Discover More About a2s offerings

The Climate Change Risk Index (C2RI) is developed by combining location-specific hazard data with the vulnerabilities of assets in that area. This integrated approach provides a comprehensive assessment of overall climate risk, helping organizations identify where their assets are most exposed and prioritize mitigation strategies.

Key Features 

• Global coverage of extreme climate perils
• Combines Risk Indicators (Hazard Index) and Risk Indices (Hazard + Vulnerability) for more informed decision-making
• Risk Indices tailored to users’ specific asset classes
• Provides 10 location-specific indices for each peril

Climate Change Indicators

Risk Index (Risk Score) for Damage Levels:

• 10% damage – Present Climate
• 30% damage – Present Climate
• 10% damage – SSP2-4.5 (2050)
• 30% damage – SSP2-4.5 (2050)
• 10% damage – SSP2-4.5 (2080)
• 30% damage – SSP2-4.5 (2080)
• 10% damage – SSP5-8.5 (2050)
• 30% damage – SSP5-8.5 (2050)
• 10% damage – SSP5-8.5 (2080)
• 30% damage – SSP5-8.5 (2080)

Perils Coverage

• River Flood, 
• Tropical Cyclone, 
• Extreme Heat,
• Water stress

Climate Scenarios

• Present
• CMIP6 model projections for SSP2-4.5 
• CMIP6 model projections for SSP5-8.5

NC5R is a high-resolution, pre-modelling hazard index product developed by a2s for the United States. It covers 7 major natural catastrophe perils and is designed to support fast, location-specific hazard assessment—especially valuable for underwriters and risk managers during early-stage analysis. 

Key Features  

• Covers 7 major natural perils across the U.S. for comprehensive risk screening
• Provides high-resolution hazard indices for precise, location-based evaluation
• Includes additional geospatial layers: distance to coastline, distance to fault lines, and elevation data
• Designed to support early-stage underwriting, site selection, and portfolio risk assessment
• Enhances risk management workflows through faster, data-driven decision-making

Approach

The NC5R product is developed using a data-driven methodology that integrates high-resolution geospatial information with advanced hazard modeling techniques. It leverages historical event records and scientific hazard assessments to generate consistent, high-resolution hazard indices across different locations. This structured approach enables users to accurately and efficiently evaluate natural catastrophe risks, supporting early-stage underwriting, asset screening, and comprehensive risk management strategies.

Climate Compliances and Declarations

a2s has deep expertise in navigating climate-related compliances and declarations across different regions. We assist businesses in meeting their climate compliance and sustainability objectives effectively.

• Understand and interpret climate regulations across multiple geographies
• Support businesses in achieving climate compliance requirements
• Guide clients on sustainability reporting and disclosure standards
• Help develop strategies aligned with evolving climate policies and frameworks

Climate Analytics and Model Development

a2s is dedicated to detailed analysis of climate change impacts. We cover every aspect of climate risk assessment—from climate model evaluation to hazard index calculation and quantifying acute and chronic climate risks. Our solutions are customized to address each client’s specific challenges.

• Evaluate climate models for accuracy and relevance
• Calculate hazard indices for various climate perils
• Quantify acute (extreme events) and chronic (long-term) climate risks
• Collaborate closely with clients to develop tailored, actionable solutions

Climate Model Evaluation

a2s conducts rigorous evaluation of CMIP6 General Circulation Models (GCMs) to support robust climate risk assessments. This includes an extensive meta-analysis of peer-reviewed literature assessing GCM performance across multiple climatic variables (e.g., temperature, precipitation) and geographic regions.

• Identification of quantitative metrics capturing critical climate dynamics such as long-term trends, inter-annual variability, and seasonal cycles
• Development and application of grid-resolved statistical methodologies to evaluate spatial accuracy of GCM outputs against observed climate data
• Use of multi-criteria ranking algorithms to score GCMs based on their ability to simulate key climate features
• Design of an ensemble weighting framework that integrates individual GCM rankings into a spatially explicit consensus projection, enhancing model reliability and reducing uncertainty