My work spans multiple scales of climate science—from cloud microphysics to regional climate systems—providing the technical foundation for stakeholder-focused applications.
Established Local Climate Risk Analytics and Product Development
I've established the development of convection-permitting (≤3 km resolution) climate modeling approaches, which accurately capture local weather patterns, extreme events, and terrain effects missed by coarser global models. This methodological rigor included evaluating the model performance against historical observation period ensuring the projections provide a robust foundation for decision-making.
This work led to the creation and delivery of breakthrough climate risk products, including the first local-scale climate dataset (2 Petabytes) for the Northeastern U.S. The high-resolution datasets for the Northeastern United States, Saudi Arabia, and Istanbul were essential for cascading impact and risk analysis across multiple sectors:
Infrastructure Planning (e.g., U.S. National Grid, Dar Group);
Water Resource Management;
Urban Design and Climate Adaptation.
My regional modeling framework has been cited by the IPCC 2021 report as promising for local-scale risk assessments.
Key Publications: Komurcu et al. (2018), Earth and Space Science - Top-read article, IPCC 2021 cited, featured in MIT News.
Aerosol-Cloud Interactions & Climate Sensitivity
As a postdoctoral researcher at Yale University, I led the first international inter-comparison of cloud water phase (ice vs. liquid) among global climate models. Cloud phase significantly affects climate sensitivity, and our study revealed that models under-predict cloud liquid water compared to satellite observations. This work influenced improvements to cloud microphysics parameterizations in multiple climate models and included collaboration with several IPCC authors from domestic and international institutions including ETH Zurich, Pacific Northwest National Laboratory (PNNL), University of Michigan, Kyushu University, and the University of Wyoming. I also studied Arctic mixed-phase clouds using large eddy simulation (LES) modeling, investigating how aerosol-cloud interactions affect persistent Arctic clouds that influence sea ice and regional climate.
Key Publications: Komurcu et al. (2014), JGR Atmospheres - Highly cited study informing climate model development.
Nature-Inspired Optimization Algorithms
During my doctoral work, I co-developed the Wind Driven Optimization (WDO) algorithm through a deep, sustained collaboration with Penn State's Electrical Engineering department—a cross-functional technical partnership that yielded multiple highly-cited publications. We created three versions (Classical WDO, Adaptive WDO, and Multi-objective WDO) with applications across disciplines.
Applications: The optimization approaches developed here inform my current work applying AI and machine learning methods to climate problems.
Interdisciplinary Foundations: Mesoscale Modeling and ML Applications in Turkiye
My master's and undergraduate research at Istanbul Technical University focused on characterizing pollutant sources and transport in Turkiye and improving simulation of extreme precipitation and flood events in Istanbul using mesoscale models (MM5). I published machine learning-based air quality analysis in NATO workshop proceedings as early as 2005.
Interdisciplinary Foundation (1999–2005): This early work required continuous, long-term collaboration with civil engineers, hydrologists, and environmental engineers on EU Framework Program proposals, establishing my ability to bridge atmospheric science and practical engineering disciplines for over two decades.
How My Research Connects to Applied Work
The technical depth from this research enables my stakeholder-focused projects:
Regional modeling expertise → High-resolution climate projections for Northeastern U.S., Saudi Arabia, Istanbul.
Climate model evaluation →Creating and assessing available local and larger scale climate projections products and providing expert guidance on model and climate data selection.
Computational systems → Managing petabyte-scale data and leading HPC-intensive projects.
Interdisciplinary collaboration →Bridging atmospheric science with engineering, economics, architecture, and policy—a skill continuously demonstrated over 20+ years of project leadership and partnerships.
This research foundation, combined with 20+ years of stakeholder engagement, enables me to deliver climate solutions that are both scientifically rigorous and practically useful.