On the linkage between changes in cloud cover and precipitation extremes over Central India
Graphical abstract
Figure Scatterplot between brightness temperature (BT) and rainfall.
Introduction
Clouds play a crucial role in atmospheric circulation and the hydrological cycle. Changes in cloud cover associated with climate change remain one of the most challenging aspects of predicting future climate change. Many previous investigations have analyzed changes in cloud cover over different parts of the globe (Henderson-Sellers, 1992; Karl et al., 1993, 1995; Croke et al., 1999; Wylie et al., 2005; Warren et al., 2007; Tang and Leng, 2013). Few of these studies show an increase in high cloud cover and decrease in low cloud cover particularly over tropics. Worldwide there are extensive changes in precipitation extremes (Goswami et al., 2006; Lau and Wu, 2007; Liu et al., 2009; Shiu et al., 2012; Villarini et al., 2013; Mishra and Liu, 2014). Lau and Wu (2011) investigated the climatological characteristics of tropical rain and cloud systems over oceanic region in the context of changes in sea surface temperature using Tropical Rainfall Measuring Mission (TRMM) data. However, limited swath over a region from TRMM sensors may miss many convective clouds between two passes over the region. So, possibilities of errors increase in use of polar satellite (TRMM) data to study cloud based climatology especially in the case of short lived convective clouds. Morever, Lau and Wu (2011) did not relate the changes in precipitation to the changes in cloud cover in their study. In fact, no effort has made in past to relate the changes in cloud cover to changes in precipitation as a function of increase in global temperature. This would seem to be a particularly relevant endeavor, given the importance of close association of convective clouds with heavy precipitation and severe weather extremes and their role in the hydrological and energy exchange cycle (Sikka and Gadgil, 1980; Websters and Stephens, 1980), especially during the SW monsoon season. Convective clouds are responsible for heavy precipitation and influence radiative balance significantly and play an important role in moisture transportation in the upper troposphere (Hong et al., 2008).
The aim of the present study is to explore the linakge between changes in cloud cover and extreme precipitation from 18 years (1998–2015) of satellite data. The short period is perceived to be inadequate to explore the relation between long term changes in precipitation and cloud cover, however this study represents best possible way to use satellite data to investigate the relationship between changes in cloud cover and precipitation extremes in the context of climate change. Data from the same geostationary satellite to study cloud cover are free from sampling errors, limited spatial coverage and temporal homogeneity (Bellerby and Sun, 2005; Zeweldi and Gebremichael, 2009). Moreover, finer resolution of the satellite data used in the study helps to identify localized convective systems. Previous studies relating cloud covers and precipitation extremes used observational and model reanalysis. Observational data are limited to few locations while reanalysis data has very coarse resolution and thus can not detect localized convective clouds. Thus, the present analysis will rely on TRMM precipitation. In particular, TRMM-3B42 precipitation product (daily accumulated) has shown reasonably good accuracy over India (Mishra et al., 2010; Prakash et al., 2016; Beria et al., 2017) during SW monsoon season. However, precipitation is under/over-estimated over land/ocean due to correction of excessive orography precipitation, under estimation of low precipitation (over arid regions) and calibration issues (Beria et al., 2017; Liu, 2016).
The paper is organised in 4 sections. Section 1 describes the data products and methods. Sections 2 explores the relation between precipitation and clouds Section 3 relates the changes in cloud cover to those in precipitation extremes. Concluding remarks are included in Section 4.
Section snippets
Data, study area and methods
Brightness temperature data at 11 μm from Meteosat is used to study the changes in cloud cover for the years 1998–2015. Meteosat 7 data from Meteosat First Generation (MFG) has been used in present study. It provides thermal infrared images at half-hourly intervals, with a spatial resolution of 4 km.
The TRMM Multisatellite Precipitation Analysis (TMPA)—3B42 version 7 (V7) dataset is precipitation estimates which is designed to combine precipitation estimates from various satellite systems as
Rainfall vs brightness temperature
The major challenge in exploring relationship between rainfall and cloud is to distinguish non-rainy cirrus from deep convective clouds. To delineate cirrus clouds, a technique developed by Adler and Negri (1988) is used in this study. Description of which is given below:
A brightness temperature gradient (ΔTb) and slope (S) are calculated for each local brightness temperature minimum in a 1°×1° grid box. The terms ΔTb and S are given by Eqs. (1) and (2), respectively:ΔTb = Tbavg – Tbmin, and
Relation between changes in cloud cover and precipitation
Coverage of ground based observation system to monitor the cloud cover changes are not spatially and temporally uniform (Mahrooghy et al., 2011). Moreover, the occurrence of high level clouds can be biased in the presence of low level clouds as low level clouds obscure the sky. Brightness temperature information from geostationary satellites can be used to classify the clouds into low, mid and high level clouds (Roca et al., 2002; Mishra et al., 2010). In this study, clouds are classified based
Summary and conclusion
Satellite data (1998–2015) over Central Indian region is analyzed to explore the linkage between changes in cloud cover and precipitation extremes. These changes in cloud cover are also linked to the changes in precipitation extremes. The results presented in this paper point to increase in heavy precipitation and decrease in bottom 30% precipitation. Significant increase in convective cloud cover and decrease in low cloud cover is also reported in this study.
A good linkage between increases in
Acknowledgements
Funding for this work from MoES under grant MoES/16/27/2014-RDEAS and SERB DST under grant SR/FTP/ES-116/2014 is thankfully acknowledged. Meteosat data from EUMETSET used in this study is acknowledged.
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