Translating Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) from scientific projections into actionable business intelligence requires practical data skills, the right tools, and systematic workflows. This guide provides the technical "how-to" that transforms abstract climate scenarios into quantified risk assessments meeting CSRD, EU Taxonomy, and investor expectations.
This article is part of our comprehensive Climate Scenarios Series, providing the complete foundation for scenario-based climate risk analysis. For guidance on which scenarios to select, see our companion article on choosing the right RCP scenario.
What distinguishes this guide: Rather than explaining what RCP and SSP scenarios are (covered comprehensively in our hub article), this spoke article focuses exclusively on how to access, process, and integrate climate data into your corporate risk assessment workflows. You'll learn practical techniques including:
The gap between "we need scenario analysis" and "we have quantified scenario-based risks" typically involves data wrangling, technical translation, and workflow design—precisely what this guide addresses.
Current regulatory context (2025): CSRD's ESRS E1 requires companies to disclose climate scenario analysis with specific time horizons (2030, 2040, 2050+), quantified impacts where feasible, and clear assumptions. This isn't theoretical—auditors now scrutinise data sources, methodology documentation, and calculation transparency. Companies without defensible technical workflows face compliance gaps.
The difference between superficial scenario analysis (qualitative narratives without data foundation) and robust assessment (quantified risks with clear data provenance) increasingly determines audit outcomes, investor confidence, and strategic credibility.
The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides the authoritative climate model outputs underlying IPCC projections. Unlike simplified climate calculators, CMIP6 offers:
Key distinction: Raw CMIP6 data is voluminous (petabytes) and complex. Practical corporate use requires accessing pre-processed subsets via climate data services rather than attempting direct manipulation of full model archives.
1. Copernicus Climate Data Store (CDS)
What it provides:
How to access:
import cdsapi
# Initialize CDS API client
c = cdsapi.Client()
# Request temperature data under SSP2-4.5 scenario
c.retrieve(
'projections-cmip6',
{
'format': 'zip',
'temporal_resolution': 'monthly',
'experiment': 'ssp2_4_5',
'variable': '2m_temperature',
'model': ['access_cm2', 'mpi_esm1_2_hr', 'hadgem3_gc31_ll'],
'year': ['2030', '2040', '2050'],
'month': ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'],
'area': [55, 5, 45, 15], # North, West, South, East - covers Germany/Central Europe
},
'temperature_ssp245.zip')
Best for: European companies requiring regional European data with straightforward API access.
2. ESGF (Earth System Grid Federation)
What it provides:
Access method:
xarray, intake-esmimport intake
import xarray as xr
# Load ESGF catalog
cat_url = "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
col = intake.open_esm_datastore(cat_url)
# Search for specific experiment
cat_subset = col.search(
experiment_id=['ssp245', 'ssp585'],
table_id='Amon',
variable_id='tas', # near-surface temperature
source_id=['MPI-ESM1-2-HR', 'UKESM1-0-LL']
)
# Load data as xarray dataset
dset_dict = cat_subset.to_dataset_dict(zarr_kwargs={'consolidated': True})
Best for: Technical teams with climate science expertise requiring maximum flexibility and comprehensive variable access.
3. Climate Explorer / KNMI
What it provides:
Access method:
Best for: Initial screening and location-specific quick assessments without programming requirements.
4. World Bank Climate Change Knowledge Portal
What it provides:
Access method:
Best for: Companies operating in developing markets requiring socioeconomic context alongside climate data.
Individual climate models contain inherent uncertainty. Best practice uses multi-model ensembles averaging across multiple models to produce robust central estimates whilst capturing uncertainty ranges.
Python workflow for ensemble processing:
import xarray as xr
import numpy as np
import pandas as pd
# Load multiple model outputs
models = ['MPI-ESM1-2-HR', 'UKESM1-0-LL', 'ACCESS-CM2', 'MIROC6']
datasets = []
for model in models:
ds = xr.open_dataset(f'temperature_ssp245_{model}.nc')
# Select specific location (e.g., Frankfurt: 50.11°N, 8.68°E)
ds_location = ds.sel(lat=50.11, lon=8.68, method='nearest')
datasets.append(ds_location)
# Calculate ensemble mean and spread
ensemble = xr.concat(datasets, dim='model')
ensemble_mean = ensemble.mean(dim='model')
ensemble_std = ensemble.std(dim='model')
ensemble_5th = ensemble.quantile(0.05, dim='model')
ensemble_95th = ensemble.quantile(0.95, dim='model')
# Convert to pandas for analysis
df = pd.DataFrame({
'year': ensemble_mean.time.dt.year,
'temperature_mean': ensemble_mean['tas'].values - 273.15, # Convert K to °C
'temperature_5th': ensemble_5th['tas'].values - 273.15,
'temperature_95th': ensemble_95th['tas'].values - 273.15,
'uncertainty': ensemble_std['tas'].values
})
# Calculate decadal averages
df['decade'] = (df['year'] // 10) * 10
decadal_summary = df.groupby('decade').agg({
'temperature_mean': 'mean',
'temperature_5th': 'mean',
'temperature_95th': 'mean',
'uncertainty': 'mean'
}).round(2)
print(decadal_summary)
Output interpretation:
This approach provides defensible quantitative inputs for scenario analysis whilst transparently communicating uncertainty—essential for audit-ready documentation.
Global climate models operate at 100-250km resolution—useful for understanding regional trends but insufficient for facility-specific risk assessment. A manufacturing plant in southern Germany requires location-specific projections, not grid-cell averages covering multiple climate zones.
Downscaling approaches:
Statistical downscaling: Uses historical relationships between large-scale climate patterns and local observations to translate coarse model outputs into fine-scale projections.
Dynamical downscaling: Runs high-resolution regional climate models nested within global models, producing physically consistent fine-scale projections.
Bias correction: Adjusts raw model outputs to match observed historical climate at specific locations, then applies same correction to future projections.
For European locations:
The Euro-CORDEX initiative provides downscaled CMIP6 projections at 12km resolution covering Europe. Accessing these through Copernicus CDS:
import cdsapi
c = cdsapi.Client()
# Request high-resolution regional data
c.retrieve(
'projections-cordex-domains-single-levels',
{
'domain': 'europe',
'experiment': 'rcp_4_5',
'horizontal_resolution': '0_11_degree_x_0_11_degree',
'temporal_resolution': 'daily',
'variable': [
'2m_temperature',
'mean_precipitation_flux',
'maximum_2m_temperature_in_the_last_24_hours',
],
'gcm_model': 'mpi_m_mpi_esm_lr',
'rcm_model': 'gerics_remo2015',
'ensemble_member': 'r1i1p1',
'start_year': '2041',
'end_year': '2050',
'area': [50.5, 8.5, 50, 9], # Frankfurt region, high precision
},
'frankfurt_cordex.nc')
For precise facility coordinates:
Bias-correct global model outputs using historical observations:
import xarray as xr
import numpy as np
# Load historical observations (e.g., from DWD for German stations)
obs_hist = pd.read_csv('station_historical_temp.csv') # 1981-2010 baseline
obs_mean = obs_hist['temperature'].mean()
# Load model historical simulation
model_hist = xr.open_dataset('model_historical_1981_2010.nc')
model_hist_location = model_hist.sel(lat=50.11, lon=8.68, method='nearest')
model_hist_mean = model_hist_location['tas'].mean().values - 273.15
# Calculate bias
bias = obs_mean - model_hist_mean
# Load future projections
model_future = xr.open_dataset('model_ssp245_2041_2050.nc')
model_future_location = model_future.sel(lat=50.11, lon=8.68, method='nearest')
# Apply bias correction
model_future_corrected = model_future_location['tas'] - 273.15 + bias
# Calculate change from baseline
future_anomaly = model_future_corrected.mean().values - obs_mean
print(f"Projected temperature increase at facility: {future_anomaly:.2f}°C by 2045")
Commercial alternatives:
For companies without in-house technical capacity:
These platforms provide user-friendly interfaces whilst handling complex downscaling internally, suitable for companies prioritising speed over customisation.
The International Institute for Applied Systems Analysis (IIASA) maintains the authoritative SSP database with projections for:
R workflow for SSP data retrieval:
library(httr)
library(jsonlite)
library(tidyverse)
# IIASA SSP Database API endpoint
base_url <- "https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=welcome"
# Example: Query GDP projections for Germany under SSP2
query_url <- paste0(
"https://tntcat.iiasa.ac.at/SspDb/rest/v2.1/runs/",
"?model=OECD ENV-Growth&",
"scenario=SSP2-Baseline&",
"region=OECD90&",
"variable=GDP|PPP"
)
response <- GET(query_url)
ssp_data <- content(response, "parsed")
# Process into dataframe
df_ssp <- data.frame(
year = ssp_data$data$years,
gdp = ssp_data$data$values,
scenario = "SSP2",
region = "Germany"
)
# Calculate growth rates
df_ssp <- df_ssp %>%
mutate(gdp_growth = (gdp - lag(gdp)) / lag(gdp) * 100)
# Visualise trajectory
ggplot(df_ssp, aes(x=year, y=gdp)) +
geom_line(size=1.2, color="steelblue") +
labs(title="GDP Projection for Germany under SSP2-4.5",
x="Year", y="GDP (billion 2005 USD, PPP)") +
theme_minimal()
Key variables for business analysis:
| Variable | Business Application | SSP Database Location |
|---|---|---|
| GDP growth | Market size projections, demand forecasting | GDP|PPP by region |
| Population | Labour availability, consumer demographics | Population by age/education |
| Energy prices | Operating cost projections | Energy|Price by fuel type |
| Carbon prices | Transition risk quantification | Carbon Price by region/scenario |
| Agricultural productivity | Food industry supply chain risks | Crop|Production by commodity |
| Urbanisation rate | Real estate market evolution | Urban Population share |
Excel template structure for scenario comparison:
Download the Climate Scenario Analysis Template which includes:
Tab 1: Scenario Assumptions
Tab 2: Physical Risk Quantification
Tab 3: Transition Risk Quantification
Tab 4: Financial Integration
Tab 5: Reporting Dashboard
Formula example for carbon cost calculation:
# In Excel cell calculating 2030 carbon costs under SSP1-2.6:
=('Physical Data'!B15 * 'Assumptions SSP1-2.6'!$C$8) +
('Physical Data'!B16 * 'Assumptions SSP1-2.6'!$C$9)
Where:
- B15 = Scope 1 emissions (tonnes CO2e)
- B16 = Scope 2 emissions (tonnes CO2e)
- $C$8 = Carbon price 2030 under SSP1-2.6 (€/tonne)
- $C$9 = Carbon price 2030 for electricity (€/tonne, market-based)
This systematic approach enables transparent, auditable calculations that satisfy both internal decision-makers and external auditors.
Scenario analysis isn't one-time—effective climate risk management requires regular updates as:
Python automation framework:
import schedule
import time
from datetime import datetime
import logging
# Configure logging
logging.basicConfig(
filename='climate_data_pipeline.log',
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
def update_climate_projections():
"""
Automated workflow for updating climate scenario data
"""
try:
logging.info("Starting climate data update")
# Step 1: Check for new CMIP6 data releases
check_cmip6_updates()
# Step 2: Download updated projections for facility locations
for facility in facility_locations:
download_facility_data(facility['lat'], facility['lon'], facility['id'])
# Step 3: Recalculate ensemble statistics
process_ensemble_data()
# Step 4: Update SSP socioeconomic projections
update_ssp_database()
# Step 5: Recalculate financial impacts
recalculate_scenario_impacts()
# Step 6: Generate updated dashboards
generate_executive_dashboard()
# Step 7: Flag significant changes for review
identify_material_changes()
logging.info("Climate data update completed successfully")
# Send notification
send_update_notification("Climate scenario data updated - review flagged items")
except Exception as e:
logging.error(f"Update failed: {str(e)}")
send_error_alert(str(e))
# Schedule quarterly updates
schedule.every(3).months.do(update_climate_projections)
# Manual trigger option
if __name__ == "__main__":
update_climate_projections()
# Keep scheduler running
while True:
schedule.run_pending()
time.sleep(86400) # Check daily for scheduled tasks
Track observable indicators showing which scenario pathway is unfolding:
Climate indicators:
Socioeconomic indicators:
Automated signpost tracking:
import pandas as pd
import numpy as np
def track_scenario_alignment():
"""
Compare observed data against scenario projections
"""
# Load observed data (from monitoring sources)
obs_temp = load_observed_temperature()
obs_emissions = load_emissions_data()
obs_renewables = load_renewable_capacity()
# Load scenario projections
scenarios = ['SSP1-2.6', 'SSP2-4.5', 'SSP3-7.0', 'SSP5-8.5']
proj_temp = load_scenario_temperature(scenarios)
proj_emissions = load_scenario_emissions(scenarios)
proj_renewables = load_scenario_renewables(scenarios)
# Calculate alignment scores (RMSE from projections)
alignment_scores = {}
for scenario in scenarios:
temp_rmse = np.sqrt(np.mean((obs_temp - proj_temp[scenario])**2))
emis_rmse = np.sqrt(np.mean((obs_emissions - proj_emissions[scenario])**2))
renew_rmse = np.sqrt(np.mean((obs_renewables - proj_renewables[scenario])**2))
# Weighted composite score
alignment_scores[scenario] = (
0.4 * (1 / temp_rmse) +
0.4 * (1 / emis_rmse) +
0.2 * (1 / renew_rmse)
)
# Identify most aligned scenario
best_fit = max(alignment_scores, key=alignment_scores.get)
report = f"""
Scenario Alignment Analysis - {datetime.now().strftime('%Y-%m-%d')}
Current observations most closely align with: {best_fit}
Alignment scores (higher = better fit):
"""
for scenario, score in sorted(alignment_scores.items(), key=lambda x: x[1], reverse=True):
report += f"\n{scenario}: {score:.3f}"
return report
# Run analysis and distribute
alignment_report = track_scenario_alignment()
distribute_report(alignment_report, recipients=['strategy@company.com', 'risk@company.com'])
This systematic monitoring enables adaptive management—updating strategic responses as uncertainty resolves and particular scenarios become more or less likely.
Regulatory auditors scrutinise scenario analysis methodology, requiring transparent documentation of:
Data sources and versions:
Climate projections:
- Source: CMIP6 via Copernicus Climate Data Store
- Models used: MPI-ESM1-2-HR, UKESM1-0-LL, ACCESS-CM2, MIROC6 (4-model ensemble)
- Version: CMIP6 (2021 release)
- Variables: Near-surface temperature, precipitation, sea level
- Downscaling: Euro-CORDEX 12km for European facilities
- Access date: 2024-11-15
Socioeconomic data:
- Source: IIASA SSP Database v2.0
- Scenarios: SSP1-2.6, SSP2-4.5, SSP5-8.5
- Variables: GDP, population, carbon prices, energy prices
- Regions: Germany, European Union, Global
- Access date: 2024-11-20
Scenario selection rationale:
Scenarios selected for analysis:
1. SSP1-2.6 (Paris-aligned, 1.5-2°C):
Rationale: Assesses transition risks under aggressive climate policy consistent
with EU policy goals. Tests resilience of business model to rapid decarbonisation,
high carbon pricing (€150-300/tonne by 2040), and technology disruption.
2. SSP2-4.5 (baseline, 2.4-2.7°C):
Rationale: Represents most likely trajectory based on current policy commitments
and technology trends. Balances physical and transition risks for medium-term
planning (2030-2050). Used as central case for financial planning.
3. SSP5-8.5 (high emissions, 4.4°C):
Rationale: Stress-tests physical risk resilience under severe climate impacts.
Despite lower likelihood (given policy momentum and renewable economics),
retained for EU Taxonomy physical risk criteria and tail-risk assessment.
Used specifically for infrastructure resilience evaluation and adaptation
investment prioritisation.
Assumptions and limitations:
Key assumptions:
- Climate sensitivity: Using CMIP6 multi-model mean (ECS = 3.7°C per CO2 doubling)
- Facility locations: Static (no relocation assumed except where specifically modelled)
- Supply chain: Current suppliers maintained unless specified in adaptation scenarios
- Technology availability: Following SSP narratives for technology deployment rates
Limitations:
- Does not model tipping points or abrupt climate shifts
- Socioeconomic projections subject to high uncertainty beyond 2050
- Regional downscaling introduces additional uncertainty (±20% typical)
- Cascade and systemic risks partially captured but not comprehensively modelled
Confidence levels:
- Near-term (2030): High confidence in physical projections, medium in socioeconomic
- Medium-term (2040-2050): Medium confidence in both physical and socioeconomic
- Long-term (2050+): Low-medium confidence, used primarily for directional insights
CSRD reports require accessibility for non-technical stakeholders whilst maintaining technical rigour for auditors. Effective dual-layered approach:
Executive Summary (CSRD main report):
Technical Annex (supporting documentation):
Example executive summary language:
"We assessed climate resilience using three scenarios representing different levels of global climate action: an optimistic pathway limiting warming to 1.5-2°C (SSP1-2.6), a middle-ground scenario reaching 2.4-2.7°C (SSP2-4.5), and a high-impact scenario exceeding 4°C (SSP5-8.5).
Under the optimistic scenario, our primary risks are transitional—carbon pricing reaching €150-200 per tonne by 2040 would increase energy costs by 25-30%, requiring €80-100 million investment in efficiency improvements and renewable energy procurement between 2025-2035. These investments are financially viable with 7-9 year payback periods.
Under middle-ground and high-impact scenarios, physical risks dominate. By 2050, our southern European facilities face 15-20 additional extreme heat days annually, reducing productivity 3-5% without adaptation. Coastal logistics infrastructure faces flooding exposure increasing expected annual losses from €2 million (current) to €8-12 million (2050). We have prioritised €45 million in adaptation investments addressing the highest-exposure assets, with detailed implementation timelines in Section 4.2.
Our strategy remains resilient across all scenarios through a portfolio of no-regret actions (energy efficiency, supply chain diversification) combined with scenario-contingent investments triggered by observable indicators."
This structure satisfies both regulatory requirements and practical decision-making needs.
Problem: Raw CMIP6 datasets measure in terabytes. Processing requires significant computational resources and technical expertise that many organisations lack.
Solutions:
Option A: Use pre-processed data services
Option B: Cloud-based processing
Option C: Engage technical consultants
Problem: Climate models output temperature, precipitation, sea level—not business-relevant metrics like production downtime, damage costs, or revenue losses.
Solutions:
Develop damage functions: Map physical hazards to operational impacts using historical data:
def calculate_heat_productivity_loss(temperature, baseline_temp=25):
"""
Estimate productivity loss from heat stress
Based on research: 2% productivity loss per °C above 25°C threshold
"""
if temperature <= baseline_temp:
return 0
else:
excess_temp = temperature - baseline_temp
productivity_loss = min(0.02 * excess_temp, 0.30) # Cap at 30% loss
return productivity_loss
# Apply to facility data
facility_impact = []
for year in projection_years:
temp = facility_temperature[year]
loss_pct = calculate_heat_productivity_loss(temp)
annual_revenue = facility_revenue # Constant revenue assumption
impact_value = annual_revenue * loss_pct
facility_impact.append({'year': year, 'impact_€': impact_value})
total_npv_impact = calculate_npv(facility_impact, discount_rate=0.05)
Use sectoral impact literature:
Engage operational teams: Internal experts know actual vulnerability better than generic models:
Problem: Climate science advances rapidly. Analysis using 2019 CMIP5 data is now outdated but updating requires repeating entire workflow.
Solutions:
Version control everything:
# Git repository structure
climate-scenarios/
├── data/
│ ├── cmip6_raw/ # Original downloaded files
│ ├── processed/ # Cleaned and downscaled data
│ └── ssp/ # Socioeconomic projections
├── scripts/
│ ├── 01_download.py # Data acquisition
│ ├── 02_process.py # Cleaning and calculations
│ ├── 03_visualise.py # Dashboard generation
│ └── 04_report.py # CSRD output formatting
├── outputs/
│ ├── figures/
│ ├── tables/
│ └── csrd_disclosure.docx
├── documentation/
│ ├── methodology.md
│ ├── data_sources.md
│ └── changelog.md
└── README.md
Document everything:
Schedule regular reviews:
For comprehensive support navigating these implementation challenges, return to our climate scenarios hub for strategic framework guidance.
Challenge: European manufacturing company with 12 facilities across 6 countries needs site-specific climate projections for all RCP scenarios.
Workflow:
import pandas as pd
import xarray as xr
# Facility inventory
facilities = pd.DataFrame({
'site_id': ['DE01', 'FR01', 'IT01', 'PL01', 'ES01', 'NL01'],
'lat': [51.0, 48.8, 45.5, 52.2, 41.4, 52.1],
'lon': [7.0, 2.3, 9.2, 21.0, 2.2, 5.2],
'country': ['Germany', 'France', 'Italy', 'Poland', 'Spain', 'Netherlands']
})
# Download Euro-CORDEX data for each location
for idx, facility in facilities.iterrows():
# Temperature projections
download_cordex_data(
variable='temperature',
lat=facility['lat'],
lon=facility['lon'],
scenarios=['rcp26', 'rcp45', 'rcp85'],
output_file=f"climate_data_{facility['site_id']}.nc"
)
# Extreme heat days (>35°C)
calculate_extreme_heat_days(
input_file=f"climate_data_{facility['site_id']}.nc",
threshold=35,
output_file=f"heat_days_{facility['site_id']}.csv"
)
# Precipitation intensity (heavy rain events)
calculate_heavy_precipitation(
input_file=f"climate_data_{facility['site_id']}.nc",
percentile=95,
output_file=f"heavy_rain_{facility['site_id']}.csv"
)
# Aggregate for portfolio view
portfolio_risk = compile_multi_site_analysis(facilities)
generate_executive_dashboard(portfolio_risk, output='manufacturing_climate_risk.xlsx')
Key outputs:
Challenge: Asset manager with €5 billion portfolio needs to assess climate transition risk across 200+ holdings under multiple scenarios.
Workflow using NGFS scenarios:
library(tidyverse)
library(readxl)
# Load portfolio holdings
portfolio <- read_excel("portfolio_holdings.xlsx")
# Load NGFS scenario data (carbon prices, sectoral impacts)
ngfs_carbon_prices <- read_csv("ngfs_carbon_prices.csv")
ngfs_sector_impacts <- read_csv("ngfs_sector_impacts.csv")
# Calculate transition risk exposure
transition_risk <- portfolio %>%
left_join(ngfs_sector_impacts, by = c("sector", "region")) %>%
mutate(
# Carbon cost impact under different scenarios
carbon_cost_orderly = revenue * carbon_intensity * ngfs_carbon_prices$orderly_2030,
carbon_cost_disorderly = revenue * carbon_intensity * ngfs_carbon_prices$disorderly_2030,
# Revenue risk from market transition
revenue_at_risk_orderly = revenue * sector_transition_risk_orderly,
revenue_at_risk_disorderly = revenue * sector_transition_risk_disorderly,
# Combined transition risk score
total_risk_orderly = carbon_cost_orderly + revenue_at_risk_orderly,
total_risk_disorderly = carbon_cost_disorderly + revenue_at_risk_disorderly
)
# Portfolio-level metrics
portfolio_summary <- transition_risk %>%
summarise(
total_nav = sum(market_value),
avg_carbon_intensity = weighted.mean(carbon_intensity, market_value),
nav_at_risk_orderly = sum(total_risk_orderly * market_value) / total_nav,
nav_at_risk_disorderly = sum(total_risk_disorderly * market_value) / total_nav
)
# High-risk holdings requiring engagement
high_risk_holdings <- transition_risk %>%
filter(total_risk_disorderly > 0.15 * revenue) %>% # >15% revenue at risk
select(company, sector, total_risk_orderly, total_risk_disorderly) %>%
arrange(desc(total_risk_disorderly))
write_csv(high_risk_holdings, "engagement_priority_list.csv")
Key outputs:
Q: Where can I download pre-processed RCP and SSP data for business use?
The most accessible sources for corporate applications are: (1) Copernicus Climate Data Store (CDS) at climate.copernicus.eu for processed CMIP6 climate projections with straightforward API access; (2) IIASA SSP Database at tntcat.iiasa.ac.at/SspDb for socioeconomic variables including GDP, population, energy, and carbon prices; (3) World Bank Climate Change Knowledge Portal at climateknowledgeportal.worldbank.org for country-level summaries combining climate and socioeconomic data. All three sources provide free access with registration. For European-specific data, the Regional Climate Atlas Germany at www.climate-service-center.de offers 401 district-level projections particularly valuable for facility-specific assessments.
Q: What programming skills are required to process climate scenario data?
Basic competency with Python or R enables most corporate applications. Essential skills include: data manipulation (pandas in Python, tidyverse in R), working with NetCDF files (xarray in Python, ncdf4 in R), basic statistical analysis (calculating means, percentiles, trends), and visualisation (matplotlib/seaborn in Python, ggplot2 in R). For companies without internal programming capacity, Excel-based approaches using downloaded CSV files are feasible for simpler analyses. Commercial platforms like Jupiter Intelligence or ClimateAi provide graphical interfaces eliminating programming requirements entirely, though with less customisation flexibility.
Q: How do I validate that my scenario analysis methodology is CSRD-compliant?
CSRD compliance requires: (1) Using at least two scenarios including one Paris-aligned pathway (RCP 1.9 or 2.6), (2) Assessing multiple time horizons (minimum 2030, 2040, 2050), (3) Evaluating both physical and transition risks with quantification where feasible, (4) Transparent documentation of data sources, assumptions, and limitations, (5) Clear connection between scenario findings and strategic decisions. Have your methodology reviewed by sustainability auditors familiar with ESRS E1 requirements. Our CSRD compliance framework provides detailed technical specifications that auditors expect to see documented.
Q: What's the difference between using raw CMIP6 data versus commercial climate risk platforms?
Raw CMIP6 data provides maximum flexibility and no ongoing licensing costs but requires significant technical expertise, computational resources, and time investment (typical setup: 1-3 months for experienced teams). Commercial platforms offer processed, business-ready outputs with user-friendly interfaces, integrated SSP data, and turnkey reporting tools (typical setup: days to weeks). Cost trade-off: CMIP6 is free but staff time-intensive; commercial platforms range €5,000-50,000+ annually depending on features and company size. Most organisations benefit from hybrid approaches—using commercial platforms for initial assessments whilst developing internal CMIP6 capabilities for specific deep-dive analyses or customisation needs.
Q: How precise should my facility-level climate projections be?
Precision requirements depend on decision context and risk materiality. For initial screening and CSRD disclosure, regional projections (50-100km resolution) typically suffice to identify material risks. For major capital investments (facility siting, significant infrastructure) or high-exposure locations (coastal, water-stressed), higher resolution (10-20km) improves decision confidence. Remember that climate projections contain inherent uncertainty—multi-model ensembles showing ±1.5°C range at a location should inform adaptation strategies emphasising flexibility rather than point-optimised designs. Focus precision investment on factors most material to your specific risks: coastal companies prioritise sea level rise precision; agricultural companies prioritise precipitation patterns; manufacturing prioritises extreme heat events.
Q: Can I use simplified climate calculators instead of accessing raw scenario data?
Simplified calculators work for high-level screening but rarely satisfy CSRD audit requirements or enable sophisticated strategy development. Most calculators provide limited scenario coverage, use outdated CMIP5 rather than CMIP6 data, lack SSP socioeconomic integration, and don't support site-specific analysis at required precision. They're appropriate for initial awareness-building or very small companies with minimal exposure. For CSRD-covered companies or those with material climate exposure, accessing authoritative data sources (Copernicus, IIASA) directly or through commercial platforms provides necessary rigour. That said, tools like the KNMI Climate Explorer offer good middle ground—simplified interface with access to robust underlying data.
Q: How do I communicate technical climate analysis to non-technical executives?
Effective executive communication follows this structure: (1) Start with business impacts, not climate science—lead with "€50M exposure under high-emissions scenario" rather than "RCP 8.5 projects 4.2°C warming"; (2) Visualise scenario comparisons using clear dashboards showing how risk varies across pathways; (3) Link quantified risks to specific strategic decisions and capital allocation priorities; (4) Present uncertainty ranges transparently whilst recommending clear action (e.g., "Under all scenarios analysed, energy efficiency investments deliver 6-9 year payback"); (5) Provide technical appendix for those wanting methodological detail. Executive summaries should translate climate jargon: use "severe warming scenario" instead of "RCP 8.5," "rapid decarbonisation pathway" instead of "SSP1-2.6."
Q: What update frequency is required for scenario analysis to remain current?
Formal scenario analysis updates should occur every 2-3 years to incorporate: new IPCC Assessment Reports (published ~7 year cycles), updated CMIP phases, improved regional downscaling, and evolved SSP socioeconomic projections. Additionally, update after: material business changes (acquisitions, new facilities, market entries), significant policy shifts (major climate legislation, carbon pricing changes), or extreme climate events testing assumptions. Between formal updates, maintain quarterly monitoring of key indicators comparing observations against scenario projections—this "signpost tracking" identifies when accelerated updates are warranted. First-time implementations should plan interim updates after 12-18 months as methodology refinement and stakeholder feedback identify improvements.
Mastering the practical data skills for climate scenario analysis transforms regulatory compliance from burden into competitive advantage. Companies that can independently access CMIP6 projections, process SSP socioeconomic data, and integrate findings into financial models gain strategic benefits beyond CSRD compliance:
Decision-Making Confidence: Quantified scenario-based risks enable defensible capital allocation, facility planning, and strategic positioning rather than vague qualitative assessments.
Audit Resilience: Transparent, reproducible data workflows satisfy increasingly rigorous regulatory scrutiny whilst reducing external consulting dependencies and costs.
Adaptive Capacity: Internal technical capabilities enable rapid re-analysis as climate science evolves, policies shift, or business circumstances change—maintaining analysis currency without vendor dependencies.
Stakeholder Credibility: Technical sophistication signals operational maturity to investors, customers, and regulators, differentiating sophisticated risk management from superficial compliance.
The initial investment in building these capabilities—whether through internal skill development, commercial platform adoption, or expert consulting support—pays dividends through reduced analysis costs, improved strategic insights, and enhanced organisational resilience.
For companies at the beginning of this journey, start with manageable steps: access pre-processed Copernicus data for your top-exposure facilities, download SSP GDP and carbon price trajectories relevant to your markets, build simple Excel models linking climate variables to key operational metrics. Build complexity progressively as capability and confidence grow.
For organisations requiring comprehensive implementation support—from data infrastructure setup through CSRD-compliant reporting—Fiegenbaum Solutions provides end-to-end guidance combining climate science expertise, technical implementation, and strategic interpretation tailored to your industry and exposure profile.
The technical barrier to sophisticated scenario analysis has never been lower. The strategic imperative to master these capabilities has never been higher. Companies that act now position themselves to thrive in the climate-affected economy ahead.
Return to our complete climate scenarios framework for strategic guidance on building comprehensive, scenario-informed climate resilience.
Ready to implement robust climate scenario analysis with expert technical support? Contact Fiegenbaum Solutions for customised data integration, methodology development, and CSRD-compliant reporting frameworks that transform climate data into strategic advantage.