Harmful Algal Blooms lead to multi-billion-dollar losses in the United States due to shellfish closures, fish mortalities, and reluctance to consume seafood. Therefore, an improved early seasonal prediction of harmful algal blooms severity is important. Conventional methods for harmful algal blooms prediction using nutrient loading as the primary driver have been found to be less accurate during extreme bloom years. Here we show that a machine learning approach using observed nutrient loading, and large-scale climate indices can improve the harmful algal blooms prediction in Lake Erie. Moreover, the seasonal prediction of harmful algal blooms can be completed by early June, before the expected peak in harmful algal bloom activity from July to October. This improved early seasonal prediction can provide timely information to policymakers for adopting proper planning and mitigation strategies such as restrictions in harvesting and help in monitoring toxins in shellfish to keep contaminated products off the market.

This study presents a comprehensive analysis of extreme events, especially drought and wet events, spanning over the past years, evaluating their trends over time. An investigation of future projections under various scenarios such as SSP-126, SS-245, and SSP-585 for the near (2023–2048), mid (2049–2074), and far future (2075–2100) using the bias-corrected Coupled Model Intercomparisons Project 6 (CMIP6) multi-model ensemble method was also performed. The Standard Precipitation Index (SPI), a simple yet incredibly sensitive tool for measuring changes in drought, is utilized in this study, providing a valuable assessment of drought conditions across multiple timescales. The historical analysis shows that there is a significant increase in drought frequency in subdivisions such as East MP, Chhattisgarh, East UP, East Rajasthan, Tamil Nadu, and Rayalaseema over the past decades. Our findings from a meticulous examination of historical rainfall trends spanning from 1951 to 2022 show a noticeable decline in rainfall across various regions such as Uttar Pradesh, Chhattisgarh, Marathwada, and north-eastern states, with a concurrent increase in rainfall over areas such as Gujarat, adjoining regions of West MP and East Rajasthan, and South Interior Karnataka. The future projection portrays an unpredictable pattern of extreme events, including droughts and wet events, with indications that wet frequency is set to increase under extreme SSP scenarios, particularly over time, while highlighting the susceptibility of the northwest and south peninsula regions to a higher incidence of drought events in the near future. Analyzing the causes of the increase in drought frequency is crucial to mitigate its worst impacts, and recent experiences of drought consequences can help in effective planning and decision-making, requiring appropriate mitigation strategies in the vulnerable subdivisions.

Estimating ocean biogeochemistry (BGC) parameters in Earth System Models is challenging due to multiple error sources and interlinked parameter sensitivities. Reducing the temperature and salinity bias in the ocean physical component of the Norwegian Earth System Model (NorESM) diminishes the BGC state bias at intermediate depth but leads to a greater bias increase near the surface. This suggests that BGC parameters are tuned to compensate for the physical ocean model biases. We successfully apply the iterative ensemble smoother data assimilation technique to estimate BGC parameters in NorESM with reduced bias in its physical ocean component. We estimate BGC parameters based on the monthly climatological error of nitrate, phosphate, and oxygen in a coupled reanalysis of NorESM that assimilates observed monthly climatology of temperature and salinity. First, we compare the performance of globally uniform and spatially varying parameter estimations. Both approaches reduce BGC bias obtained with default parameters, even for variables not assimilated in the parameter estimation (e.g., fluxes and primary production). While spatial parameter estimation performs locally best, it also increases biases in areas with few observations, and overall performs poorer than global parameter estimation. A second iteration further reduces the bias in the near‐surface BGC with global parameter estimation. Finally, we assess the performance of global estimated parameters in a 30‐year coupled reanalysis produced by assimilating time‐varying temperature and salinity observations. This reanalysis reduces error by 10%–20% for phosphate, nitrate, oxygen, and dissolved inorganic carbon compared to a reanalysis done with default parameters.

Macroscopic cyanobacterial biofilms were collected from alluvial plain soils and estuarine mangrove soils representing the Lower Gangetic Plains of South East Asia (India). The composition of the biofilms was investigated using light microscopy and field emission scanning electron microscopy of collected samples. In this study four simple trichal non-heterocytous morphotypes were found to be unique. Out of four, three morphotypes clearly showed differences with respect to described taxa as based on most recent taxonomic classification and possibly represent new report from the Indian subcontinent. One morphotype was successfully established under culture conditions and described as Leptolyngbya indica sp. nov. isolated from the alluvial arsenic affected rice field soil. This study provides vital information on morphotypic diversity of Cyanobacteria from specific biotopes which can contribute key information on their biogeography and potential application in green remediation.