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Historical climate data is fundamental for understanding local climate trends and extremes and evaluating the impact of climate change on agriculture, food security and water resources. In addition, the data is used for implementing adaptation measures for protecting lives and adapting socio-economic sectors to the changing climate conditions. While global climate trends can already be assessed with available data, regional data disparities result in blind-spots for climate change assessments—particularly for areas which are the most vulnerable. To contribute to filling this gap, the International Climate Assessment & Dataset (ICA&D) has been extended to the climate-vulnerable areas of the Caribbean, the Pacific and in 5 sub-regions in Africa. ICA&D provides an accessible, web-based system to collect, prepare, quality control, and analyze basic climatological data with daily resolution. The system was developed over 25 years ago by the Meteorological Service of the Netherlands (KNMI) as the backbone of the European WMO data-node of the Regional Climate Centre (https://www.ecad.eu/) and implemented in Southeast Asia by meteorological service of Indonesia (BMKG). Now, with support from the EU-funded ClimSA project, under which WMO is implementing a €5.5 million grant, ICA&D will be further extended and run by Regional Climate Centers assigned and supported by WMO. WMO and KNMI collaborated in 2022 through CLIMSA project to expand ICA&D to two pilot regions, the Caribbean and West-Africa on https://caribbean.icad-wmo.org/ and http://west-africa.icad-wmo.org/. The collaboration has led to setting up new websites which are designed to be user friendly and highly cyber secure. The websites, one for each region, consist of information platforms, including a geographical map with the locations of the stations’ datasets, and a feature to quickly plot timeseries from derived data and an e-learning module on the website’s functions. The data sharing policy ensures that access to raw observational data from National Meteorological and Hydrological Services (NMHS) remain restricted while allowing derived indices to be accessed by the public. Indices alone can monitor critical areas and impacts of climate change, such as the temperature of the warmest night of the year, the subsequent impact on health and induced excess of mortality.

The International Climate Assessment & Dataset (ICA&D) contributes to the provision of climate services in regions across the world. ICA&D contributes both to the provision of climate data and to climate monitoring, thereby supporting the WMO Regional Climate Centres in carrying-out their mandatory functions.In WMO Regional Association VI (Europe and the Middle East), the European Climate Assessment & Dataset (ECA&D) fulfills this role and from this the ICA&D concept is derived. ICA&D has already been implemented in Indonesia as the Southeast Asian Climate Assessment & Dataset (SACA&D).ICA&D facilitates the sharing of daily meteorological surface observations from countries in a given region with meteorological services in that region and with scientists worldwide, and deriving climate monitoring products from these observations. Examples are the climate indices of extremes, such as the number of warm or dry days, which can be monitored over time in view of climate change. ICA&D complements national meteorological databases in having only a daily resolution and containing information from multiple countries in the region in the same format. With support from the EU-funded ClimSA project, under which WMO is implementing a EUR 5.5 million grant, ICA&D will be implemented in the Caribbean, the Pacific and 5 sub-regions in Africa. ICA&D will be set-up in selected Regional Climate Centres that form part of the WMO institutional network. Within the ClimSA project, the ICA&D website has seen an upgrade and has been made more user friendly. Furthermore, e-learning modules are being developed to aid the ICA&D maintainers in the region in updating their ICA&D database and website.

A suite of statistical atmosphere-only linear inverse models of varying complexity are used to hindcast recent MJO events from the Year of Tropical Convection and the Cooperative Indian Ocean Experiment on Intraseasonal Variability/Dynamics of the Madden–Julian Oscillation mission periods, as well as over the 2000–2009 time period. Skill exists for over two weeks, competitive with the skill of some numerical models in both bivariate correlation and root-mean-squared-error scores during both observational mission periods. Skill is higher during mature Madden–Julian Oscillation conditions, as opposed to during growth phases, suggesting that growth dynamics may be more complex or non-linear since they are not as well captured by a linear model. There is little prediction skill gained by including non-leading modes of variability.