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1. 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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2. The Evolving Role of External Forcing in North Atlantic SST Variability over the Last Millennium

   https://doi.org/10.1175/JCLI-D-21-0338.1  

   Klavans, Jeremy M.; Clement, Amy C.; Cane, Mark A.; Murphy, Lisa N. (May 2022, Journal of Climate)

   Abstract Atlantic multidecadal variability (AMV) impacts temperature, precipitation, and extreme events on both sides of the Atlantic Ocean basin. Previous studies with climate models have suggested that when external radiative forcing is held constant, the large-scale ocean and atmosphere circulation are associated with sea surface temperature (SST) anomalies that have similar characteristics to the observed AMV. However, there is an active debate as to whether these internal fluctuations driven by coupled atmosphere–ocean variability remain influential to the AMV on multidecadal time scales in our modern, anthropogenically forced climate. Here we provide evidence from multiple large ensembles of climate models, paleoreconstructions, and instrumental observations of a growing role for external forcing in the AMV. Prior to 1850, external forcing, primarily from volcanoes, explains about one-third of AMV variance. Between 1850 and 1950, there is a transitional period, where external forcing explains one-half of AMV variance, but volcanic forcing only accounts for about 10% of that. After 1950, external forcing explains three-quarters of AMV variance. That is, the role for external forcing in the AMV grows as the variations in external forcing grow, even if the forcing is from different sources. When forcing is relatively stable, as in earlier modeling studies, a higher percentage of AMV variations are internally generated. 

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3. Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes

   https://doi.org/10.1073/pnas.2312573121  

   Bach, Eviatar; Krishnamurthy, V.; Mote, Safa; Shukla, Jagadish; Sharma, A. Surjalal; Kalnay, Eugenia; Ghil, Michael (April 2024, Proceedings of the National Academy of Sciences)

   Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction. 

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4. Investigating the Relative Contribution from Tropical Indo-Pacific SST to Asian Monsoon Precipitation Variability Using LIM

   https://doi.org/10.1175/JCLI-D-23-0657.1  

   Guderian, Erin; Han, Weiqing; Webster, Peter; Zhang, Lei; Di_Lorenzo, Emanuele (December 2024, Journal of Climate)

   Abstract A critical issue is determining the factors that control the year-to-year variability in precipitation over southern Asia. In this study, we employ a cyclostationary linear inverse model (CS-LIM) to quantify the relative contribution of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) to the interannual variability of the Asian monsoon, especially Indian summer monsoon rainfall (ISMR). Through a series of CS-LIM experiments, we isolate the impacts of the direct forcing from Pacific SSTAs, Indian Ocean SSTAs, and their interaction on Asian monsoon rainfall variability. Our results reveal distinct patterns of influence with the direct forcing from the Pacific (Indian) Ocean tending to enhance (reduce) the magnitude of precipitation variability, while the Indo-Pacific interaction acts to strongly damp the variability of Asian monsoon precipitation, especially over India. We further investigate these specific impacts on ISMR by analyzing the relationship between tropical Indo-Pacific SSTAs and the leading three empirical orthogonal functions (EOFs) of ISMR. The results from our CS-LIM experiments indicate that the direct forcing from El Niño–Southern Oscillation (ENSO) enhances the variability of the first and third EOFs, while the Indian Ocean SSTA opposes ENSO’s effects, which is consistent with previous studies. Our new results show that the tropical Indo-Pacific interaction strongly damps ISMR variability, which is due to the ENSO-induced Indian Ocean dipole (IOD) opposing the direct impacts from ENSO on ISMR. Additionally, reduced ENSO amplitude and duration associated with the Indo-Pacific interaction may also contribute to the damping effect on ISMR, but this requires further study to understand the relevant mechanisms. 

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5. An observational analysis of precipitation and deforestation age in the Brazilian Legal Amazon

   https://doi.org/10.1016/j.atmosres.2022.106122  

   Mu, Ye; Jones, Charles (March 2022, Atmospheric research)

   Rainfall in the Amazon is influenced by atmospheric circulation dynamics on multiple spatiotemporal scales. Anthropogenic influences such as deforestation, land-use changes, and global climate change are also critical factors in determining rainfall in South America. Modeling studies have projected a drier climate with the ongoing deforestation in the Amazon, but observational evaluation of the variability of rainfall and deforestation patterns has been limited. This study analyzes spatiotemporal trends in rainfall between 1981 and 2020 and relationships with deforestation age in the Brazilian Legal Amazon (BLA). An improved rainfall dataset is derived by calibrating the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data with observations from a rain gauge network in the BLA. Trend analysis is employed to identify significant changes in precipitation over the BLA. Satellite-based land cover data Mapbiomas and ET datasets are used to evaluate similar trends. While large spatial variability is observed, the results show coherent relationships between negative dry-season rainfall trends and old-age deforested areas. Deforestation aged up to a decade enhanced rainfall and older deforested regions have reduced rainfall during the dry season. These results suggest substantial changes in the hydroclimate of the BLA and increased vulnerability to future land cover change. 

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