Climate change effects on people’s livelihood

Generally climate is defined as the long-term average weather conditions of a particular place, region, or the world. Key climate variables include surface conditions such as temperature, precipitation, and wind. The Intergovernmental Panel on Climate Change (IPCC) broadly defined climate change as any change in the state of climate which persists for extended periods, usually for decades or longer (Allwood et al. 2014). Climate change may occur due to nature’s both internal and external processes. External process involves anthropogenic emission of greenhouse gases to the atmosphere, and volcanic eruptions. The United Nations Framework Convention on Climate Change (UNFCCC) made a distinction between climate change attributable to human contribution to atmospheric composition and natural climate variability. In its Article 1, the UNFCCC defines climate change as “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods” (United Nations 1992, p. 7)

Sensitivity of online coupled model to extreme pollution event over a mega city Delhi

Sensitivity of interactive Weather-Chemistry model has been examined to predict the air quality (1 and 3 days in advance) of Indian mega city Delhi during two identical extreme events of Diwali in 2012 and 2013. Analysis is conducted 3 days prior to 3 days later of Diwali day for both events to verify the rapid changes in fine particulate matter (PM2.5) due to widespread display of Diwali fireworks. The model successfully predicted the variability in PM2.5 during 2012 for the entire period of analysis with reasonable accuracy. Although model performed reasonably well until Diwali day in 2013 but it was unable to simulate rapid built up of PM2.5 (1500 μg−3 hourly average) during post Diwali day as it failed to capture unusual mid-night steep temperature gradient followed by a record lowering of boundary layer height. The predictability of the model and its limitation to micrometeorological processes are discussed in detail