Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran

This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1-G8). Synoptic data for the period 1995-2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020-2040), middle future (2040-2060), and far future (2060-2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson's correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995-2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by -74%. The projected maximum temperature showed an increase up to +8°C in highland climate regions. The minimum temperature revealed an increase up to +4°C in the Zagros mountains and decreased by -4°C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate.

Urban Emotion Sensing Beyond 'Affective Capture': Advancing Critical Interdisciplinary Methods

The use of mobile sensor methodologies in urban analytics to study 'urban emotions' is currently outpacing the science required to rigorously interpret the data generated. Interdisciplinary research on 'urban stress' could help inform urban wellbeing policies relating to healthier commuting and alleviation of work stress. The purpose of this paper is to address-through methodological experimentation-ethical, political and conceptual issues identified by critical social scientists with regards to emotion tracking, wearables and data analytics. We aim to encourage more dialogue between the critical approach and applied environmental health research. The definition of stress is not unambiguous or neutral and is mediated by the very technologies we use for research. We outline an integrative methodology in which we combine pilot field research using biosensing technologies, a novel method for identifying 'moments of stress' in a laboratory setting, psychometric surveys and narrative interviews on workplace and commuter stress in urban environments.