DOI: 10.26027/datahcnii0
title: Daily precipitation ITCZ states for observations, reanalyses, and 25 CMIP6 models for “We need to simulate more northern ITCZs and less southern ITCZs over the east Pacific Ocean in coupled climate models.”

description: Tropical precipitation biases have persisted since the very first generations of climate models. These biases are highly sensitive to the region and/or season of interest, with commonalities in the east Pacific and Atlantic Ocean basins, possibly due to their similar observed climatological northern hemisphere intertropical convergence zone (ITCZ). However, the colloquial name “double ITCZ bias” comes from a time and/or zonal mean, and may be missing important information about errors at smaller time and space scales. In this study, we explore daily characteristics of the ITCZ in observations, reanalyses, and 25 Coupled Model Intercomparison Project 6 (CMIP6) models over the east Pacific Ocean. We devise and apply an algorithm that determines a region's dominant daily ITCZ configuration, its “ITCZ state,” based on the daily mean precipitation field. The five ITCZ states include: northern hemisphere (nITCZ), southern hemisphere (sITCZ), double (dITCZ), equatorial (eITCZ), and absent (aITCZ). We find that nearly all CMIP6 models gravely underestimate nITCZs and overestimate sITCZs during January through May, in contrast with what “double ITCZ bias” suggests. Surprisingly, all reanalyses also underestimate nITCZs and overestimate eITCZs. Errors in ITCZ state interannual variability are consistent with mean errors in reanalyses, while sITCZ interannual variability is far too low relative to the mean in most CMIP6 models. Lastly, all reanalyses and CMIP6 models overestimate precipitation rates in the southern hemisphere ITCZ band for dITCZs and sITCZs, suggestive of errors with atmospheric origins.
description: This dataset includes output from a Python algorithm that determines regional ITCZ state each day of the year for many decades of data over the tropical east Pacific Ocean (90°W–135°W, 20°S–20°N) using the daily averaged precipitation field that has been bilinearly interpolated to a common 1° by 1° latitude by longitude grid of three observational datasets, four reanalysis datasets, and output from 25 models participating in the sixth Coupled Model Intercomparison Project (CMIP6). The observational precipitation datasets include: i) NASA's Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM combined microwave and infrared dataset (IMERG), ii) NASA’s IMERG microwave only dataset (IMERG_mw), and ii) NOAA’s Global Precipitation Climatology Project (GPCP) dataset. The reanalysis precipitation datasets include: i) European Centre for Medium-Range Weather Forecast's Fifth Reanalysis (ERA5), ii) NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), iii) Japan Meteorological Agency's Japanese Reanalysis for Three Quarters of a Century (JRA-3Q), and iv) the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR). For a list of the 25 CMIP6 models, see Table 1 in Gonzalez et al. (2025). 

This dataset includes regional daily ITCZ state (time), longitude-dependent ITCZ state (time, longitude), precipitation threshold percentile (0.9), and several regional precipitation thresholds determined by each day’s 90th percentile of precipitation: regional precipitation threshold in the Southern Hemisphere, 20°S–2°S (time), regional precipitation threshold in the Northern Hemisphere, 2°N–20°N (time), and regional precipitation threshold in both hemispheres, 20°S–20°N (time). The regional daily ITCZ state makes several decisions, based on the longitude-dependent ITCZ states, that are outlined in the methods section of Gonzalez et al. (2025). The longitude-dependent ITCZ states allows users to make their own decisions for regional ITCZ state.
description: The algorithm that determines each dataset’s ITCZ states was developed and written by Alex O. Gonzalez. The input to the ITCZ states algorithm is daily averaged precipitation as a function of time, latitude, and longitude for each dataset of interest retrieved from their respective websites. See Gonzalez et al. (2025) for DOIs of each precipitation dataset.