2026-10-15T09:02:57Z https://api.figshare.com/v2/oai

oai:figshare.com:article/27240801 2024-11-14T10:18:54Z category_26878 category_25759 category_28846 portal_753 item_type_3 month_year_11_2024

Framework for statistical downscaling of the global climate model seasonal geopotential thickness fields to seasonal<b> </b>maximum temperature in Southern Africa to aid climate<b> </b>change adaptation Moahloli Ntele (19817196) Climate change impacts and adaptation not elsewhere classified Climatology Modelling and simulation Maximum temperature Climate change Adaptation Anthropogenic Global warming Seasonal climate forecast (SCF) Predictability Linear trends Global climate model SDG 9 Industry, innovation and infrastructure SDG 11 Sustainable cities and communities SDG 13 Climate action <p dir="ltr">Maximum temperature and rainfall observed data files were downloaded from the IRI Data Library as well as the model predicted 850-to-500 geopotential thickness fields (used to predict maximum temperature over southern Africa) and 850 circulation data fields (predictor for rainfall). Model Output statistics in CPT - climate predictability tool, was set up using CCA - canonical correlation analysis to produce retroactive forecasts. MATLAB was further utilized to post-process / fine-tune the output from CPT and to produce other results. The researcher used the output from the global climate model to develop a statistical model for maximum temperature seasonal forecasts for Southern Africa.</p> 2024-11-13T12:46:35Z Dataset Dataset 10.25403/UPresearchdata.27240801.v3 https://figshare.com/articles/dataset/Framework_for_statistical_downscaling_of_global_climate_model_geopotential_thickness_fields_to_maximum_temperature_in_Southern_Africa/27240801 CC BY 4.0 Open Access after 2026-11-13