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Inconsistent changes in global precipitation seasonality in seven precipitation datasets

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Abstract

Changes in precipitation seasonality or redistribution of precipitation could exert significant influences on regional water resources availability and the well-being of the ecosystem. However, due to the nonuniform distribution of precipitation stations and intermittency of precipitation, precise detection of changes in precipitation seasonality on the global scale is absent. This study identifies and inter-compares trends in precipitation seasonality within seven precipitation datasets during the past three decades, including two gauge-based datasets derived from the Climatic Research Unit (CRU) and the Global Precipitation Climatology Centre (GPCC), one remote sensing-retrieval obtained from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), three reanalysis datasets obtained from National Centers for Environmental Prediction reanalysis II (NCEP2), European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and Modern Era Reanalysis for Research and Applications Version 2 (MERRA2), and one precipitation dataset merged from above three types, Multi-Source Weighted Ensemble Precipitation Version 1.2 (MSWEP_V1.2). Values of two indices representing the precipitation seasonality, the normal seasonality index (SI) and the dimensionless seasonality index (DSI), are estimated for each land grid in each precipitation dataset. The results show that DSI is more sensitive to changes in the temporal distribution of precipitation as it considers both annual amount and monthly fluctuations of precipitation, compared to SI that only considers monthly fluctuations of precipitation. There are large differences in precipitation seasonality at annual and climatologic scales between precipitation datasets for both SI and DSI. Within the seven precipitation datasets, PERSIANN-CDR SI and DSI show high precipitation seasonality while CRU SI, and ERA-Interim and MERRA2 DSI show the low precipitation seasonality in all continental regions. During 1988–2013, PERSIANN-CDR, NCEP2 and ERA-Interim show more widespread, statistically significant trends in precipitation seasonality than other four precipitation datasets. PERSIANN-CDR and NCEP2 show statistically significant decreases in SI over Middle East and Central Asia, while ERA-Interim, MERRA2 and MSWEP_V1.2 SI increase over Central and South Africa. Increases in SI over the most of South America are significant. Regions of Canada/Greenland/Iceland, East and South Africa show significant increases in precipitation seasonality, while South Europe/Mediterranean and Central Africa show significant decreases in precipitation seasonality in most datasets. Although time series of seasonality indices values fluctuate correlatively in recent three decades, there are no regions on which all precipitation datasets show a consistent, statistically significant, positive or negative trend in indices of precipitation seasonality. These inconsistent changes in precipitation seasonality within various precipitation datasets imply the importance of choosing dataset when studying changes in regional precipitation seasonality.

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adapted from the https://www.ipcc-data.org/guidelines/pages/ar5_regions.html. Regions filled with red color are selected for regional analyses in this study

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Acknowledgements

The analysis was financially supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 51809295, 91547108 and 51779279), the Guangzhou Science and Technology Plan Project (Grant No. 201904010097) and the National Key Research and Development Program of China (Grant Nos.2017YFC0405900 and 2016YFC0401300). The CRU TS v. 4.03 data was download from https://crudata.uea.ac.uk/cru/data/hrg/. GPCC data was downloaded from http://www.cgd.ucar.edu/cas/catalog/surface/precip/gpcc.html. NCEP-DOE-Reanalysis 2 data was downloaded from ftp://ftp.cdc.noaa.gov/. ECMWF ERA-Interim reanalysis data was downloaded from http://www.ecmwf.int/. WFDEI reanalysis data was downloaded from https://rda.ucar.edu/datasets/ds314.2/; PERSIANN-CDR remote sensing retrieval data was downloaded from https://chrsdata.eng.uci.edu/, and MSWEP_V1.2 data was downloaded from http://www.gloh2o.org/.

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Authors and Affiliations

  1. School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, People’s Republic of China

    Xuezhi Tan & Bingjun Liu

  2. Center of Water Resources and Environment, Sun Yat-sen University, Guangzhou, 510275, People’s Republic of China

    Xuezhi Tan, Yi Wu, Bingjun Liu & Shiling Chen

  3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, People’s Republic of China

    Xuezhi Tan & Bingjun Liu

  4. School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, People’s Republic of China

    Yi Wu & Shiling Chen

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Tan, X., Wu, Y., Liu, B. et al. Inconsistent changes in global precipitation seasonality in seven precipitation datasets. Clim Dyn 54, 3091–3108 (2020). https://doi.org/10.1007/s00382-020-05158-w

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