ITCZ trend analysis via Geodesic P-spline smoothing of the AIRWAVE TCWV and Cloud datasets

The Inter-Tropical Convergence Zone (ITCZ) is the region where the northeast and southeast trade winds converge. The ITCZ, that shifts north to south seasonally, plays a key role in the general circulation of the atmosphere. Furthermore, recent studies assess its importance in the Earth energy balance so that understanding its variability is essential for improving global climate models. The ITCZ region can be easily detected from space. Indeed, it can be identified using mean outgoing longwave radiation (OLR) or seasonal mean precipitations. Since this region is characterized by strong upwelling circulation, the ITCZ location affects many atmospheric quantities like the cloud distribution, the precipitation and the humidity distribution. The Advanced Infra-Red WAter Vapour Estimator (AIRWAVE) dataset consists of 20-year day-night Total Column of Water Vapor (TCWV) data retrieved from the Along Track Scanning Radiometer (ATSR) missions over the sea in clear sky conditions. The AIRWAVE dataset has been analyzed with a Geodesic P-spline efficient spatial smoothing method specifically developed to extract information from large datasets. The method exploits a geodesic grid built on an icosahedron structure which accounts for geodesic distances between data locations. Model estimation is done with the R-INLA package for Bayesian computation. The posterior distribution of the model is then analyzed for identification of the ITCZ both over sea and land with associated uncertainty quantification. A complementary dataset containing the cloud coverage extracted from ATSR measurements (and thus providing data on both land and sea surfaces) was processed with the same tool. The resulting AIRWAVE interpolated fields were then analysed to detect trends in the ITCZ latitudinal migration over the 20 years of ATSR lifetime and over the whole globe. As AIRWAVE could be applied also to the Sea and Land Surface Temperature Radiometer (SLSTR) data, this study can be extended to more recent and future years. Here we show the results of the comparison of the ITCZ location and trend analysis obtained from AIRWAVE data and from other datasets such as the Global Precipitation Climatology Project (GPCP) or the GlobVapour dataset.