caa a22 4500 60547107X CHVBK 20210128100331.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00382-014-2310-6 doi (NATIONALLICENCE)springer-10.1007/s00382-014-2310-6 Comparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau [Elektronische Daten] [Qinglong You, Jinzhong Min, Wei Zhang, Nick Pepin, Shichang Kang] Precipitation is a critical component of the water balance, and hence its variability is critical for cryospheric and climate change in the Tibetan Plateau (TP). Mean annual and seasonal precipitation totals are compared between gridded observations interpolated to a high resolution (0.5°×0.5°) and multiple reanalysis type-datasets during 1979-2001. The latter include two NCEP reanalyses (NCEP1 and NCEP2), two European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-40 and ERA-Interim), three modern reanalyses [the twentieth century reanalysis (20century), MERRA and CFSR] and three merged analysis datasets (CMAP1, CMAP2 and GPCP). Observations show an increase in mean precipitation from the northwestern to the southeastern (SE) regions of the TP which are divided by an isohyet of 400mm, and overall trends during the studied period are positive. Compared with observations, most of the datasets (NCEP1, NCEP2, CMAP1, CMAP2, ERA-Interim, ERA-40, GPCP, 20century, MERRA and CFSR) can both broadly capture the spatial distributions and identify temporal patterns and variabilities of mean precipitation. However, most multi-datasets overestimate precipitation especially in the SE where summer convection is dominant. There remain substantial disagreements and large discrepancies in precipitation trends due to differences in assimilation systems between datasets. Taylor diagrams are used to show the correlation coefficients, standard deviation, and root-mean-square difference of precipitation totals between interpolated observations and assimilated values on an annual and seasonal basis. Merged analysis data (CMAP1 and CMAP2) agree with observations more closely than reanalyses. Thus not all datasets are equally biased and choice of dataset is important. Springer-Verlag Berlin Heidelberg, 2014 Precipitation nationallicence Multi-datasets nationallicence Observation nationallicence Tibetan Plateau nationallicence You Qinglong Earth System Modelling Center (ESMC), Nanjing International Academy of Meteorological Sciences (NIAMS), Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044, Nanjing, China aut Min Jinzhong Earth System Modelling Center (ESMC), Nanjing International Academy of Meteorological Sciences (NIAMS), Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044, Nanjing, China aut Zhang Wei Earth System Modelling Center (ESMC), Nanjing International Academy of Meteorological Sciences (NIAMS), Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044, Nanjing, China aut Pepin Nick Department of Geography, University of Portsmouth, Portsmouth, UK aut Kang Shichang State Key Laboratory of Cryospheric Science, Chinese Academy of Sciences, 730000, Lanzhou, China aut Climate Dynamics Springer Berlin Heidelberg 45/3-4(2015-08-01), 791-806 0930-7575 45:3-4<791 2015 45 382 https://doi.org/10.1007/s00382-014-2310-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00382-014-2310-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- You Qinglong Earth System Modelling Center (ESMC), Nanjing International Academy of Meteorological Sciences (NIAMS), Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044, Nanjing, China aut NATIONALLICENCE 700 1- Min Jinzhong Earth System Modelling Center (ESMC), Nanjing International Academy of Meteorological Sciences (NIAMS), Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044, Nanjing, China aut NATIONALLICENCE 700 1- Zhang Wei Earth System Modelling Center (ESMC), Nanjing International Academy of Meteorological Sciences (NIAMS), Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044, Nanjing, China aut NATIONALLICENCE 700 1- Pepin Nick Department of Geography, University of Portsmouth, Portsmouth, UK aut NATIONALLICENCE 700 1- Kang Shichang State Key Laboratory of Cryospheric Science, Chinese Academy of Sciences, 730000, Lanzhou, China aut NATIONALLICENCE 773 0- Climate Dynamics Springer Berlin Heidelberg 45/3-4(2015-08-01), 791-806 0930-7575 45:3-4<791 2015 45 382