caa a22 4500 605471134 CHVBK 20210128100331.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00382-014-2336-9 doi (NATIONALLICENCE)springer-10.1007/s00382-014-2336-9 Influence of ENSO SSTs on the spread of the probability density function for precipitation and land surface temperature [Elektronische Daten] [Mingyue Chen, Arun Kumar] The impact of the interannual variations in ENSO SSTs on the spread of probability density function (PDF) for the seasonal mean of variables of societal relevance are analyzed based on a large set of the hindcasts from NCEP CFSv2. The study is focused on the analysis of global rainfall and 2-m temperature over land (T2m) for December-January-February (DJF) seasonal mean. For rainfall, the spatial distribution of the ENSO SST induced changes on the spread of PDF strongly resembles changes in the mean but have a smaller amplitude. Over the central-eastern equatorial Pacific, changes in the spread lead to a reduction in signal-to-noise ratio (SNR) during El Niño years while to an increase in the SNR during La Niña years. Over extratropics, year to year changes in the spread are relatively small. For T2m, the changes in spread have little systematic dependence on the ENSO SSTs and the amplitudes of the changes in spread are much smaller than corresponding changes in the ensemble mean. The results demonstrate small systematic year to year variations in the PDF spread, for example over extratropics for rainfall and over most of global land areas for T2m, and indicate that it might be a good practice in seasonal predictions to assume that the spread of seasonal means from year to year is constant and the skill in seasonal forecast information resides primarily in the shift of the first moment of the seasonal mean of the PDF. Springer-Verlag (outside the USA), 2014 Seasonal mean predictability nationallicence Probability density function nationallicence Rainfall nationallicence Land surface temperature nationallicence Chen Mingyue National Oceanic and Atmospheric Administration, Climate Prediction Center, National Centers for Environmental Prediction, RM#3011, W/NP52, 5830 University Research Court, 20740-3818, College Park, MD, USA aut Kumar Arun National Oceanic and Atmospheric Administration, Climate Prediction Center, National Centers for Environmental Prediction, RM#3011, W/NP52, 5830 University Research Court, 20740-3818, College Park, MD, USA aut Climate Dynamics Springer Berlin Heidelberg 45/3-4(2015-08-01), 965-974 0930-7575 45:3-4<965 2015 45 382 https://doi.org/10.1007/s00382-014-2336-9 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/s00382-014-2336-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Mingyue National Oceanic and Atmospheric Administration, Climate Prediction Center, National Centers for Environmental Prediction, RM#3011, W/NP52, 5830 University Research Court, 20740-3818, College Park, MD, USA aut NATIONALLICENCE 700 1- Kumar Arun National Oceanic and Atmospheric Administration, Climate Prediction Center, National Centers for Environmental Prediction, RM#3011, W/NP52, 5830 University Research Court, 20740-3818, College Park, MD, USA aut NATIONALLICENCE 773 0- Climate Dynamics Springer Berlin Heidelberg 45/3-4(2015-08-01), 965-974 0930-7575 45:3-4<965 2015 45 382