In this study, changes in the spatial and temporal patterns of climate extreme indices were analyzed. Daily maximum and minimum air temperature, precipitation, and their association with climate change were used as the basis for tracking changes at 50 meteorological stations in Iran over the period . Sixteen indices of extreme temperature and 11 indices of extreme precipitation, which have been quality controlled and tested for homogeneity and missing data, are examined. Temperature extremes show a warming trend, with a large proportion of stations having statistically significant trends for all temperature indices. Over the last 15 years (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010), the annual frequency of warm days and nights has increased by 12 and 14 days/decade, respectively. The number of cold days and nights has decreased by 4 and 3 days/decade, respectively. The annual mean maximum and minimum temperatures averaged across Iran both increased by 0.031 and 0.059°C/decade. The probability of cold nights has gradually decreased from more than 20 % in 1975-1986 to less than 15 % in 1999-2010, whereas the mean frequency of warm days has increased abruptly between the first 12-year period (1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)) and the recent 12-year period (1999-2010) from 18 to 40 %, respectively. There are no systematic regional trends over the study period in total precipitation or in the frequency and duration of extreme precipitation events. Statistically significant trends in extreme precipitation events are observed at less than 15 % of all weather stations, with no spatially coherent pattern of change, whereas statistically significant changes in extreme temperature events have occurred at more than 85 % of all weather stations, forming strongly coherent spatial patterns.

Analysis of satellite-derived surface layer phytoplankton chlorophyll-a concentration (Chl-a) in the Bay of Bengal revealed an eastward shift of peak of Chl-a bloom during northeast monsoon period (November-March) from western to the eastern Bay. The winter Chl-a bloom in the western Bay is shorter duration ($1-2 months) while that in the eastern Bay is longer duration ($3-4 months). Unlike other oceans, the eastward bloom peak shift found in the Bay of Bengal is opposite to the direction of propagation of Rossby wave and seasonal mean zonal currents. During winter, sufficient light is available in the Bay of Bengal for phytoplankton growth. Herein, we studied the processes controlling the eastward winter bloom peak shift and the zonal depiction of bloom duration in the Bay of Bengal. There is no single dominant process that drives the eastward bloom peak shift everywhere in the Bay of Bengal. Rather, different physical processes operate in different regions. The physical mechanisms governing the eastward winter Chl-a bloom peak shift reported here include open ocean Ekman pumping, coastal upwelling, upwelling driven by Rossby wave, wind-induced vertical mixing, nutrients supply from river input, and westward advection through prevailing zonal currents.

As the phytoplanktons consume carbon dioxide, they significantly influence the global carbon cycle and thus, the global temperature by modifying sea surface temperature. Studies on the changes in chlorophyll–a (Chl-a) amount are therefore, key for understanding the changes in ocean productivity, global carbon budget and climate. Here, we report the cyclone-induced Chl-a blooms in the North Indian Ocean (NIO) using the ocean colour measurements from satellites for the past two decades (1997–2019). The average Chl-a concentration associated with cyclone-induced phytoplankton blooms is around 1.65 mg/m3, which is about 20–3000% higher than the average open ocean or pre-cyclone Chl-a levels, depending on the cyclones. In general, the phytoplankton bloom is inversely related to the translational speed (TS) of cyclones, as slower storms make intense Chl-a blooms. In addition to wind-induced upwelling and TS of cyclones, cold-core eddies also play a major role in enhancement of Chl-a when the cyclones encounter eddies on their track. It is observed that the cyclone-induced phytoplankton blooms are larger in the La Niña years than that in the El Niño and normal years. The amplitude of bloom is higher for the positive IOD years in Bay of Bengal, but for negative IOD years in Arabian Sea. Henceforth, this study provides new insights into the life cycle, seasonal changes, and magnitude of the cyclone-induced primary production, remote forcing and greenhouse mediated climate change in NIO.

Abstract. The trajectories' prediction of floating objects above the sea surface represents an important task in search and rescue (SAR) operations. In this paper we show how it is possible to estimate the most probable search area by means of a stochastic model, schematizing the shape of the object appropriately and evaluating the forces acting on it. The LEEWAY model,a Monte Carlo-based ensemble trajectory model, has been used; here, both statistical law to calculate the leeway and an almost deterministic law inspired by the boundary layer theory have been considered. The model is nested within the subregional hydrodynamic model TSCRM (Tyrrhenian Sicily Channel Regional Model) developed in the framework of PON-TESSA (Programma Operativo Nazionale; National Operative Program – TEchnology for the Situational Sea Awareness) project. The main objective of the work is to validate a new approach of leeway calculation that relies on a real person in water (PIW) event, which occurred in the Tyrrhenian Sea in July 2013. The results show that by assimilating a human body to a cylinder and estimating both the transition from laminar to turbulent boundary layer and the drag coefficients, it can be possible to solve a force balance equation, which allows the search area to be estimated with good approximation. This new point of view leads to the possibility of also testing the same approach for other different categories of targets, so as to overcome the limitations associated with the calculation of the leeway in the future by means of standard statistical law.