Investigating changes in cloud cover using the long‐term record of precipitation extremes

Abstract

Clouds are important in a climate system due to their impact on the radiation budget and precipitation. Changes in cloud cover are hard to infer due to lack of reliable long‐term cloud datasets. An attempt is made in this study to investigate the changes in cloud cover using the relationship between precipitation extremes and clouds. Heavy precipitation is associated with convective clouds while light precipitation occurs mostly with low clouds. The Global Precipitation and Climatology Project (GPCP) precipitation data are used in this study to relate the changes in heavy and light precipitation with those in convective and low cloud cover, respectively, from the Visible and Infrared Scanner data of the Tropical Rainfall Measuring Mission available from 1998 to 2014. Slopes were derived between changes in precipitation extremes and cloud cover using monthly data. These slopes were applied to long‐term trends of precipitation extremes from GPCP data (1979–2016) to infer long‐term changes in convective and low cloud cover. Cloud cover derived using this technique shows substantial inter‐monthly and inter‐annual variability. The results show an increase of about 4.48 ± 1.9% per decade in convective cloud cover over tropical ocean (25 ° S–25 ° N). This is consistent with National Oceanic and Atmospheric Administration (NOAA) High Resolution Infrared Radiometer Sounder (HIRS) observations, which show an increase of about 5.04 ± 2.18% per decade in convective cloud cover over tropical ocean. In the present study an increasing trend of about 5.54 ± 2.07% in convective cloud cover over land (20 °–60 ° N) is also derived, which is comparable to the NOAA HIRS trend of about 6.57 ± 2.53% increase per decade. Decreases of about 3.52 ± 1.69% and 4.26 ± 1.48% per decade in low cloud cover over tropical ocean and northern mid‐latitude land, respectively, are reported and are consistent with decreases of about 3.05 ± 1.68% and 5.31 ± 2.22% from NOAA HIRS data over those regions.

Investigation of Springtime Cloud Influence on Regional Climate and Its Implication in Runoff Decline in Upper Colorado River Basin

“…On the other hand, strong associations between COD and precipitation are found for all the 3 months ( R > 0.78), consistent with other studies, for example, (Halimi et al., 2017; Mishra, 2019). The close co‐variation of the two parameters is understandable because COD is proportionated to cloud thickness as a first approximation and the precipitation probability increases as the cloud thickness increases (Sakakibara, 1978).…”

Machine learning for total cloud cover prediction

“…[24,26,28]). Due to the direct connection to clouds [57], functionals of precipitation ensemble forecasts represent a natural choice for additional predictors. We here use the mean f PREC of the ECMWF 51-member precipitation forecast as additional covariate and investigate the performance of MLP, GBM and POLR approaches, showing the best forecast skill in Sect.…”

Synergetic Satellite Trend Analysis of Aerosol and Warm Cloud Properties ver Ocean and Its Implication for Aerosol‐Cloud Interactions

“…These satellite‐based AOD trends are generally consistent with previous studies based on atmospheric visibility (Che et al, 2007; Hu et al, 2017; Li et al, 2016) or ground‐based aerosol properties, such as PM 2.5 (Hasheminassab et al, 2014; Kim et al, 2015; Rattigan et al, 2016; Yang et al, 2018). Long‐term trends in cloud properties, such as liquid water path (LWP), cloud effective radius (CER), cloud optical thickness (COT), and cloud droplet number concentration (CDNC), have also been examined (e.g., Bennartz & Rausch, 2017; Elsaesser et al, 2017; Li et al, 2018; Manaster et al, 2017; McCoy et al, 2018; Mishra, 2019; Sreekanth, 2016; Stengel et al, 2017; Zhao et al, 2016).…”