Process-oriented evaluation of african easterly waves over west africa and the tropical north atlantic competition: mapp process-oriented diagnostics for NOAA climate model improvement and…

Process-oriented evaluation of african easterly waves over west africa and the tropical north atlantic competition: mapp process-oriented diagnostics for NOAA climate model improvement and applications african easterly waves (AEWS) are a prominent weather system over africa. They modulate mesoscale convective systems and extreme precipitation over west africa and can lead to tropical cyclone formation over the tropical north atlantic. Accurate representation of AEWS is thus crucial for reliable projections of the regional climate and extremes in those regions (including tropical cyclone activity affecting the U.S.). However, existing weather and climate models generally have difficulty in capturing the characteristics of AEWS and their impacts on regional precipitation due to complex interactions among various physical and dynamical processes, such as convection, planetary boundary layer, cloud-radiation, aerosol-radiation, land-atmosphere, and air-sea interactions. This proposed project aims to develop a set of process-oriented diagnostics (PODS) to evaluate AEWS and to identify pathways for model improvement. Our specific objectives and outcomes/deliverables are: 1. Develop PODS: create PODS that quantitatively assess the representation of AEWS (such as structure, frequency, intensity, and spatial distribution) and key processes crucial to AEWS. Contribute these tools to the model diagnostic task force (MDTF) for wider usage. 2. Apply PODS to evaluate class-leading modeling systems using existing state-of-the-art datasets: adopt the CMIP6 datasets (https://esgf-node.llnl.GOV/search/CMIP6/) and the unified forecast system (UFS) coupled model experiments (already conducted by the proposal team). Quantitatively assess the statistics of AEW representations using PODS and by evaluating against reanalysis and observations over historical periods. Benchmark process-level deficiencies to undergird the successful prediction of AEWS and accentuate the robustness of PODS by evaluating multiple models. 3. Advance understanding of model biases and identify pathways for model improvement using hierarchical testing: conduct case studies during the african monsoon multidisciplinary analysis (AMMA) field campaign. Benchmark biases arising at shorter timescales by carrying out control experiments using the UFS global model. Conduct iterative experiments using the common community physics package single-column model, to evaluate column physics against AMMA dataset and to identify areas of improvement in the model physics. Discuss potential physics enhancements with developers and apply proposed enhancements to the UFS global model to reassess AEWS until improvement of prediction is achieved. We anticipate that our PODS and physically based testing and evaluation will improve our understanding and prediction of regional climate variability including extremes and its impact on agriculture, water resources, and human livelihoods in west africa and tropical cyclone activity downstream. This type I proposal will close a GAP in the existing MDTF software package that currently does not have PODS to assess AEWS. IT falls right into the focal area of the mapps competition on addressing key issues in the representation of earth system processes in CMIP6- ERA and developmental models to improve model fidelity and echo a key element in noaas mission to better understand and predict changes in climate, weather, ocean and coasts.