This document summarizes seasonal forecasts for global solar PV energy from the Climate Forecasting Unit. It identifies regions with the highest solar resource potential and variability in spring (March-May), and assesses the skill of climate forecast models to predict spring solar radiation levels up to 1 month in advance. Several key areas are identified where solar forecasts are most skillful and could provide valuable information for decision-making, including parts of South America, Southeast Asia, Southern Africa, Northern Australia and Western Europe. An example operational forecast for spring 2011 illustrates probabilistic predictions of above-, below- or normal solar radiation levels.

1. Climate Forecasting Unit
SPRING
Seasonal Forecasts for
Global Solar PV Energy
Melanie Davis, Francisco Doblas-Reyes, Fabian Lienert

2. Climate Forecasting Unit
Fig. S1.1.1: Spring solar GHI availability from 1981-2011 (ERA-Interim)
m/s
Stage A: Solar GHI (Global Horizontal Irradiance) Resource Assessment
Solar PV energy potential: Where is it the sunniest?
Dark red regions of this map shows where global solar GHI is highest in spring, and lighter yellow regions
where it is lowest.
N.b. This information is based on reanalysis* data (ERA-Interim) not direct observations.
* Reanalysis information comes from an objective combination of observations and numerical models that simulate one or more aspects of the Earth system, to
generate a synthesised estimate of the state of the climate system and how it changes over time.
SPRING Solar PV Forecasts
(March + April + May)

3. Climate Forecasting Unit
Fig. S1.1.2: Spring solar GHI inter-annual variability from 1981-2011 (ERA-Interim)
m/s
Stage A: Solar GHI Resource Assessment
Solar PV energy volatility: Where does the solar radiation vary the greatest?
Darker red regions of this map show where global solar GHI varies the most from one year to the next in
spring, and lighter yellow regions where it varies the least.
N.b. This information is based on reanalysis* data (ERA-Interim) not direct observations.
SPRING Solar PV Forecasts
(March + April + May)

4. Climate Forecasting Unit
Europe
Spring solar GHI availability Spring solar GHI inter-annual variability
m/s
Areas of
interest: N.
Continent
S.Sahal/
Zimbabwe/
Zambia/
Mozambique
S.E.Continent/
N.E.Pakistan/
N.E.Afghanistan/
Tajikistan
N-N.E.
Australia
S.America Africa Asia Australia
N.E.Mexico/
W.USA
N.America
France/
N.W.Spain
Stage A: Solar GHI Resource Assessment
Where is solar PV energy resource potential and variability highest?
By comparing both the spring global solar GHI resource availability and inter-annual variability, it can be seen
that there are several key areas (listed above) where solar GHI is both abundant and highly variable.
These regions are most vulnerable to solar GHI variability over climate timescales, and are therefore of
greatest interest for seasonal forecasting in spring.
SPRING Solar PV Forecasts
(March + April + May)

5. Climate Forecasting Unit
Fig. S2.1.1: Spring solar GHI ensemble mean correlation
(ECMWF S4, 1 month forecast lead time, once a year from 1981-2010)
time
forecast
+ 1.0
obs. forecast
- 1.0
forecast
example 1
forecast
- 1.0
example 2
example 3
Stage B: Solar GHI Forecast Skill Assessment
1St
validation of the climate forecast system:
The skill of a climate forecast system, to predict global solar GHI variability in spring 1 month ahead, is partially
shown in this map. Skill is assessed by comparing the mean of a spring solar GHI forecast, made every year
since 1981, to the reanalysis “observations” over the same period. If they follow the same variability over time,
the skill is positive. This is the case even if their magnitudes are different (see example 1 and 2).
Perfect
Forecast
Same as
Climatology
Worse
than
Clima-
tology
SPRING Solar PV Forecasts
(March + April + May)
Can the solar forecast mean tell us about
the solar GHI resource variability
at a specific time?
SolarGHI

6. Climate Forecasting Unit
Fig. S2.1.1: Spring solar GHI ensemble mean correlation
(ECMWF S4, 1 month forecast lead time, once a year from 1981-2010)
Stage B: Solar GHI Forecast Skill Assessment
1St
validation of the climate forecast system:
Dark red regions of the map show where the climate forecast system demonstrates the highest skill in spring
seasonal forecasting, with a forecast issued 1 month in advance. White regions show where there is no
available forecast skill, and blue regions where the climate forecast system performs worse than a random
prediction. A skill of 1 corresponds to a climate forecast that can perfectly represent the past “observations”.
Perfect
Forecast
Same as
Climatology
Worse
than
Clima-
tology
SPRING Solar PV Forecasts
(March + April + May)
Can the solar forecast mean tell us about
the solar GHI resource variability
at a specific time?

7. Climate Forecasting Unit
Fig. S2.1.2: Spring solar GHI CR probability skill score
(ECMWF S4, 1 month forecast lead time, once a year from 1981-2010)
time
forecast
+ 1.0
obs. forecast
- 1.0
forecast
example 1
forecast
- 1.0
example 2
example 3
Stage B: Solar GHI Forecast Skill Assessment
2nd
validation of the climate forecast system:
The skill of a climate forecast system, to predict global solar GHI variability in spring 1 month ahead, is fully
shown in this map. Here, skill is assessed by comparing the full distribution (not just the mean value as in the
previous map) of a spring solar GHI forecast, made every year since 1981, to the “observations” over the
same period. If they follow the same magnitude of variability over time, the skill is positive (example 2).
Perfect
Forecast
Same as
Climatology
Worse
than
Clima-
tology
SPRING Solar PV Forecasts
(March + April + May)
Can the solar forecast distribution tell us
about the magnitude of the solar GHI
resource variability and its uncertainty at
specific time?
SolarGHI

8. Climate Forecasting Unit
Fig. S2.1.2: Spring solar GHI CR probability skill score
(ECMWF S4, 1 month forecast lead time, once a year from 1981-2010)
Stage B: Solar GHI Forecast Skill Assessment
2nd
validation of the climate forecast system:
Dark red regions of the map show where the climate forecast system demonstrates the highest skill in spring
seasonal forecasting, with a forecast issued 1 month in advance. White regions show where there is no
available forecast skill, and blue regions where the climate forecast system performs worse than a random
prediction. A skill of 1 corresponds to a climate forecast that can perfectly represent the past “observations”.
Perfect
Forecast
Same as
Climatology
Worse
than
Clima-
tology
SPRING Solar PV Forecasts
(March + April + May)
Can the solar forecast distribution tell us
about the magnitude of the solar GHI
resource variability and its uncertainty at
specific time?

9. Climate Forecasting Unit
Europe
Areas of
interest:
E.Chile/S.SE
Argentina/
N.E.Brasil
Indonesia/
W.Philippines/
Cambodia/Thailand/
Vietnam/UAE/
Oman/S.Pakistan/
S.Iran/Afghanistan
N.Australia/
Pacific Isles
S.America Africa
Asia
Australia
E.USA
N.America
North Sea/
S.France/
E.Europe
Spring solar GHI magnitude, and its uncertainty
forecast skill
Spring solar GHI variability forecast skill
Solar GHI variability
forecast skill only
Solar GHI magnitude and its uncertainty forecast skill
S.Moz-
ambique
Stage B: Solar GHI Forecast Skill Assessment Where is solar GHI forecast skill highest?
By comparing both the spring global solar GHI forecast skill assessments, it can be seen that there are
several key areas (listed above) where solar GHI forecasts are skilful in assessing its variability, magnitude
and uncertainty. These regions show the greatest potential for the use of operational spring wind forecasts,
and are therefore of greatest interest to seasonal solar GHI forecasting in spring.
SPRING Solar PV Forecasts
(March + April + May)

10. Climate Forecasting Unit
Stage B: Solar GHI Forecast Skill Assessment
Magnitude and uncertainty forecast skillVariability forecast skill
m/sm/sm/s
SPRING Wind Forecasts
These four maps compare the seasonal spring solar GHI global forecast skill maps (bottom) alongside the
spring global solar GHI availability and inter-annual variability map (top). It can be seen that there are several
key areas (highlighted above) where the forecast skill is high in assessing its variability, magnitude and
uncertainty, and the solar GHI is both abundant and highly variable. These regions demonstrate where spring
seasonal solar GHI forecasts have the greatest value and potential for operational use.
EuropeAreas of
Interest:
(Forecast skill)
E.Brazil
Indonesia/
W.Philippines/
Cambodia/Thailand/
Vietnam/UAE/
Oman/S.Pakistan/
S.Iran/Afghanistan
W.
S.America Africa
Asia
Australia
Mexico/
S.Canada
N.America
North Sea/
S.France/
E.Europe
S.Moz-
ambique
Europe S.America Africa Asia AustraliaN.America
S.E.Continent/
N.E.Pakistan/
N.E.Afghanistan/
Tajikistan
France/
N.W.Spain
Areas of
Interest:
(Resources)
N-N.E.Australia
Solar GHI resource inter-annual variabilitySolar GHI resource availability
Stage A: Solar GHI Resource Assessment
Variability forecast skill
Where is solar GHI forecast skill highest?
Where is solar resource potential + volatility highest
SPRING Solar PV Forecasts
(March + April + May)
N.E.Mexico/
W.USA
N.
Continent
S.Sahal/
Zimbabwe/
Zambia/
Mozambique
E.Chile/S.SE
Argentina/
N.E.Brasil
N.Australia/
Pacific Isles
E.USA

11. Climate Forecasting Unit
%
Areas of Interest Identified:
(Resources and Forecast Skill)
S.America
E.BrasilE.Brasil
W.
Australia
N.Australia
S.America
Fig. S3.1.1: Probabilistic forecast of (future) spring 2011, solar GHI most likely tercile
(ECMWF S4, 1 month forecast lead time)
Stage C: Operational Solar GHI Forecast
This operational solar forecast shows the probability of global solar GHI to be higher (red), lower (blue) or
normal (white) over the forthcoming spring season, compared to their mean value over the past 30 years. As
the forecast season is spring 2011, this is an example of solar GHI forecast information that could have been
available for use within a decision making process in February 2011.
SPRING Solar PV Forecasts
(March + April + May)
Europe
S.France
Africa
S.Mozambique
Indonesia/ W.Philippines/
Cambodia/ Thailand/
Vietnam/ N.E.Afghanistan
Asia

12. Climate Forecasting Unit
%
Areas of Interest Identified:
(Resources and Forecast Skill)
Stage C: Operational Solar GHI Forecast
The key areas of highest interest are shown, identified in the stages A and B of the forecast methodology.
These regions demonstrate where spring seasonal solar GHI forecasts have the greatest value and potential
for operational use. The areas that are blanked out either have lower forecast skill in spring (Stage B) and/or
lower solar GHI availability and inter-annual variability (Stage A).
Fig. S3.1.1: Probabilistic forecast of (future) spring 2011, solar GHI most likely tercile
(ECMWF S4, 1 month forecast lead time)
SPRING Solar PV Forecasts
(March + April + May)
S.America
E.BrasilE.Brasil
W.
Australia
N.Australia
S.America Europe
S.France
Africa
S.Mozambique
Indonesia/ W.Philippines/
Cambodia/ Thailand/
Vietnam/ N.E.Afghanistan
Asia

13. Climate Forecasting Unit
%
Areas of Interest Identified:
(Resources and Forecast Skill)
Stage C: Operational Solar GHI Forecast
This does not mean that the blanked out areas are not useful, only that the operational solar GHI forecast for
these regions should be used within a decision making process with due awareness to their corresponding
limitations. The primary limitations to a climate forecast are either the forecast skill and/or the low risk of
variability in solar GHI for a given region. See the “caveats” webpage for further limitations.
Fig. S3.1.1: Probabilistic forecast of (future) spring 2011, solar GHI most likely tercile
(ECMWF S4, 1 month forecast lead time)
SPRING Solar PV Forecasts
(March + April + May)
S.America
E.BrasilE.Brasil
W.
Australia
N.Australia
S.America Europe
S.France
Africa
S.Mozambique
Indonesia/ W.Philippines/
Cambodia/ Thailand/
Vietnam/ N.E.Afghanistan
Asia

14. Climate Forecasting Unit
The research leading to these results has received funding
from the European Union Seventh Framework Programme
(FP7/2007-2013) under the following projects:
CLIM-RUN, www.clim-run.eu (GA n° 265192)
EUPORIAS, www.euporias.eu (GA n° 308291)
SPECS, www.specs-fp7.eu (GA n° 308378)