1) Climate modeling can provide useful risk-based climate information for decision-making and adaptation planning, such as assessing impacts on water resources, agriculture, and infrastructure development. 2) Integrating global, regional, and local climate and weather models in a "seamless prediction" approach allows addressing climate issues across timescales from seasons to decades. 3) Seasonal forecasts of rainfall and river flows are helping to inform reservoir management and electricity production decisions in locations like Ghana's Lake Volta region.

1. Cli d lli idClimate modelling: A guide to
investment decisions
Vicky Pope
Met Office Hadley Centre

2. The impact of a global temperature rise
of 4 ºCof 4 C
Change in temperature from pre‐industrial climate
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Melting
ice
Methane
release
M
Ocean
Reduced
crops
Forest
Increased
drought
More
heatwaves
Ocean
Acidification
Rainforest
loss
Forest
fire Stronger
tropical storms
Current City population • 3‐10 million • 10‐20 million© Crown copyright Met Office

3. The benefits of integrated services:
for policy and long term planning

4. Bridging the Gaps and Bridging the Scales
GlobalGlobal
Weather/Climate
Model: 25 -100km
Regional
Weather/Climate Regional Impacts
M d l
Regional Impacts
Models: Hydrology,
model: 25 - 12km Model:Vegetation,
Topography
Local
Local Decision-
Making: Land
use, Water use,
Local
downscaling
model:
4 1km use, a e use,
Adaptive
Responses
4 - 1km
© Crown copyright Met Office

5. Future Nile flow:
A Climate Change Risk Management Programmeg g g
• Impact on water resources
by 2050s
• Possible adaptation
• Regional climate models
d Nil f tiand Nile forecasting
system
• Risk based approachRisk based approach
• Rainfall and river runoff –
wide range of possible
outcomes
Image NASA © Crown copyright Met Office

6. Changes in rain and snowfall in the high
mountains of South Asiamountains of South Asia
Himachal Pradesh East Nepal &
W E
Summer precipitation (Indian monsoon)Winter precipitation (Western disturbances)
NW
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
Karakoram Hindu Kush Jammu‐Kashmir
Himachal Pradesh,
Uttarakhand & West Nepal
East Nepal &
Bhutan
ion mm/day
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
nge in precipitat
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
Chanday
Karakoram
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
n snowfall mm/d
JK
000 2050 2100
HP
1950 2000 2050 2100
-2
0
2
4
6
NB
1950 2000 2050 2100
-2
0
2
4
6
JK
000 2050 2100
HP
1950 2000 2050 2100
-10
0
10
20
30
40
NB
1950 2000 2050 2100
-10
0
10
20
30
40
JK
000 2050 2100
HP
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
NB
1950 2000 2050 2100
-0.5
0.0
0.5
1.0
1.5
JK
000 2050 2100
HP
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
NB
1950 2000 2050 2100
-1.5
-1.0
-0.5
0.0
0.5
1.0
Change in
Time ‐ Met Office model ‐ MPI model

7. Probability forecasts of coastal flood risk:
Balanced approach to decision making
Central estimate Upper limit of
pp g
Central estimate
from UKCP models
Upper limit of
"high++“ model
Change in
extreme sea
level of 50 year y
storm
© Crown copyright Met Office

8. Energy project
HeadlineHeadline
results
© Crown copyright Met Office
© Crown copyright Met Office

9. The benefits of integrated services:
for planning and civil contingencies

10. Seamless prediction
Supporting decision making
Now
ours
Days
week
onth
onal
cadal
mate
mate
nce
ry
N
H
D
1‐w
1‐mo
Seas
Dec
Clim
Past clim
Confiden
bounda
Analysis of past weather
observations to manageobservations to manage
climate risks
Eg. Agriculture: this informs
crop choice and planting date
to optimise yields and di i i d
Monthly to decadal
predictions informs
probability of drought, cold,
heat.to optimise yields and
minimise crop failure risk.
Predicting routine and
hazardous weather conditions
and disseminating tailored
and timely warnings.
P bli
heat.
Contingency planners,
national and international
humanitarian response,
government and private
Global and regional climate
predictions.
Informs mitigation policy and
adaptation choices Impacts
© Crown copyright Met Office
Forecast lead‐time
Public, emergency response,
international disaster risk
reduction
g p
infrastructure investment
adaptation choices. Impacts
on water resurces, heat
stress, crops, infrastructure.

11. Example of seasonal prediction of
reservoir management
Lake Volta dam Akosombo
g
Ghana
Lake Volta dam, Akosombo
• 1000MWatt facility: provides ~50% of
Ghana’s electricity
Lake
Volta
• Rainfall has strong seasonal dependency:
peak months June‐September
• Inflow prediction needed to assess likely
requirement for oil‐fired generation
• …thus lake‐level is monitored closely!…thus lake level is monitored closely!
© Crown copyright Met Office

12. Summary
• Useful risk based climate information forUseful risk based climate information for
adaptation ‐ e.g. River Nile, Hamalayas
• Long range forecasts for decision tools MoreLong range forecasts for decision tools. More
skilful in some regions and aggregated over
regions such as large water catchments – Voltaregions such as large water catchments Volta
• ‘Seamless prediction’ addresses climate
variability and changevariability and change
• Integrated approach provides environmental
information for decision makinginformation for decision making
© Crown copyright Met Office