Westward Extensions

   https://doi.org/10.1029/2023JD040429  

   Jones, Jhordanne J; Chavas, Daniel R; Johnson, Zachary F (August 2024, Journal of Geophysical Research: Atmospheres)

   Abstract Zonal extensions of the Western Pacific subtropical high (WPSH) strongly modulate extreme rainfall activity and tropical cyclone (TC) landfall over the Western North Pacific (WNP) region. These zonal extensions are primarily forced on seasonal timescales by inter‐basin zonal sea surface temperature (SST) gradients. However, despite the presence of large‐scale zonal SST gradients, the WPSH response to SSTs varies from year to year. In this study, we force the atmosphere‐only NCAR Community Earth System Model version 2 simulations with two real‐world SST patterns, both featuring the large‐scale zonal SST gradient characteristic of decaying El Niño‐developing La Niña summers. For each of these patterns, we performed four experimental sets that tested the relative contributions of the tropical Indian Ocean, Pacific, and Atlantic basin SSTs to simulated westward extensions over the WNP during June–August. Our results indicate that the subtle differences between the two SST anomaly patterns belie two different mechanisms forcing the WPSH's westward extensions. In one SST anomaly pattern, extratropical North Pacific SST forcing suppresses the tropical Pacific zonal SST gradient forcing, resulting in tropical Atlantic and Indian Ocean SSTs being the dominant driver. The second SST anomaly pattern drives a similar westward extension as the first pattern, but the underlying SST gradient driving the WPSH points to intra‐basin forcing mechanisms originating in the Pacific. The results of this study have implications for understanding and predicting the impact of the WPSH's zonal variability on tropical cyclones and extreme rainfall over the WNP. 

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2. Climate and the Global Famine of 1876–78

   https://doi.org/10.1075/JCLI-D-18-0159.1  

   Singh, D. (September 2018, Journal of climate)

   From 1875 to 1878, concurrent multiyear droughts in Asia, Brazil, and Africa, referred to as the Great Drought, caused widespread crop failures, catalyzing the so-called Global Famine, which had fatalities exceeding 50 million people and long-lasting societal consequences. Observations, paleoclimate reconstructions, and climatemodel simulations are used 1) to demonstrate the severity and characterize the evolution of drought across different regions, and 2) to investigate the underlying mechanisms driving its multiyear persistence. Severe or record-setting droughts occurred on continents in both hemispheres and in multiple seasons, with the ‘‘Monsoon Asia’’ region being the hardest hit, experiencing the single most intense and the second most expansive drought in the last 800 years. The extreme severity, duration, and extent of this global event is associated with an extraordinary combination of preceding cool tropical Pacific conditions (1870–76), a record-breaking El Niño (1877–78), a record strong Indian Ocean dipole (1877), and record warm North Atlantic Ocean (1878) conditions. Composites of historical analogs and two sets of ensemble simulations—one forced with global sea surface temperatures (SSTs) and another forced with tropical Pacific SSTs—were used to distinguish the role of the extreme conditions in different ocean basins. While the drought in most regions was largely driven by the tropical Pacific SST conditions, an extreme positive phase of the Indian Ocean dipole and warm NorthAtlantic SSTs, both likely aided by the strong El Niño in 1877–78, intensified and prolonged droughts in Australia and Brazil, respectively, and extended the impact to northern and southeastern Africa. Climatic conditions that caused the Great Drought and Global Famine arose from natural variability, and their recurrence, with hydrological impacts intensified by global warming, could again potentially undermine global food security. 

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3. Observed Relative Contributions of Anomalous Heat Fluxes and Effective Heat Capacity to Sea Surface Temperature Variability

   https://doi.org/10.1029/2023GL103165  

   Takahashi, Naoya; Richards, Kelvin J.; Schneider, Niklas; Stuecker, Malte F.; Annamalai, Hariharasubramanian; Nonaka, Masami (September 2023, Geophysical Research Letters)

   Sea surface temperatures (SSTs) vary not only due to heat exchange across the air‐sea interface but also due to changes in effective heat capacity as primarily determined by mixed layer depth (MLD). Here, we investigate seasonal and regional characteristics of the contribution of MLD anomalies to the month‐to‐month variability of SST using observational datasets. First, we propose a metric called Flux Divergence Angle, which can quantify the relative contributions of surface heat fluxes and MLD anomalies to SST variability. Using this metric, we find that MLD anomalies tend to amplify SST anomalies in the extra‐tropics, especially in the eastern ocean basins, during spring and summer. In contrast, MLD anomalies tend to suppress SST anomalies in the eastern tropical Pacific during December‐January‐February. This paper provides the first global picture of the observed importance of MLD anomalies to the local SST variability. 

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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. (December 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. Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model

   https://doi.org/10.1088/1748-9326/adf864  

   Zhang, Gan; Rao, Megha; Yuval, Janni; Zhao, Ming (August 2025, Environmental Research Letters)

   Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using neural general circulation model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ∼8 min with a single graphics processing unit while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July–November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990–2023 (r= ∼0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observed sub-basin TC tracks (p< 0.1) and basin-wide accumulated cyclone energy (ACE) (p< 0.01) of the North Atlantic and North Pacific basins. These findings highlight the promise of leveraging ML models with physical insights to model TC risks and deliver seamless weather-climate predictions. 

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