Subnational climate data analysis methods

🌆 🌎 As we wrap up the IPCC Special Report on Cities and Climate second-order draft, I wanted to share three new preprints our team submitted for the Mar 31 deadline. A common thread across all of these papers is something we have been thinking a lot about: how to better align emissions and mitigation analysis with the boundaries where decisions actually get made. A lot of existing work, especially modeling and future pathway analysis, defines urban areas using pixels or morphology. That can be useful, but it doesn't reflect how cities actually govern, plan, and make policy. We take a different approach by working with administrative boundaries, imperfect as they are, because they are closer to the jurisdictions through which decisions are made. A few TL;DRs from the papers: - Manya, D., Roelfsema, M., Zhang, Y., Yu, Y., Luderer, G., Weigmann, P., Hsu, A. (2026). Cities and urban areas are central to global decarbonization pathways. Pre-print: https://lnkd.in/evgmJywM. We downscale global and regional IAM pathways to urban areas and show that they're central to future decarbonization and net-negative pathways. We also evaluate more than 3,500 subnational mitigation targets and find that many city and subnational targets in G20 countries still fall short of what is needed even under current policies, let alone 2C-aligned pathways. - Robiou du Pont, Y., Manya, D., Song, K., Haarstad, H., and Hsu, A. (2026). From countries to cities: assessing climate ambition with a multi-level fair-share allocation framework. Pre-print: https://lnkd.in/eSvtfgZ4. This paper introduces the first multi-level fair-share framework for assessing city and subnational targets against 1.5C and 2C pathways using responsibility and capability principles. Our analysis suggests that many Global North cities have already exceeded their carbon budgets, pointing to the need not only for deeper local mitigation, but also for financial support to Global South cities that still retain development space. We will be making the underlying data available soon through Paris Equity Check. - Ying, Y., Manya, D., and Hsu, A. (2026). Aligning emissions with decisionmaking: estimating urban contributions to global carbon dioxide emissions. Pre-print: https://lnkd.in/evUcgpUH.  This paper tackles a long-standing question in the urban climate literature: why estimates of the urban share of global emissions vary so much. We estimate that urban jurisdictions account for roughly 72–76% of global territorial CO2 emissions in 2022, and about 82% of consumption-based CO2 emissions in 2015. We also show why pairing territorial and consumption-based accounting is essential for understanding responsibility and identifying mitigation leverage points. Thank you to my team Data-Driven EnviroLab (Diego Manya Yuetong Zhang Ying Yu, PhD) and collaborators (Mark Roelfsema Yann Robiou du Pont Gunnar Luderer + teams) for making the final push with us!

ERA5-Land 2m Temperature Analysis for South Asia (2024) I have applied machine learning and geospatial analysis techniques to explore monthly 2-meter air temperature trends across South Asia in 2024 using ECMWF’s ERA5-Land reanalysis data. Data & Tools: 1. Source: ERA5-Land monthly aggregated temperature data (2m above ground) 2. Platform: Google Earth Engine 3. Language: Python with geemap and Cartopy for spatial processing and visualization Key Findings: 1. Seasonal Temperature Variation: Clear month-by-month temperature variation, with cold winters in the Himalayan region and intense heat peaks during summer across the Indian subcontinent. 2. Spatial Insights: Temperature gradients reflect diverse climate zones, critical for environmental and climate impact studies. Methodology: 1. Extracted temperature data for each month in 2024. 2. Converted Kelvin to Celsius for meaningful interpretation. 3. Generated detailed spatial maps using Python visualization libraries with Cartopy projections. 4. Created a comprehensive multi-panel figure showcasing monthly variations for easy comparison. Significance: This analysis demonstrates how integrating open climate data with cloud-based geospatial tools and machine learning enables high-quality, reproducible climate monitoring. These insights can support regional planning, agriculture, disaster preparedness, and climate resilience initiatives. #ClimateScience #DataScience #GeospatialAnalysis #MachineLearning #GoogleEarthEngine #ClimateChange #uthAsia

I’ve been working on a climate zoning and vegetation analysis for Japan using Google Earth Engine, combining long term climate data with satellite vegetation indices. The study uses ERA5-Land monthly data from 2000 to 2020, focusing on two core variables: Near-surface air temperature (2m) Total precipitation After stacking the climate variables, I applied K-means clustering to divide Japan into four climate zones based purely on temperature and precipitation patterns. This approach highlights the strong north–south climate contrast across the country, without relying on predefined climate classifications. To quantify the results, I calculated the area covered by each climate class and extracted minimum and maximum temperature statistics for every zone. To connect climate with ecosystem response, I integrated MODIS NDVI (2002–2020) and analyzed vegetation trends for one selected climate class. This helps show how vegetation behaves over time under similar climate conditions. This kind of workflow is useful for climate regionalization, environmental monitoring, and linking climate variability with vegetation dynamics, all at a national scale. If you’re working with ERA5, MODIS, or unsupervised classification in GEE, this is a solid starting point for large-scale climate analysis. #GIS #RemoteSensing #GoogleEarthEngine #ClimateAnalysis #ERA5 #MODIS #NDVI #ClimateChange #Geospatial #EnvironmentalMonitoring

In a The World Bank blog, German Caruso and Inés de Marcos introduce the Climate Effects Navigator Toolkit (CLIENT), a new interactive platform that combines climate and human capital data to analyze the long-term effects of disasters on health, education, and livelihoods. Key features: 📊 Tracks six hazard types (e.g. droughts, floods, heatwaves, hurricanes) over nearly five decades. Users can tweak thresholds, timeframes, and measure by land or population to analyze exposure, frequency, and severity at subnational levels. 🧍Uses census microdata to show who’s most affected. Users can explore how disasters impact school attendance, employment, electricity access, and more, before and after events, to highlight vulnerable groups like children or underserved households. ⚙ Overlays World Bank project data with climate-affected areas, helping identify where current initiatives are helping, and where gaps remain, enabling better targeting of climate-smart investments. 🔍 Integrates almost five decades of climate data across 38,000+ subnational regions and harmonizes climate records, census data, population stats, and administrative boundaries into a flexible toolkit with over 300 customizable parameters. 🗒️ Read the blog: https://lnkd.in/gGsURKjD 🖥️ Try the toolkit: https://lnkd.in/gUJB3Kkc 💻 Check out the Climate Change Knowledge Portal: https://lnkd.in/gw2eThqb