Herold nap expo_climate_monitoring_10042019

1. Dr. Nicholas Herold
Office of Environment and Heritage, Australia
NAP-expo, South Korea, April 2019
Monitoring the climate system of
the past and present

2. Know your climate trends

3. y = 0.1221x + 968.89
R² = 7E-05
500
700
900
1100
1300
1500
1700
1900
2100
1935 1945 1955 1965 1975 1985 1995 2005 2015
Annualrainfall(mm)
Understanding your climate trend [1]
• Understanding long term climate
trends is necessary for robust
decision making. 30 years minimum.
• Prevents adapting to the noise
instead of the signal.
• Satellite and reanalysis products can
help augment local data.
y = 21.858x - 42800
R² = 0.109
500
700
900
1100
1300
1500
1700
1900
2100
2005 2015
Annualrainfall(mm)
y = -11.593x + 24404
R² = 0.1009
500
700
900
1100
1300
1500
1700
1900
2100
1985 1995 2005 2015
Annualrainfall(mm)

4. Understanding your climate trend [2]
0
5
10
15
20
25
30
35
40
45
1980 1985 1990 1995 2000 2005 2010 2015 2020
Percentofhotdayseachyear
• Helps understand whether current conditions are due to long-term
influences like global warming, or variability (such as El-Nino).

5. Means vs extremes and indices to measure them
Heatwave melting
pavement in India
Drought in South Africa
“rain bomb” in
the USA
Rain-damaged
crops in India

6. What is a climate index?
Time
Climate
variable

7. Mean
What is a climate index?
Time
Climate
variable

8. > 90th percentile
threshold
Time
Climate
variable
What is a climate index?

9. Example: Number of days ≥ 30°C
40
50
60
70
80
90
100
110
120
1950 1960 1970 1980 1990 2000 2010 2020
Numberofdays
Temperatures above 30°C
damages wheat

10. Examples of sector-specific indices
● Agricultural indices: Standardised Precipitation
Index (SPI), Growing Season Length (GSL), multiple
temperature indices.
● Health indices: Excess Heat Factor (EHF).
● Extreme rainfall indices: Maximum 1 day rainfall,
maximum 5 day rainfall.
● Energy sector: cooling degree days and heating
degree days.

11. Sector-specific indices for your region
● The Expert Team on Sector-specific Climate Indices (ET-SCI)
ClimPACT2 users by country
(ET-SCI workshops in yellow boxes) South America 2013
Caribbean 2016
South Pacific
2015
South
Asia 2016
An international team of
climate scientists dedicated
to improving the availability
and consistency of sector-
specific climate indices
through the creation of
software, regional
workshops, research, and
training materials.

12. ClimPACT2
Data for a single
location
Time-series output
Spatial data
for a region
Gridded output
Reads in daily temperature
and rainfall
60+ monthly and
annual indices
https://github.com/ARCCSS-extremes/climpact2

13. ClimDEX.org: indices from station data
● Uses station data sourced globally to calculate indices.
● Can access indices for individual stations OR global grids of
interpolated station indices (~300 km resolution).
● Access the data or plots.
Global gridded dataset of
indices using station data www.climdex.org

14. Indices from a global land model
● By Malcolm Mistry, Ca’ Foscari University of Venice, Italy.
● Uses the Global Land Data Assimilation System (GLDAS).
● Global 25 km dataset of all ET-SCI indices for 1970 – 2016.
https://doi.pangaea.de/10.1594/PANGAEA.898014
Annual warmest daytime temperature in 2003

15. Original records of the highest
temperature ever recorded by a
meteorological station. Courtesy
of Khalid Ibrahim El Fadli, Libyan
National Meteorological Center.
Max, Min and 9 am temperatures
http://www.met-acre.net/chapters.htm
Data sharing and data rescue
• Many countries have data that are not
shared in global databases or with other
countries.
• Many countries have data that is not
digitized (sometimes sitting on shelves
and degrading). Met-acre can help with
your data rescue!

16. Examples of indices in adaptation
1. The Caribbean
2. South Asia
3. Italy

17. The Caribbean
● Standardised Precipitation Index (SPI)
used for drought forecasting.
● Annual maximum daytime
temperature vs rice production.
Courtesy Dr. Cedric Van Meerbeeck
Annual maximum daytime temperature (°C)
Riceproduction(tons)

18. 0
5
10
15
20
25
30
35
0
500
1000
1500
2000
2500
3000
3500
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
YIELD_D1 CDD
CDD
YIELD
India: peanut yield vs consecutive dry days
C.C. : -0.5

19. Italy
● Projections of indices using
climate models in the national
adaptation plan.
● Risk index combines climate
extreme indices with indicators
of exposure and vulnerability. Change in days >= 20mm (left) and frost days (right)
2021 – 2050 vs 1981 – 2010. National adaptation plan.

20. Take-away messages
1. “Long” climate records ensure you adapt to climate change and
not climate variability.
2. Adapt appropriately by knowing what aspects of climate your
sectors are sensitive to. Sector-specific climate indices help you
do this.
3. Demonstrating a link between climate and sectors is essential for
adaptation planning and for your funding proposals!

21. Thank you
Dr. Nicholas Herold
nicholas.herold@environment.nsw.gov.au
Useful resources:
• Expert Team on Sector-specific Climate
Indices: WMO group advocating sector-
relevant climate indices.
• ClimPACT2: Software to calculate indices.
• WMO Climate services toolkit: Database of
online tools, resources and training.
• Climdex: climate indices available for
individual stations or as global grids.
• CDAAS RIMES: Portal to access climate
model projections for your region.
• Regional Climate Outlook Forums: produce
regional climate predictions to reduce
climate-related risks. See figure below.