Overview of climate variability and climate change in GMS
Overview of climate variability
and climate change
Eitzinger Anton, Giang Linh, Lefroy Rod
Laderach Peter, Carmona Stephania
Overview of climate variability and likely climate change impacts on
agriculture across the Greater Mekong Sub-region (GMS)
10 – 11 March, 2014, Hanoi, Vietnam
Climate science … many questions
and uncertain answers!
1. What is the evidence and observed changes in
the climate system and how reliable are climate
models and scenarios?
2. How to use climate models & future predictions
for Agriculture and modeling?
3. How can we adapt agriculture systems to
unknown future conditions?
The atmosphere and ocean
have warmed, the amounts
of snow and ice have
diminished, sea level has
risen, and the
greenhouse gases have
IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A.
Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Each of the last three decades has been successively warmer at the Earth’s surface
than any preceding decade since 1850. In the Northern Hemisphere, 1983–2012 was
likely the warmest 30-year period of the last 1400 years (medium confidence).
Ocean warming dominates the increase in energy stored in the climate
system, accounting for more than 90% of the energy accumulated between 1971 and
2010 (high confidence). It is virtually certain that the upper ocean (0−700 m)
warmed from 1971 to 2010.
IPCC AR5 report – observed changes in the climate system
Over the last two decades, the Greenland and Antarctic ice sheets have been losing mass, glaciers
have continued to shrink almost worldwide, and Arctic sea ice and Northern Hemisphere spring snow
cover have continued to decrease in extent (high confidence)
The rate of sea level rise since the mid-19th century has been larger than the mean rate during the
previous two millennia (high confidence). Over the period 1901–2010, global mean sea level rose by
0.19 [0.17 to 0.21] m.
The atmospheric concentrations of carbon dioxide (CO2), methane, and nitrous oxide have increased
to levels unprecedented in at least the last 800,000 years. CO2 concentrations have increased by 40%
since pre-industrial times, primarily from fossil fuel emissions and secondarily from net land use
change emissions. The ocean has absorbed about 30% of the emitted anthropogenic carbon dioxide,
causing ocean acidification.
Drivers of Climate Change Total radiative forcing is positive,
and has led to an uptake of energy
by the climate system. The largest
contribution to total radiative
forcing is caused by the increase in
the atmospheric concentration of
CO2 since 1750.
Climate models have improved since the AR4.
Models reproduce observed continental-scale
surface temperature patterns and trends over many
decades, including the more rapid warming since
the mid-20th century and the cooling immediately
following large volcanic eruptions.
(very high confidence).
This evidence for human influence has grown since
AR4. It is extremely likely that human influence has
been the dominant cause of the observed warming
since the mid-20th century.
Changes in the global water cycle in response to the
warming over the 21st century will not be uniform.
The contrast in precipitation between wet and dry
regions and between wet and dry seasons will
increase, although there may be regional exceptions.
IPCC Global-scale assessment of recent observed changes, human
contribution to the changes, and projected further changes
Observed ocean and surface temperature anomaly
• Annual average
• Decadal average
• Contribution to change
Representative Concentration Pathways (RCPs)
… former Emission Scenarios (SRES)
concentrations of the full suite of greenhouse gases and aerosols
and chemically active gases, as well as land use/land cover
AR 5 projected regional changes:
“Reduced precipitation in Indonesia during Jul-
Oct. due to the pattern of Indian Ocean
warming; increased rainfall extremes of landfall
cyclones on the coasts of the South China
Sea, Gulf of Thailand, and Andaman Sea.”
How to use climate models & future
predictions for Agriculture and modeling?
To know the uncertainty of
the data is important!
We don’t know… What are the
conditions in 30, 50, 100 years?
The different emission scenarios are
not important ... by 2030 the
difference between the
concentration pathways is minimal.
Understand variability and precise
forecasting is important!
• There is still uncertainty on climate models
when it comes to variability
• Historical observations of weather and climate
can help to understand better variability
• We need a better forecasting for Agriculture
Historical climate in GMS
Presentation: Linh Giang
• Recent studies show the emergence of general
trends in the climate of the GMS.
• Average daily temperatures across Southeast Asia
• Precipitation patterns are quite complex across
• In the Greater Mekong region from 1961 to 1998,
although the number of extreme rainfall events
decreased, the amount of rain falling during these
events increased (Manton et al 2001).
CRU TS 3.10.01
The CRU TS 3.10.01 Climate dataset has been produced by the
Climatic Research Unit (CRU) of University of East Anglia.
The database comprises 5583 station records of which 4842 have
enough data for the 1961-1990 period to calculate estimate the
average temperatures for this period.
Climate grids are constructed for nine climate variables
for the period 1901-2009
- Diurnal temperature range,
- Daily minimum temperature,
- Maximum temperatures,
- Wet-day frequency,
- Frost-day frequency,
- Vapor pressure, and
- Cloud cover.
CRU TS 3.10.01
842 points in GMS were
collected from CRU TS 3.10.01 which
covers from 1901 to 2009, globally at
0.5 degree spatial resolution on land
• Mean temperature
• Minimum temperature
• Maximum temperature
• Mean temperature
increased by between 1.8 ˚C
and 2 ˚C.
• Maximum temperature rose
by between 1.7˚C and 2.2˚C.
• Minimum temperature grew
by between 1.6˚C and 2.2˚C.
The region has seen more hot
days and warm nights and
fewer cool days and nights.
• Total annual rainfall
will increase by 5-25%
across the northern
part of the Mekong
region in the next few
• Heavier storms during
the wet season will
account for the
because drier dry
seasons are predicted
(TKK & SEA START RC
The trends in rainfall had
the range of highly
• In spite of a few station in South-East Asia , CRU
data is useful to get overview of the climate
change in the long time,
• The highest temperature in research area
concentrate in the south, and recorded the
significant increase in South of Cambodia and
South-East of Thailand,
• The shoreline area receive the large amount of
precipitation (Especially in Middle of Vietnam,
Myanmar, and Thailand)
Statistical downscaling of climate models
• Use anomalies and discard baselines in GCMs
– Climate baseline: WorldClim
– Used in the majority of studies
– Takes original GCM timeseries
– Calculates averages over a baseline and future periods (i.e.
– Compute anomalies
– Spline interpolation of anomalies
– Sum anomalies to WorldClim
• Bio1 = Annual mean temperature
• Bio2 = Mean diurnal range (Mean of monthly (max temp - min temp))
• Bio3 = Isothermality (Bio2/Bio7) (* 100)
• Bio4 = Temperature seasonality (standard deviation *100)
• Bio5 = Maximum temperature of warmest month
• Bio6 = Minimum temperature of coldest month
• Bio7 = Temperature Annual Range (Bio5 – Bi06)
• Bio8 = Mean Temperature of Wettest Quarter
• Bio9 = Mean Temperature of Driest Quarter
• Bio10 = Mean Temperature of Warmest Quarter
• Bio11 = Mean Temperature of Coldest Quarter
• Bio12 = Annual Precipitation
• Bio13 = Precipitation of Wettest Month
• Bio14 = Precipitation of Driest Month
• Bio15 = Precipitation Seasonality (Coefficient of Variation)
• Bio16 = Precipitation of Wettest Quarter
• Bio17 = Precipitation of Driest Quarter
• Bio18 = Precipitation of Warmest Quarter
• Bio19 = Precipitation of Coldest Quarter
Changes from 24 climate models using climate clusters for GMS
* X current annual mean temperature, X current annual rainfall, source http://worldclim.org
Impacts on coffee production in Nicaragua Coffee under pressure
• Increasing Temperatures
during dry season
• Excess precipitation in wet
season, long dry season
• Increasing precipitation
Nicaragua, above 1000m
• Increasing Temperatures during dry season
• Current annual precipitation optimal
• Stronger droughts negative
• At high elevations increasing max temperatures
Specific vulnerability profiles of farmers in Nicaragua Coffee under pressure
Matagalpa is characterized by high
exposure (coffee suitability
decreases drastically) high
sensitivity (high variability in
yields) and low adaptive capacity
(poor access to credit, poor
knowledge on pest and disease
management and low
The adaptation strategy focuses
on diversification, capacity
building, strengthening of the
organizations and on the
enforcement of environmental
laws and development policies for
the coffee sector.
Breedings requirements to make coffee
production in Nicaragua climate resilient
Simulación del "Yield" presente
siembra: Primera (en mayo), 2 suelos
genéricos, 2 fertilizantes, promedio de
30 años de clima (worldclim), sin riego.
Rendimientos esperados en grandes
partes entre 500 - 700 kg/ha
Simulación del futuro 2020
Con predicciones del clima (GCM) a la
década 2020 (promedio entre 2010-
2039). Reducción del rendimiento en
las zonas del corredor seco de
Nicaragua y Honduras.
Cambio hasta -75% de rendimiento
esperado (sin estrategias) en 2020
Zonas de alta producción
Identificación de Zonas focales para
estrategias de adaptación con
Spots), diversificación (Hot Spots) y
conservación (Pressure Spots)
Posibles estrategias de adaptación: Germoplasma con más
resistencia al calor y sequía, riego con sistemas de cosecha de
agua durante la época de alta lluvia, diversificación a otros
sistemas de producción, recuperación de suelos degradados, ...
Bean systems in Central America
Project: Tortillas on the Roaster:
Less areas available for
Diversification options in mid-
Climate change impact assessment for Haiti
New areas that need strategic
Transformative adaptation of current production
systems to climate resilient systems in the future
When, where, how and with whom?
How can we adapt agriculture systems to unknown future conditions?
IPCC GCM scenario outputs (AR5)
Accelerated adoption of
climate resilient practices
Current situation Production &
social constraints to dealing with
climate change & variability
Land health indicators
(CIAT Agro biodiversity)
The local perspective