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ClimateAi Unveils Breakthrough 1km Resolution Climate Risk Forecasting using Physics-Informed Machine LearningCovered by AP News

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Get the Insights You Need to Build a Climate-Proof Strategy

From regulatory needs around TCFD/CSRD to critical decision support, get asset and portfolio- level risk analyses to set your business up for future success

ClimateLens™-Adapt

Our AI and machine learning-powered climate analytics give you long-term visibility into climate risks and opportunities so you can start adapting your business to tomorrow’s challenges today.

Exploratory Tools for Climate Adaptation

  • Identify migration of key climate zones and crop regions
  • Discover more resilient locations for your business
  • Evaluate and compare climate trends for new and existing locations

Climate Risk Modeling for Your Assets and Supply Chain

  • Best-in-class global climate risk assessment
  • Industry-tested and machine learning-based asset impact functions
  • TCFD/CRSD compliant: Includes both acute risks (hurricane, flooding, etc.) and chronic hazards

Water risk index to evaluate and mitigate future scarcity

  • Comprehensive water risk analysis covering local and regional (groundwater, surface water) sources
  • Evaluate different interventions (e.g. drip irrigation) to improve sustainability
  • Receive actionable insights and clear recommendations with just a location

Ready to see ClimateLens-Adapt in action?

Schedule a discovery call today to see how ClimateAi’s climate analytics tools can help your business adapt to extreme weather.

Book a demo

Why consider climate data and analytics in your long-term strategic decision making?

Identify risks and opportunities in your supply chain or portfolio

Understand how climate hazards impact your operations and assets

Build an action plan to address risks and protect your investments

Work to make your business TCFD/CSRD compliant

CASE STUDIES

How Businesses are Using ClimateLens-Adapt to Get Ahead:

Green leak onions farm field mitigating climate risk for BASF Case study.

Case Study

Assessing Current and Future Climate Risks–at a Fraction of the Time and Cost

A large multinational corporation with 11 divisions, including agricultural solutions, was noticing an increase in climate volatility.

Wonderful Case Study webinar

Downloadable Content

Webinar: The Wonderful Company

See how The Wonderful Company uses climate intelligence to derisk their supply chains

Turn Climate Challenges into Business Opportunities

Climate resilience delivers a triple dividend for businesses: avoided losses, new market opportunities, and social and environmental benefits. Building a climate resilience strategy for your business can accelerate all three. In fact, climate resilience is one of the biggest market (and social) opportunities for companies today. ClimateAi has helped dozens of companies adapt to climate volatility, so we’ve got front-row seats to what works — and what doesn’t.

Check out our 2024 Climate Resilience Playbook for a detailed roadmap and case studies.

ClimateLens Adapt FAQs

Weather-dependent businesses often require significant infrastructure investments, such as farmland, irrigation systems, transportation networks, and energy facilities. Demand for their products may also fluctuate with different climate scenarios. With actionable long-term forecasts, businesses can vet strategies and investments in current regions (and even new potential regions), conducting better due diligence and increasing confidence in actions. Building resilience is an opportunity to avoid losses improve margins, and uncover new opportunities.

Long-term climate data and analytics can help companies comply with TCFD recommendations and CSRD requirements by informing risk assessment and disclosure, providing scenario analysis, guiding adaptation planning, and facilitating stakeholder engagement on climate-related issues.

Our climate projections are based on climate models from the CMIP6 archive, which is also used as a basis for the IPCC reports. We use post-processing to reduce uncertainties from errors in the climate models by calibrating them against high-resolution observations and verify our forecasts by how well they represent past climate change. For this we use a cross-validation over the past four decades and assess how our post- processing changes the standard deviation of the projections as well as the Continuous Ranked Probability Score (CRPS), a measure of probabilistic forecast quality.