Answer
Solar interannual variability is the year-to-year swing in annual irradiance at one site, measured as the standard deviation divided by the long-term mean. Solargis lists 1.3% at Calama, Chile and 3.8% at Kosice, Slovakia for a single year. It widens the gap between P50 and P90 energy estimates, which lenders use to size debt.
Last updated: November 2026
SurgePV publishes this post and sells solar design software.
Most production models default to a Typical Meteorological Year (TMY) or a long-term average. That is a fair central estimate, but it hides a basic fact: individual years differ from the average. When a project is financed against a single P50 number, a run of below-average years can strain returns and customer relationships.
This guide explains what solar interannual variability is, what drives it, how to calculate it and how to account for it in sizing, storage and financial modeling. It also shows where solar design software fits, and where it does not.
In this guide, you will learn:
- What solar interannual variability means and how it is measured
- How it differs from intraday, seasonal and long-term trends
- The climate modes and local factors behind year-to-year swings
- How to calculate the coefficient of variation and turn it into P50/P90 estimates
- Why it matters for residential, commercial and off-grid designs
- Practical design responses and common misconceptions
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What Is Solar Interannual Variability?
Solar interannual variability measures how much annual solar radiation deviates from its long-term average at one location. Solargis describes it as year-to-year fluctuations in solar radiation, usually expressed as a percentage of the long-term average, caused by weather cycles and random variation.
It is usually expressed as the coefficient of variation (CV): the standard deviation of annual values divided by the multi-year mean, multiplied by 100.
For example, if a site has a long-term average of 1,700 kWh/m² per year with a standard deviation of 60 kWh/m², the CV is about 3.5%. If annual values are roughly normally distributed, about two-thirds of years fall within one standard deviation of the mean.
A TMY file compresses many years into one representative year. It captures the average seasonal cycle but does not show how far individual years can swing. For a 25-year asset, those swings show up as real cash-flow differences.
The concept applies to every solar resource component:
- Global Horizontal Irradiance (GHI): total shortwave radiation on a horizontal surface. Most PV yield calculations start here.
- Direct Normal Irradiance (DNI): direct beam radiation perpendicular to the sun’s rays. It matters most for concentrating solar and tracking.
- Diffuse Horizontal Irradiance (DHI): scattered radiation from the sky. It dominates in cloudy climates.
- Plane of Array (POA) irradiance: GHI, DNI and DHI transposed to the actual module tilt and azimuth.
PVsyst documents its default assumption for annual variability. Before version 8.1.4 the default was 2.5% for sites at or above 1,700 kWh/m² per year, rising linearly to 8% at 900 kWh/m². Since 8.1.4 the default comes from a linear fit to GEBA measurements, giving 5.1% at 900 kWh/m² and 3.5% at 1,700 kWh/m². PVsyst advises computing the variability for your own location because typical values can differ significantly from the model.
How Interannual Variability Differs from Other Solar Variability Timescales
Solar variability operates on several timescales with different causes and design implications. Confusing them leads to the wrong mitigation. The categories below follow Solargis’s solar resource variability guide.
| Timescale | Typical period | Primary cause | Design impact |
|---|---|---|---|
| Short-term | 1 to 60 minutes | Cloud passage, aerosol events | Ramp-rate control, inverter response, grid stability |
| Intraday | Each day vs the typical daily pattern | Weather systems, fog, storms | Daily energy storage, maintenance scheduling |
| Seasonal | Within a year | Orbital geometry, a predictable pattern | System sizing, tilt and azimuth optimization |
| Interannual | Whole years vs the long-term average | Weather cycles, random variation | P50/P90 estimates, financial risk, debt sizing |
| Long-term | Decades | Directional trends, multi-decadal cycles | Asset-life revenue, refinancing assumptions |
Interannual variability sits between seasonal and long-term effects. Unlike seasonal variability, it cannot be predicted from the calendar. Unlike a long-term trend, it is not a directional shift. It is variation around the mean, and its size depends strongly on location.
What Causes Year-to-Year Solar Variation?
Year-to-year variation follows the same large-scale atmospheric patterns that drive rainfall, temperature and storm tracks. The usual suspects are the following.
El Niño-Southern Oscillation (ENSO)
ENSO shifts sea surface temperatures in the tropical Pacific. El Niño years tend to increase cloud cover and rainfall in parts of the Americas and Australia, which lowers irradiance there. La Niña years often do the opposite. The Southern Oscillation Index (SOI) is the pressure-based measure of this cycle.
North Atlantic Oscillation (NAO) and Arctic Oscillation (AO)
These modes influence winter storm tracks across the North Atlantic and Europe. Depending on the phase, they shift cloudier or clearer conditions between northern and southern Europe. The effect is strongest in winter.
Pacific Decadal Oscillation (PDO)
PDO is a longer-lived pattern of sea surface temperature variability in the North Pacific. Because it persists for years, it can create multi-year anomalies in solar resource in the regions it influences.
Krakauer and Cohan (Resources, 2017) examine interannual variability and seasonal predictability of wind and solar resources, including links to climate indices. Read the paper for regional correlations rather than relying on a single number.
Local and Regional Factors
Large-scale modes do not act alone. Regional factors include:
- Monsoon strength: a stronger or weaker monsoon changes cloud duration and aerosol washout.
- Dust and aerosol loading: Saharan dust, wildfire smoke and urban haze reduce irradiance in the years they are heavy.
- Volcanic eruptions: major stratospheric eruptions inject sulfate aerosols that reflect sunlight and can dim surface radiation for a year or more.
- Snow cover and albedo: persistent snow raises ground reflectance, which can change bifacial gain from year to year.
How to Quantify Interannual Variability
The standard method uses the coefficient of variation. The steps:
- Collect annual solar radiation totals for at least 10 years. The World Bank solar guidebook says at least 10 years of data are usually required for a reasonably confident assessment.
- Calculate the mean annual value.
- Calculate the standard deviation of the annual values.
- Divide the standard deviation by the mean and multiply by 100.
Formula: CV (%) = (Standard Deviation / Mean) × 100
For probabilistic estimates, Solargis gives this approach. Take the standard deviation of yearly values over the N years available and divide by the square root of N. Divide by the series average to get a percentage, then multiply by 1.282 to convert to the P90 level. For a single year, N = 1. Solargis notes that assuming PV output variability equals GHI variability overlooks cell temperature, system inefficiencies and partial shading.
Example Calculation
This worked example uses assumed inputs, not a measured site. Assume a long-term mean GHI of 1,883 kWh/m² per year and a yearly standard deviation of 41 kWh/m² over 27 years.
| Step | Calculation | Result |
|---|---|---|
| Single-year standard deviation | Given | 41 kWh/m² |
| 10-year average standard deviation | 41 / √10 | 12.97 kWh/m² |
| As a share of the mean | 12.97 / 1,883 | 0.69% |
| 10-year P90 reduction | 0.69% × 1.282 | 0.88% |
| Single-year P90 reduction | 41 / 1,883 × 1.282 | 2.79% |
The 10-year average has a P90 value about 0.88% below the long-term mean, while a single year’s P90 is about 2.79% below it. Match the averaging period to what you are testing: a single bad year for cash-flow stress, or a multi-year average for a longer-horizon estimate.
Typical Values by Site
Single-year GHI variability in Solargis’s sample sites ranges from 1.3% to 3.8%:
| Site | Country | 1-year GHI variability |
|---|---|---|
| Calama | Chile | 1.3% |
| Upington | South Africa | 1.3% |
| Kurnool | India | 2.3% |
| Fresno | United States | 2.5% |
| Kosice | Slovakia | 3.8% |
- Calama, Chile1.3%
- Upington, South Africa1.3%
- Kurnool, India2.3%
- Fresno, United States2.5%
- Kosice, Slovakia3.8%
Source: Solargis, Solar Resource Variability (1-year period), checked November 2026.
The World Bank guidebook adds that research shows a coefficient of variation below 4% for southern Europe, and it tabulates NASA-derived values over 22 years for four Indian locations:
| Location | Years of data | Coefficient of variation |
|---|---|---|
| Chennai | 22 | 2.2% |
| Mumbai | 22 | 2.5% |
| New Delhi | 22 | 3.4% |
| Sivaganga | 22 | 4.3% |
These tables are from different sources and methods, so do not mix them in one ranking. Both point the same way: a few percent in sunny and semi-arid climates, with cloudier or monsoon-affected sites at the higher end.
Averaging also matters. Solargis reports that for Kosice, variability falls from 3.8% over one year to 1.7% over five years, 0.5% over ten and 0.1% over 25.
- 1 year3.8%
- 5 years1.7%
- 10 years0.5%
- 25 years0.1%
Source: Solargis, Solar Resource Variability, checked November 2026.
Key Takeaway
A bad single year is far more likely than a bad decade. Size debt service and cash buffers for the single-year case, and use the multi-year average for lifetime energy. Do not use one number for both questions.
Why Interannual Variability Matters for System Design
A designer who looks only at long-term average production misses part of the risk. Variability feeds every major design and financial decision, which is why it is a central input in energy forecasting.
P50 vs. P90 Production Estimates
P50 is the median annual production, exceeded in half of all years. P90 is the level exceeded in 90% of years. Lenders use P90 to size debt because loan payments must be met even in poor solar years. Solargis defines P90 as the mean minus 1.282 times the standard deviation.
The P50 to P90 gap comes mainly from interannual variability and model uncertainty. A site with low variability has a narrow spread, and a site with higher variability has a wider one, which reduces the bankable production figure. For more, see the P50/P90 energy yield explainer.
Energy Storage Sizing
For off-grid and hybrid systems, variability is a reliability risk as well as a financial one. A battery bank sized for the average year may be drained in a cloudy year. Run production for several historical years, not just the TMY, when sizing storage for critical loads.
Inverter and AC Sizing
An unusually bright year can increase clipping if the inverter is undersized, and a dull year raises fixed O&M cost per kWh. Test both in a sensitivity analysis.
Customer Expectations
A homeowner comparing proposals often sees one production number. If it is a P50 estimate, a below-average first year can disappoint. Present a range instead. For example (illustrative figures): “Expected production is 9,500 kWh a year, and typical years may fall between 8,900 and 10,100 kWh.”
Pro Tip
Put a P90 line under the P50 yield in every proposal and tell the customer in one sentence what a below-average year would do to their first-year savings. Customers who hear the range up front are less likely to be surprised by a dull year.
Designing for Variability: Sizing, Storage and Financial Assumptions
The right response depends on project type and risk tolerance. These are practical strategies.
Use Multi-Year Datasets Where Possible
TMY is standard for long-term modeling, but it is a synthetic average. For bankable or high-stakes projects, add multi-year time series from a satellite or reanalysis provider. Running a design through 10 or more historical years shows the spread of annual output.
Model Both P50 and P90 Scenarios
Include both in every commercial proposal. Feed the P90 case into the financial model used for debt sizing. Use P50 for customer-facing savings only if the customer understands it is an average, not a guarantee.
Add Sizing Margins for Critical Loads
For off-grid, agricultural or backup systems, size the array and battery for a below-average year. One approach is to design for the P90 production year when load shedding is unacceptable.
Account for Compounding Losses
Irradiance variability does not map one-to-one to PV output variability. Module temperature, inverter behavior, soiling and shading all respond to weather. A cloudy year may also be cooler, which partly offsets lower irradiance. A hot, clear year may bring more clipping and temperature derating. Good simulation captures these interactions.
Update Degradation Assumptions
Combine your production estimate with the module’s warranted degradation rate. If the starting estimate is P50, a median degradation assumption gives a median lifetime output. For conservative financing, pair a P90 start with a cautious degradation assumption. See the degradation rate definition and solar panel degradation rates.
Revisit Financial Models After the First Year
The first operating year is a reality check. Compare actual production with the P50 to P90 band. If it falls below P90, the resource or the model may have been overestimated. If it is above P50, the site may be better than expected. Update forecasts before refinancing or expansion.
Stress-test a proposal against a low-resource year
Rerun payback with a lower yield in the same model, and show customers the range before they sign.
Book a DemoWant the price first? Read the SurgePV pricing page.
Regional Differences in Interannual Variability
Not every location carries the same risk. Use the pattern to set design margins and to explain risk to clients, but use site data for the number.
Arid and Semi-Arid Regions
Calama and Upington show the lowest single-year variability in the Solargis sample, at 1.3%. Cloud cover is rare, so annual totals stay close to the long-term mean and P90 sits close to P50.
Temperate Climates
Cloud cover and storm tracks shift from year to year, so variability is higher. Solargis lists 3.8% at Kosice, Slovakia, and the World Bank cites research putting southern Europe below 4%. Cloudier sites need wider P50 to P90 bands.
Monsoon and Tropical Climates
Monsoon strength varies from year to year, which changes cloud cover and rainfall. The World Bank’s Indian values run from 2.2% in Chennai to 4.3% in Sivaganga, so even within one country the spread is wide. Where solar is combined with wind or other renewables, check whether shortfalls coincide, because backup or grid interconnection matters more if they do.
High-Latitude Locations
At high latitudes the winter resource is small, so a small absolute change in winter irradiance is a large percentage change. Check winter months separately when the load is winter-heavy.
Common Misconceptions About Interannual Variability
Several myths persist in the installer community. Correcting them prevents design and financial errors.
Myth 1: A TMY Year Represents a Typical Year
A TMY is a synthetic composite. It selects months from different years to build a statistically average year, so it does not match any single real year and does not show year-to-year swings.
Myth 2: Interannual Variability Is the Same Everywhere
It varies by site. Solargis’s sample runs from 1.3% to 3.8%, and a generic default will understate risk in some places and overstate it in others. Use site-specific values from satellite databases or local measurements.
Myth 3: PV Output Variability Equals Solar Resource Variability
Solargis warns that this simplification can underestimate real PV output variability in climates with significant temperature swings, because it ignores cell temperature, system losses and partial shading.
Myth 4: Long-Term Trends Cancel Out Variability
A long-term trend shifts the mean gradually, while interannual variability is noise around that mean. Both matter and need different treatment.
Myth 5: Storage Eliminates Interannual Risk
Storage helps with daily and seasonal mismatch. It cannot cover a multi-year shortfall unless the battery is oversized far beyond economic sense. For multi-year risk, conservative financial sizing and backup supply do more than extra battery capacity.
Where SurgePV Fits
SurgePV’s public product scope covers 3D roof modeling, array layout, shading analysis, energy-yield modeling, financial modeling and proposal generation. Results depend on the source data, assumptions and equipment models you use, and the responsible engineer still reviews them.
SurgePV does not publish a multi-year weather workflow or an automatic P50 and P90 conversion. A practical way to use it with the method above:
- Run the base energy-yield case with your chosen weather file.
- Calculate site variability with a resource provider, then run a lower-yield sensitivity case in the generation and financial tool to see the effect on payback.
- Use shading analysis to check shade losses, since they do not scale evenly with irradiance.
- Show the range in the solar proposal tool so customers see more than one number.
Ask about specific probabilistic features in a demo, because they are not part of the public scope.
Next Steps
Pick one live proposal and add a P90 line and a low-year payback case to it this week. To see how yield and financial modeling work together on a real project, book a SurgePV demo or read the PVsyst report bankability guide.
Sources
- Solargis: Solar Resource Variability
- Solargis: P50, P90 and Other Scenarios
- PVsyst: Interannual Variability
- Krakauer and Cohan: Interannual Variability and Seasonal Predictability of Wind and Solar Resources, Resources, 2017
- World Bank: Utility Scale Solar Power Plants, A Guide for Developers and Investors
- IEA: Managing Seasonal and Interannual Variability of Renewables
Frequently Asked Questions
What is solar interannual variability?
It is the year-to-year fluctuation in solar radiation at a specific location, usually shown as a percentage of the long-term average. It is calculated as the standard deviation of annual values divided by their mean. Solargis lists single-year values from 1.3% at Calama, Chile to 3.8% at Kosice, Slovakia.
How does interannual variability affect solar project finances?
It widens the gap between P50 and P90 energy estimates. A site with higher variability has a lower P90 value, which can reduce the debt a lender will offer against the project. A model built only on P50 can overstate cash flow in below-average years.
What is a normal interannual variability value for solar GHI?
Published single-year values are a few percent. Solargis lists 1.3% at Calama and Upington, 2.5% at Fresno and 3.8% at Kosice, and the World Bank cites 2.2% to 4.3% for Indian locations. Always calculate the value for your own site, because PVsyst warns that typical values can differ significantly from its model.
How many years of data are needed to calculate interannual variability?
The World Bank solar guidebook says at least 10 years of data are usually required for a reasonably confident assessment. Longer records reduce the influence of single extreme years. Use local station or satellite data for the site, not a generic default.
Is interannual variability the same as seasonal variability?
No. Interannual variability compares whole years, while seasonal variability is the predictable pattern across months driven by Earth’s orbit and tilt. Intraday variability compares each day’s profile with the typical daily pattern. Each needs a different design and modeling response.
How can designers reduce risk from interannual variability?
Run P50 and P90 scenarios, use multi-year weather data where the project is high-stakes, and feed the P90 case into debt sizing. For off-grid or backup loads, size for a below-average year. Update financial models with site-specific variability, not generic defaults.
Does interannual variability matter for grid-tied and off-grid systems differently?
Yes. A grid-tied system’s revenue averages over its life, so one bad year mostly affects cash flow. An off-grid system with fixed loads and limited storage has to be sized for a low-production year to avoid load shedding.
Where this fits
This article is part of SurgePV's Solar Design hub, which works through the topic from first principles to the decisions a project team actually has to make.
