Overview of climate variability and climate change

Eitzinger Anton, Giang Linh, Lefroy RodLaderach Peter, Carmona Stephania

Overview of climate variability and likely climate change impacts on agriculture across the Greater Mekong Sub-region (GMS)

1 April, 2014, Vientiane, Laos

• Cross-cutting, multi-disciplinary team who believe that better decisions can be made with the power of information

• Supporting functions within CIAT, and global research leadership in specific themes

Decision and Policy Analysis

• Focussed on delivering research outcomes in:o Climate change (CRP7)o Ecosystem Services (CRP5)o Linking Farmers to Markets (CRP2)

• Through expert, disciplinary groups in:o Modellingo Gender analysiso Impact and Strategic Studieso Policy Analysiso Knowledge Managemento Big Data

Decision and Policy Analysis

Our focus• Providing information and climate data for Agriculture• Climate Change impact assessment

– For Food security & cash crops, entire value chains• Vulnerability of communities

– Perception of risks, adaptive capacity, gender differences• Social & economic constraints for adaptation• Adaptation & mitigation strategies• Cost & benefit of strategies• Supply chain inclusive adaptation framework• Work for/with national policy institutions!• Mitigation through carbon insetting• Triple-win of adaptation, mitigation and food security … whilst

conserving biodiversity• Bring to implementation of CSA (climate smart agriculture) practices

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 concentrations of greenhouse gases have increased.

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.

IPCC, 2013

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, 2013

IPCC Global-scale assessment of recent observed changes, human contribution to the changes, and projected further changes

IPCC, 2013

Observed ocean and surface temperature anomaly

• Annual average• Decadal average• Contribution to change

IPCC, 2013

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

RCP 8.5(high emissions)

RCP 6.0

RCP 4.5

RCP 2.6(low emissions)

IPCC, 2013

Climate Models & Future predictions

IPCC, 2013

AR 5 projected regional changes:Southeast Asia

“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.”

IPCC, 2013

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!

2030

For agriculture:

IPCC, 2013

Climate variability

• 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

http://ccafs.cgiar.org

http://ccafs-climate.orgCCAFS Climate

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.

2020s, 2050s)– Compute anomalies– Spline interpolation of anomalies– Sum anomalies to WorldClim

http://www.worldclim.org

Worldclim stations worldwide

47,554 precipitation

24,542 tmean

14,835 tmax y tmin

Sources:•GHCN•FAOCLIM•WMO•CIAT•R-Hydronet•Redes nacionales

for GMS

• 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

x xx

x

x

xx

xx

x

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 have increased

• Precipitation patterns are quite complex across Southeast Asia.

• 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).

OVERVIEW

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- Temperature, - Diurnal temperature range, - Daily minimum temperature, - Maximum temperatures, - Precipitation,- 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 area, including:

• Precipitation• 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 decades.

• Heavier storms during the wet season will account for the regional increase because drier dry seasons are predicted (TKK & SEA START RC 2009).

The trends in rainfall had the range of highly variable

Conclusion

• 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)

Ecocrop-modeling

Why crop modeling in climate change?… assessing the impact of climate change on productivity and climate-suitability of crops and production systems … and understand the limiting factors

… using well-established empirical and mechanistic models such as Ecocrop, Maxent, DSSAT, …..that allow for the incorporation of spatial data and fine-tuned biophysical data

How?

outline

• What is Ecocrop?• FAO Ecocrop plant database• Suitability modeling with Ecocrop• Modeling Ecocrop with DIVA GIS• Calibrating ecological ranges (using literature)• Projecting suitability into the future

• The database was developed 1992 by the Land and Water Development Division of FAO (AGLL) as a tool to identify plant species for given environments and uses, and as an information system contributing to a Land Use Planning concept.

• In October 2000 Ecocrop went on-line under its own URL www.ecocrop.fao.org. The database now held information on more than 2000 species.

• In 2001 Hijmans developed the basic mechanistic model (also named EcoCrop) to calculate crop suitability index using FAO Ecocrop database in DIVA GIS.

• In 2011, CIAT (Ramirez-Villegas et al.) further developed the model, providing calibration and evaluation procedures.

• http://ecocrop.fao.org

• Common bean

• database held information on more than 2000 species

Suitability modeling with EcocropEcoCrop, originally by Hijman et al. (2001), was further developed, providing calibration and evaluation procedures (Ramirez-Villegas et al. 2011).

It evaluates on monthly basis if there are adequate climatic conditions within a growing season for temperature and precipitation…

…and calculates the climatic suitability of the resulting interaction between rainfall and temperature…

How does it work?

What happens when Ecocrop model runs?1

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8

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12 potentialgrowing seasons

1 kilometer grid cells(climate environments)

The suitability of a location (grid cell) for a crop is evaluated for each of the 12 potential growing seasons.

Growing season

0 24 100 80

𝑇𝑘𝑖𝑙𝑙=4+Tkill ( initial )

𝑇𝑘𝑖𝑙𝑙𝑇𝑚𝑖𝑛

𝑇𝑜𝑝𝑚𝑖𝑛

𝑇𝑚𝑎𝑥

𝑇𝑜𝑝𝑚𝑎𝑥

𝑇 (𝑋 )−𝑇𝑚𝑖𝑛

𝑇𝑜𝑝𝑚𝑖𝑛−𝑇𝑚𝑖𝑛

=𝑇 𝑠𝑢𝑖𝑡 1−𝑇 (𝑋 )−𝑇 𝑜𝑝𝑚𝑎𝑥

𝑇𝑚𝑎𝑥−𝑇 𝑜𝑝𝑚𝑎𝑥

=𝑇 𝑠𝑢𝑖𝑡

𝑇 𝑠𝑢𝑖𝑡=0

𝑇 𝑠𝑢𝑖𝑡=100

For temperature suitabilityKtmp: absolute temperature that will kill the plant Tmin: minimum average temperature at which the plant will grow Topmin: minimum average temperature at which the plant will grow optimally Topmax: maximum average temperature at which the plant will grow optimally Tmax: maximum average temperature at which the plant will cease to growFor rainfall suitabilityRmin: minimum rainfall (mm) during the growing season Ropmin: optimal minimum rainfall (mm) during the growing season Ropmax: optimal maximum rainfall (mm) during the growing season Rmax: maximum rainfall (mm) during the growing season Length of the growing seasonGmin: minimun days of growing seasonGmax: maximum days of growing season

P

P

P

P

• Growing season: xx days (average of Gmin/Gmax)

• Temperature suitability (between 0 – 100%)

• Rainfall suitability (between 0 – 100%)

• Total suitability = TempSUIT * RainSUIT

If the average minimum temperature in one of these months is 4C or less above Ktmp, it is assumed that, on average, KTMP will be reached on one day of the month, and the crop will die. The temperature suitability of that month is thus 0%. If this is not the case, the temperature suitability is evaluated for that month using the other temperature parameters. The overall temperature suitability of a grid cell for a crop, for any growing season, is the lowest suitability score for any of the consecutive number of months needed to complete the growing season

The evaluation for rainfall is similar as for temperature, except that there is no “killing” rainfall and there is one evaluation for the total growing period (the number of months defined by Gmin and Gmax) and not for each month. The output is the highest suitability score (percentage) for a growing season starting in any month of the year.

Results from GMS study

Not available = natural (forest, wetland, …), protected, water, bare, urban areasNeeds change = land mixed with pastoralism (forest, herbaceous, wetlands, …)Available = Agriculture (commercial, subsidized, irrigated, …)

Land use change at risk

for agriculture

www.ciat.cgiar.orgScience to cultivate change

Use and Interpretation of EcoCrop• Purely Climatic Suitability:

• Does not include soils• Does not include pests and diseases

• Rainfall does not equal available water:• Irrigation• Soil water management (SOM, mulch, etc.)• Topography and soil type affect drainage

• Phenology: Different requirements at different stages of growth (especially for perennials)

• What is “most suitable” not necessarily the best to grow – markets, labour, farming system, etc.

Other approaches for crop modeling

• Maximum entropy methods are very general ways to predict probability distributions given constraints on their moments

• Predict species’ distributions based on environmental covariates

What is Entropy Maximization?

• You can think of Maxent as having two parts: a constraint• component and an entropy component

• The output is a probability distribution that sums to 1• For species distributions this gives the relative probability of observing

the species in each cell• Cells with environmental variables close to the means of the presence

locations have high probabilities

MaxEnt model

B

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Input: Crop evidence (GPS points)19 bioclimatic variables of current (worldclim) & future climateOutput:Probability of distribution of coffee (0 to 1)

MaxEnt model

Bioclimatic variables for suitability modeling

• 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

derived from monthly temperature & precipitation

Coffee suitability - Maxent Results Nicaragua

B

Results

Variable AdjustedR2

R2 due to variable

% of totalvariability

Present mean

Change by 2050s

Locations with decreasing suitability (n=89.8 % of all observations)BIO 14 – Precipitación del mes más seco 0.0817 0.0817 24.8 24.49 mm -3.27 mm

BIO 04 – Estacionalidad de temperatura 0.1776 0.0959 29.1 0.83 0.166BIO 12 – Precipitación anual 0.2057 0.0281 8.5 2462.35 mm -24.31 mmBIO 11 - Temperatura media del cuarto más frío 0.2633 0.0576 17.5 20.11 ºC 1.86 ºC

BIO 19 - Precipitación del cuarto más frío 0.2993 0.0155 4.7 169.13 mm -7.08 mm

BIO 05 - Temperatura máxima del mes más cálido 0.3198 0.0102 3.1 28.45 ºC 2.30 ºC

BIO 13 - Precipitación del mes más húmedo 0.2838 0.0205 6.2 450.27 mm 10.72 mm

Otros - - 6.2

Coffee suitability - Maxent Results Nicaragua

Decision Support System for Agro technology Transfer (DSSAT)

+

Decision support system modelling (for benchmark sites)

Agronomic managementExpert & farmer survey

Integrated crop-soil modeling

160 LDSF sample sites

Baseline domains

Impact2030 A1b

Experimental [n] cultivars[n] fertilizer application

[n] seasons

Application domains

Analysis of biophysical systems and simulating crop yield in relation to management factors. Combine these models with field observations that allow adjustment of the models in the course of the growing season .

Future24 GCM

A1B (IPCC)

CurrentworldClim

Validation with available station data

Daily weather generatorMarkSIM

Weather station data

(daily)

Climate data

yield

soil management

Statistical negative and positive outliers of predicted yield change by 2020

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Areas where the production systems of crops can be adaptedAdaptation-Spots Focus on adaptation of production system

Areas where crop is no longer an optionHot-SpotsFocus on livelihood diversification

New areas where crop production can be establishedPressure-SpotsMigration of agriculture – Risk of deforestation!

Identifying Impact-Hot-Spots and select sites for socio-economic analysis

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• Beans as most important income (sell 70% of harvest)• Climate variability (intense rain, drought), missing labor & credits,

high input costs, … forces them to changes• Increasing livestock displace crops into hillside areas• Half of farmer rent their land• Distance to market is far• Mostly no road access in rainy season• They buy inputs/sell produce from/to farm-stores

(they call them: Coyotes)

Result: Sample-site 1 - Texistepeque (Las Mesas), Santa Ana ,El Salvador

Message 2: Adaptation Strategies must be fine-tuned at each site!

Las MesasAltitude: 667 m (about 2188 feet) Hot-spot -141 kg/ha

For 2020:• 35 mm less rain (current 1605mm)• mean temperature increase 1.1°CFor 2050:• 73mm less rain ( -5%)• mean temperature increase 2.3°C• hottest day up to 35°C (+ 2.6°C)• coolest night up to 17.7°C (+

1.8°C)

Hot-spot

60Message 3: There can be winners if they adapt quickly!

Result: Sample-site 2 – Valle de Jamastran, Danlí, Honduras Adaptation-spot

JamastranAltitude: 783 m (about 2568 feet) Adaptation-spot -115 kg/ha

• Active communities with already advanced agronomic management of maize-bean crops

• Favorable soil conditions and management• Long-term technical assistance / training• Irrigation schemes (e.g. 50 mz of 17 bean producers)• Diversification options (vegetables, livestock)• Market channels through processing industries• Advanced infrastructure (electricity, roads)• Need to optimize water use efficiency • Credit problems

For 2020:• 41 mm less rain (current 1094 mm)• mean temperature increase 1.1°CFor 2050:• 80 mm less rain ( -7%)• mean temperature increase 2.4°C• hottest day up to 34.2°C (+ 2.6°C)• coolest night up to 17°C (+ 2.1°C)

Conclusions crop models• Ecocrop, when there is a lack on crop

information, for global or regional assessment

• Maxent, perennial crops with presence only data (coordinates) available

• DSSAT, only for few crops (beans, maize, …), high data input demand and calibrated field experiments are necessary

• We need to communicate uncertainty of model predictions

Empiricalmodels

Mechanisticmodels

Land use change analysis

Questions on land use change

• Where does Forest remain?• Forest loss?• Forest gain?• Forest was converted to agriculture?• Forest was converted to plantations?• Forest was converted to other?• Intensification of Agriculture on Non-Forest?• Agriculture to other use?

• Data resolution 30m• Forest change calculated by Hansen

– Tree-cover 2000– Loss– Gain– Loss year

Forest change in square kilometer in the Greater Mekong Sub-region

• From 2000 to 2012• Includes tree-cover > 50%

Forest loss/gain in square kilometer in the Greater Mekong Sub-region

• From 2000 to 2012– Treecover– Loss– Gain– Loss per year

• GFC tree-cover > 50% year 2000

• GFC tree-cover > 50% year 2000, loss & gain year 2012

• Data resolution 250m• 16 day timesteps• Vegetation index NDVI (0-100)• derived results:

– NDVI total change between 2000 to 2012 – NDVI inter-annual change (sd)

• A time-series of NDVI observations can be used to examine the dynamics of the growing season or monitor phenomena such as droughts.

• The Normalized Difference Vegetation Index (NDVI) data set is available on a 16 day. The product is derived from bands 1 and 2 of the MODerate-resolution Imaging Spectroradiometer on board NASA's Terra satellite.

A time-series analysis of Land Use

Methodology…

Downloa

d data

• More than 300 images of NDVI 250m MODIS sensor were downloaded from the period 2000-2013

Image

Filtering

• NDVI scenes was first filtered to eliminate high and low values (poor quality data) using Quality Assessment Science Data Sets (QASDS)

Noise

Removal

• Applying the approach of Fourier interpolation algorithm, to separate the noise spectrum from the signal spectrum of the data set frequency domain

2004 – 2012

• MODIS NDVIyear 2000

• MODIS NDVIyear 2012

• MODIS NDVI year 2000 > 0.5 > 0.75

• NDVI by 2012 -10% +10%

• loss • gain

• GFC-Landsat (a) vs. MODIS (b)

(a)

(b)

Inter annual change of NDVI from standard deviation (sd)

Using Modis NDVI layer & GFCModis NDVI change 2000 to 2012 = AModis NDVI inter-annual change(std) 2000 to 2012 = FForest 2000 and Non-Forest 2000 from GFC

• Forest remains– Forest 2000 [A = 0, F = 0]

• Forest converted to Agriculture?– Forest 2000 [A -1, F +1]

• Forest converted but not agriculture?– Forest 2000 [A -1, F = 0]

• Intensification of Agriculture on Non-Forest?– Non-Forest 2000 [F+1]

• Agriculture to other use?– Non-Forest 2000 [F-1]

Overview of climate variability and climate change

Eitzinger Anton, Giang Linh, Lefroy RodLaderach Peter, Carmona Stephania

Overview of climate variability and likely climate change impacts on agriculture across the Greater Mekong Sub-region (GMS)

1 April, 2014, Vientiane, Laos

Thank you!