High-resolution climate change indicators

Climate models have long struggled with coarse resolution, limiting precise climate risk insights. But AI-driven methods are now changing this, unlocking more detailed intelligence than traditional physics-based approaches. I recently spoke with a research scientist at Google Research who highlighted a promising new hybrid approach. This method combines physics-based General Circulation Models (GCMs) with AI refinement, significantly improving resolution. The process starts with Regional Climate Models (RCMs) anchoring physical consistency at ~45 km resolution. Then, it uses a diffusion model, R2-D2, to enhance output resolution to 9 km, making estimates more suitable for projecting extreme climate events. 🔥 About R2-D2 R2‑D2 (Regional Residual Diffusion-based Downscaling) is a diffusion model trained on residuals between RCM outputs and high-resolution targets. Conditioned on physical inputs like coarse climate fields and terrain, it rapidly generates high-res climate maps (~800 fields/hour on GPUs), complete with uncertainty estimates. ✅ Why this matters - Offers detailed projections of extreme climate events for precise risk quantification. - Delivers probabilistic forecasts, improving risk modeling and scenario planning. - Provides another high-resolution modeling approach, enriching ensemble strategies for climate risk projections. 👉 Read the full paper: https://lnkd.in/gU6qmZTR 👉 An excellent explainer blog: https://lnkd.in/gAEJFEV2 If your work involves climate risk assessment, adaptation planning, or quantitative modeling, how are you leveraging high-resolution risk projections?

Physical climate risk data: the more we learn, the less we know? Khalid Azizuddin's recent piece in *Responsible Investor captures well what many practitioners are grappling with today: - asset-level data that remain incomplete or hard to interpret; - physical hazard exposure often disconnected from financial materiality; - little visibility on supply chains or customers; - adaptation and resilience efforts largely ignored; - and a risk of over-simplifying complex realities into a single “score.” Some three years ago, EDHEC Business School set out to address exactly these challenges, working to advance climate risk modelling and make decision-useful for investors, companies, and public authorities. In this work, we have developed: 🔹 a blueprint for a new generation of probabilistic climate scenarios; 🔹 high-resolution geospatial modeling capabilities to allow for geographic and sectoral downscaling, consistent with each scenario; 🔹 an open database of decarbonisation and resilience technologies through the #ClimaTech project, which officially launched this week. While the research is public, the new EDHEC Climate Institute has also been assisting a school-backed venture, Scientific Climate Ratings (SCR), which integrates this research to deliver forward-looking quantification of the #financialmateriality of climate risks for infrastructure companies and investors worldwide. While SCR provides a rating scale for comparability, it avoids the trap of over-simplification. Each rating is backed by probabilistic scenario modelling, analysis of physical and transition risk exposures, and explicit accounting for adaptation measures. The result is a synthesis that remains transparent, interpretable, and anchored in scientific rigour. Together, these initiatives aim to move the discussion from data abundance to decision relevance, equipping practitioners with tools that connect climate science, finance, and strategy.

Indicators of Global #ClimateChange 2023: annual update of key indicators of the state of the climate system and #human #influence Abstract: Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of #scientific #evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely #information on key #indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining #carbon budget, and estimates of global #temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced #warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023 This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO2e yr−1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here Please read the full article!

I am very excited about our new study that was just published in Nature Geoscience. It shows that future extreme precipitation will intensify far more than previously estimated, driven by stronger mesoscale moisture convergence. Using a global high-resolution Earth system model that simulates extreme-producing phenomena far better than its low-resolution counterpart (see image below), we find that daily extremes could rise by over 40% by 2100—nearly three times the dynamical contribution seen in standard low-resolution models. These results highlight the urgent need for high-resolution climate modeling to constrain risks better and support effective adaptation strategies.

🔥 Land Surface Temperature (LST) Analysis for 2013–2023 using MODIS & Google Earth Engine I recently completed a 10-year Land Surface Temperature (LST) analysis using the MODIS/061/MOD11A1 (LST_Day_1km) dataset in Google Earth Engine (GEE). This work highlights how geospatial technologies can support climate research, environmental planning, and urban resilience. 🌍 What I Did -Processed and analyzed MODIS LST time-series data (2013–2023). -Converted LST values from Kelvin to °C. -Computed annual mean LST for my Area of Interest (AOI). Generated yearly LST maps, including a custom legend and color palette. Produced: 📊 Annual LST bar chart 📈 Annual LST line chart with a linear trendline 🎞️ GIF-style animation showing LST changes from 2013 to 2023 Calculated the temperature trend (°C/year) using linear regression. 📌 Applications of This Analysis -Climate Change Monitoring: Detect long-term warming or cooling trends. -Urban Heat Island Assessment: Identify temperature hotspots for urban -planning and heat mitigation. -Environmental & Ecosystem Health: Monitor heat stress on vegetation and land degradation. -Agriculture & Drought Monitoring: Support early warning systems and agricultural planning. -Water Resources & Hydrology: Improve evapotranspiration and water balance modeling. -Disaster Risk & Heatwave Management: Map heat-prone zones for climate resilience planning. -Land Use/Land Cover Impact: Understand how land cover types influence surface temperature. ⚡ Advantages of This Approach -High Temporal Resolution: Daily MODIS data ensures reliable annual LST estimates. -Large Spatial Coverage: Suitable for regional/national-scale assessments. Reliable Dataset: MODIS LST is globally validated for environmental monitoring. -Automated & Reproducible: GEE scripting makes the workflow scalable and repeatable. -Fast Cloud Processing: No need for local downloads or heavy computation. Clear Visual Outputs: Maps, charts, and animations enhance communication. -Decision-Support Ready: Useful for planners, climate researchers, and policy makers. Source code: https://lnkd.in/eT6hYq4S If you're interested in remote sensing-based environmental monitoring, feel free to connect or reach out! #GIS #RemoteSensing #GoogleEarthEngine #MODIS #ClimateAnalysis #LST #Geospatial #DataScience

📘 Downscaling CHIRPS Precipitation Data to 100m Resolution Using Sentinel-2 in Google Earth Engine Source Code = https://lnkd.in/dNc7NbjE 1. Introduction: Rainfall data at high spatial resolution is critical for precise hydrological analysis, drought monitoring, and agriculture planning. However, most global precipitation datasets, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), are available at coarser resolutions (~5 km). This project addresses this limitation by downscaling CHIRPS daily precipitation data to 100-meter spatial resolution using bilinear interpolation and Sentinel-2 as a high-resolution spatial reference. 2. Objective: To extract and sum CHIRPS precipitation data over a selected AOI (WMH District) for a specific 3-month period (October 2023 – January 2024). To downscale the CHIRPS raster data to a finer 100-meter resolution using Sentinel-2 spatial referencing. To visualize and compare the original and downscaled precipitation maps. To prepare refined precipitation layers for potential integration with NDVI, crop condition analysis, or drought indices. 3. Importance of the Study: Higher spatial resolution enables more localized analysis of rainfall, especially in heterogeneous landscapes. Improved input for climate models and agro-hydrological studies. Better decision-making for irrigation scheduling, water resource management, and drought preparedness. Supports integration with high-resolution datasets such as NDVI, land use, or soil moisture for multi-parameter environmental studies. 4. Benefits: Enhanced accuracy in rainfall data analysis at local and district levels. Scalable method applicable to any region globally. Supports policymakers and researchers with higher-resolution inputs for climate resilience, agricultural planning, and hydrological monitoring. Efficient use of cloud computing via Google Earth Engine for handling large spatiotemporal datasets. 5. Output: Original CHIRPS Precipitation Map (Oct 2023 – Jan 2024) clipped to WMH District. Downscaled Precipitation Map at 100m resolution, reprojected using Sentinel-2 reference. Color-coded visualization using a 5-class blue gradient, where: Light blue = Low precipitation Dark blue = High precipitation Ready-to-export raster layer of downscaled precipitation (if export added). Output maps can be further used for vegetation correlation (e.g., NDVI vs. rainfall) and SPI generation. #GEE #GoogleEarthEngine #BuildupAreaExpansion #GeospatialAnalytics #RemoteSensing #UrbanExpansion #Geospatial #GoogleEarthEngine #GIS #SustainableDevelopment #Sentinel2 #GeospatialTech #PhD #Agriculture #ClimateSmart #GIS #DeepLearning #ClimateSmartAgriculture #CropHealthMonitoring #DroughtMonitoring #SustainableFarming #Sentinel2 #GoogleEarthEngine #NDVI #LandsatData #GISMapping #GeospatialAnalysis #AIinAgriculture #EarthObservation #AgricultureMapping #RemoteSensin #SatelliteImagery

The future of climate projections... The EU's "Destination Earth" initiative created a Digital Twin (DT) of the Earth system. This DT is a kind of simulated "living" replica of the Earth system and was designed to provide more fine-grained climate projections. Powered by the first pre-exascale supercomputers in Europe, the Climate DT is able to provide climate impact data at scales of a few kilometres (current scale is around 100km, see image). Such local granularity matters, as climate change is a global but also very local phenomenon. The new Climate DT can bridge the gap between global (rather large-scale) climate projections and local climate impacts. Hopefully, this will support policy-making on climate adaptation and mitigation with a regional focus in mind. More info on the Climate DT: https://lnkd.in/dG3YV_kA Academic paper on Destination Earth: https://lnkd.in/dj_gjRNW #climatechange, #sustainability