Implication of data uncertainty in the detection of surface radiation trends and observational evidence of renewed solar dimming over India

Abstract

Analysis of the daily surface solar radiation (SSR) data over the 12 stations spread across India revealed that the data have many missing values and significant variability, which induce large uncertainties in the seasonal and annual means. Since the number of missing values and data variability change over time, ignoring them in the trend analysis, as done in most previous studies, may lead to erroneous results. We propose a method to incorporate the data uncertainty in the trend analysis and compare it with the traditional method, that ignores uncertainty, using the synthetic data with a known trend and having a different percentage of missing values. The proposed method is able to capture the trends in more than 85% of the cases, whereas the traditional method does it for less than 70% of the cases. Analysis of the SSR data by the proposed method revealed a renewed stronger solar dimming of about − 45 W/m2 per decade over the last decade (2006–2015) at all the 12 stations. Analysis of an independent satellite-derived SSR data from the Breathing Earth System Simulator (generated using the MODIS satellite data) also showed the significantly decreasing trends of about − 20 W/m2 per decade in the annual SSR over the entire India. The SSR proxies (sunshine duration and diurnal temperature range) and the wind speed also exhibited the trends consistent with the renewed dimming and suggest that it can be attributed to the increasing aerosol concentration over India in the recent decade.