Spatio-temporal analysis of HadCM3 GCM Climate projections using Parameterized MiSTIC framework - study of Elevation Dependent Warming across Peninsular India

Abstract

General Circulation Models (GCMs) aid in developing climate-resilient policies, preparing for extreme events and implementing governance and disaster management mitigation strategies. Thus, assessing GCMs’ climate forecasting capabilities is crucial to establishing the credibility and reliability of climate projections, facilitating well-informed decision-making and climate change readiness. The UK Met Office Hadley Centre Coupled Model, version 3 (HadCM3), is extensively evaluated in this study. HadCM3 is used to simulate global and regional climate patterns and has been crucial to climate change assessments. This thesis explores the spatio-temporal dynamics of temperature in contiguous Peninsular India by identifying regions of high & low variability, with an emphasis on Elevation-Dependent Warming (EDW). The central objective of this study is to improve our understanding of the attributes, patterns, trends, and fundamental factors that affect temperature variations across diverse spatial and temporal dimensions and the HadCM3 model’s ability to accurately represent this phenomenon. Through a 30-year analysis of observed and modelled minimum (Tmin) and maximum temperature (Tmax) data (1991-2020 and 1990-2019, respectively) over the study region, this study explores the relationship between elevation and warming trends. The observed and modelled Tmin and Tmax have been increasing at a rate of +0.13°C/decade and +0.17°C/decade for observed data & +0.43°C/decade and +0.45°C/decade for modelled data, whereas the observed and modelled diurnal temperature range (DTR) increased at +0.03°C /decade & +0.02°C/decade. The lapse rates of observed and modelled minimum and maximum temperatures are found to be positive, but the DTR lapse rate is found to be negative, indicating increasing DTR with elevation. Modelled data shows more pronounced trends (+6.344°C/km for Tmin, +5.954°C/km for Tmax, and -0.39°C/km for DTR) than observed temperature (+2.376°C/km for Tmin, +2.341°C/km for Tmax, and -0.193°C/km for DTR). This discrepancy suggests that the global average lapse rate (+6.5°C/km) underlines the modelled data, resulting in much higher lapse rates in the study region, which implies that the model uses a uniform lapse rate to model temperature and fails to account for the study region’s unique topographic temperature relationships. The study proposes the P-MiSTIC method, a multivariate extension of MiSTIC, to identify zones with spatio-temporal consistency and investigate these inconsistencies. This study uses Tmin and Tmax as P-MiSTIC inputs with weights of -1 and 1. Using MiSTIC, the study finds 12, 9, 8, and 4 spatio-temporally invariant zones for observed and modelled Tmin and Tmax, respectively. Importantly, the elevation-based change rates of variables for these zones (-2.57°C/km and -2.19°C/km for observed Tmin and Tmax, and -7.48°C/km and -6.67°C/km for modelled Tmin and Tmax) closely match the data-derived lapse rates. Using the P-MiSTIC method, 11 and 1 zones are identified for observed and modelled data, respectively, indicating that modelled data is consistent over the Peninsular region. DTR change rates for zones within observed temperature data increase with elevation, indicating elevation-dependent warming in specific study regions. In particular, the Western Himalayan region and the Karakoram region, with the highest elevations, drive the phenomenon in the study region with the Western Himalayan and Karakoram DTRs increasing at 0.19°C/decade and +0.28°C/decade over the study period. The GCM model’s DTR data shows a much slower increase of +0.02°C/decade in the study region. Using the P-MiSTIC method on three decade-wise subsets, only one zone is identified in modelled data across all decades, while observed data identifies 12, 11, and 9 zones for decades 1, 2, and 3. The elevation-based DTR change rates for the observed temperature zones are estimated at -0.03°C/km, -0.028°C/km, and +0.533°C/km for decades 1, 2, and 3. DTR trends change from decreasing to increasing as the study progresses from decades 1 and 2 to decade 3, suggesting elevation-dependent warming in the study region is accelerating. The Western Himalayas and the Karakoram experience considerable variations of DTR. In the Western Himalayas, the DTR decreases at -0.14°C/decade, -0.29°C/decade, and - 0.41°C/decade for decades 1, 2, and 3, while in the Karakoram, it increases at +0.11°C/decade, +0.13°C/decade, and +2.46°C/decade. However, the model’s DTR data shows a uniform increase of +0.02°C/decade across all decades, indicating the model’s uniform rate. By dividing the datasets into DJF, MAM, JJA, and SON seasons, the study examined seasonal spatio-temporal variations. Seasonal observations identified 11, 10, 7, and 8 zones where the diurnal temperature range (DTR) changed with elevation at -0.518°C/km, -0.089°C/km, 0.668°C/km, and 0.453°C/km for the four seasons. Western Himalayan and Karakoram regions continue to be identified by P-MiSTIC in all the seasons, indicating their distinct trends. In these zones, DTR values were lowest in JJA and highest in MAM. The DTR has been increasing in the Western Himalayan (0.28°C/decade, 0.34°C/decade, 0.22°C/decade, and 0.21°C/ decade) and Karakoram (0.26°C/decade for all seasons) over the study period. The Western Himalayas showed seasonal variations while the Karakoram region showed a relatively uniform trend. However, the modelled data yielded DJF, MAM, JJA, and SON zones of 3, 3, 3, and 2 with DTR trends of -0.539°C/km, 0.451°C/km, 4.689°C/km, and 1.737°C/km. This reinforces weak spatial variability but strong seasonal variability. Most zone-wise temporal DTR trends in the modelled data were insignificant, ranging from -0.07°C/decade to 0.05°C/decade except during SON, the Himalayan zone exhibited a 0.14°C/decade increase. Both datasets exhibited a seasonal cycle of DTR with minimum DTR values in JJA, followed by a gradual increase in SON. The elevation-based DTR trends showed the greatest increase in summer and a decrease in winter. Data-driven zoning analysis illustrates how zonal boundaries and temperatures vary through time. These zones allow researchers to examine contrasting trends in a study region that are challenging to extrapolate over large areas. The modelled data has been identified as a single zone in the entire study region, highlighting the lack of spatio-temporal variability in the modelled data for the region. The decrease in zonal variability over the past three decades is evident in the observed data zones, suggesting that this trend may be occurring in regions outside of the study area also. With a single zone throughout the study and decade-wise datasets, the modelled data indicated low spatio-temporal heterogeneity. The diagonal zone separation in the seasonal analysis reveals the model’s inherent discrepancies, which may be due to parameterizations and systematic errors. Despite its data-driven approach, the study’s identified zones correlate with natural patterns, indicating specific environmental and ecological conditions governing climate behaviour across the study region. P-MiSTIC consistently identified the Western Himalayan and Karakoram regions, confirming that elevation influences temperature patterns and also demonstrating that it is capable of identifying zones with unique spatio-temporal characteristics. The study shows that Elevation-Dependent Warming occurs in higher-elevation zones. JJA, where DTR values are low, has seen a faster warming rate in the past decade than in previous decades. The study illustrates how high-elevation Himalayan zones drive temperature variations. It stresses the importance of accounting for spatio-temporal variations when studying climate in complex topographies and the model’s inability to accurately capture these patterns. GCMs modelling the Indian subcontinent as a whole presents a challenge in enhancing climate patterns and comprehending its impacts.