23STUDENT Unraveling the Widespread Asymmetric Trends of Maximum and Minimum Temperature in Coastal Regions of India and in the Continental United States

The intricate relationship between the oceans and the atmosphere establishes a dynamic cornerstone within the climate system. This dynamic connection is most notably witnessed through the profound influence of topography and proximity to surrounding seas on regional temperature profiles. The changes in the climate system can be attributed to meteorological indicators like the Diurnal Temperature Range (DTR), which reflects the difference between daily maximum and minimum temperatures. Changes in internal climatic elements, such as topography and land use, alongside external forces like ocean currents, wind patterns, and humidity, complicate DTR dynamics. Understanding and attributing these changes necessitate innovative methodologies beyond conventional linear trends. In this direction, our study focuses on understanding the evolution of DTR in the coastal realms of India and the continental United States. In our approach, we apply the non-stationary Multidimensional Ensemble Empirical Mode Decomposition (MEEMD) method, which is developed from Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD). MEEMD is used to deduce the presence of a trend in the variables, and Monte Carlo Simulation evaluates the significance of these trends. MEEMD eliminates the oscillatory component of a time series and reveals while preserving the slow-varying component. This approach does not need a functional form a priori and can extract the time series’ hidden non-linear and non-stationary nature. In MEEMD, a time series at a grid point is decomposed using EEMD into oscillatory components known as Intrinsic Mode Functions (IMF). The sequential elimination of IMFs produces residual, either monotonic or contains only one extremum, and cannot be further decomposed into an oscillatory component. The present work uses comprehensive datasets spanning a 69-year timeframe, encompassing near-surface air Temperature (1950-2019) and Sea Surface Temperature (SST) (1950-2019) records. We extract the non-linear trends embedded within these datasets by deploying the CRU 0.50 x 0.50 gridded temperature data and the ERA5 Reanalysis 0.25 x 0.25 hourly gridded SST data. Our preliminary examination shed light on distinct regional imprints of DTR trends. In contrast to its eastern and western coastal counterparts, the southern coast of India witnesses a decreasing DTR pattern. Similarly, the western coastal regions of the continental United States exhibit a decreasing DTR trend compared to their eastern counterparts. We suggest these trends originate from intricate, non-linear changes within Sea Surface Temperatures. Integral to our exploration is recognizing Sea Surface Temperature as a pivotal climatic parameter and unraveling the underlying mechanisms driving variations in the coastal regions.