Spatial downscaling of future climate predictions for Agriculture

Julián RamírezAndy Jarvis

Carlos Navarro

Contents• Background: climate and

agriculture• Future climate and GCMs• Downscaling methods• Disaggregation• CCAFS-T1 / CIAT-DAPA

data inventory• CCAFS climate data

strategy

Climate and agriculture

• Information on climate is critical for agriculture, because:

• 1. Agriculture is a niche-based activity• 2. Abiotic factors (i.e. climate, soils) and their

interactions are main drivers– Location– Performance– Adaptive responses– Management practices

• 3. Weather and climate predictability is fairly limited• 4. Each system is an specific case, so is its future…

Climate and agriculture

• Agriculture demands:– Multiple variables– Very high spatial resolution– Mid-high temporal (i.e. monthly, daily)

resolution– Accurate weather forecasts and climate

projections– High certainty

• Both for present and future

Climate and agriculture• Due to that, modelling approaches are constrained by input data

© CCAFS

Early20th century

Optimal (mid)20th century

Despite some improvements in data availability

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© Global Historical Climatology Network (GHCN)http://www.ncdc.noaa.gov/ghcnm/v2.php

And methods

Since 1998 every ~28km

TRMM: 3-hour rainfall monitoring

midnight 3am 6am

midday 9am 3pm

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GCMs: How do we predict the future?

• GCMs are the only means we have to predict future climates…

– ~24 exist up to now– All different… so we canexpect issues

IPCC 4th AR GCMsShort name Model Atmosphere* Ocean* MIRCH MIROC3.2. (hires), Japan T106, L56 0.28°x0.19°, L47 MIRCM MIROC3.2. (medres), Japan T42, L20 1.4°x(0.5-1.4°), L43

BCCRC BCCR-BCM2.0, Norway T63, L31 1.5°x0.5°, L35 C3T47 CGCM3.1 (T47), Canada T47, L31 1.85°x1.85°, L29

C3T63 CGCM3.1 (T63), Canada T63, L31 1.4°x0.94°, L29

CNRMC CNRM-CM3, France T63, L45 1.875°x(0.5-2°), L31 CSIRO CSIRO-Mk3.0, Australia T63, L18 1.875°x0.84°, L31

GFD20 GFDL-CM2.0, USA 2.5°x2.0°, L24 1.0°x(1/3-1°), L50 GFD21 GFDL-CM2.1, USA 2.5°x2.0°, L24 1.0°x(1/3-1°), L50

GISSA GISS-AOM, USA 4°x3°, L12 4°x3°, L16 GISSH GISS-EH, USA 5°x4°, L20 5°x4°, L13

GISSR GISS-ER, USA 5°x4°, L20 5°x4°, L13

IAPFG IAP-FGOALS1.0-G, China 2.8°x2.8°, L26 1°x1°, L16 INMCM INM-CM3.0, Russia 5°x4°, L21 2.5°x2°, L33

IPSLC IPSL-CM4, France 2.5°x3.75°, L19 2°x(1-2°), L30 MPICM ECHAM5/MPI-OM, Germany T63, L32 1°x1°, L41

MRICM MRI CGCM2.3.2A, Japan T42, L30 2.5°x(0.5-2.0°)

NCARC NCAR-CCSM3, USA T85, L26 1°x(0.27-1°), L40 NCARP NCAR-PCM, USA T42, L18 1°x(0.27-1°), L40

UKMOC UKMO-HadCM3, UK 3.75°x2.5°, L19 1.25°x1.25°, L20 UKMOG UKMO-HadGEM1, UK 1.875°x1.25°, L38 1.25°x1.25°, L20

INGVE INGV-SXG, Italy T42, L19 2°x(0.5-2°), L31

Issues in GCMs

• First, they differ on resolution

Issues in GCMs• Second: they differ in availabilityWCRP CMIP3 A1B-P A1B-T A1B-Tx A1B-Tn A2-P A2-T A2-Tx A2-Tn B1-P B1-T B1-Tx B1-Tn

BCCR-BCM2.0 OK OK OK OK OK OK OK OK OK OK OK OKCCCMA-CGCM3.1-T63 OK OK NO NO NO NO NO NO OK OK NO NOCCCMA-CGCM3.1-T47 OK OK NO NO OK OK NO NO OK OK NO NOCNRM-CM3 OK OK NO NO OK OK NO NO OK OK NO NOCSIRO-MK3.0 OK OK OK OK OK OK OK OK OK OK OK OKCSIRO-MK3.5 OK OK OK OK OK OK OK OK OK OK OK OKGFDL-CM2.0 OK OK OK OK OK OK OK OK OK OK OK OKGFDL-CM2.1 OK OK OK OK OK OK OK OK OK OK OK OKGISS-AOM OK OK OK OK NO NO NO NO OK OK OK OKGISS-MODEL-EH OK OK NO NO NO NO NO NO NO NO NO NOGISS-MODEL-ER OK OK NO NO OK OK NO NO OK OK NO NOIAP-FGOALS1.0-G OK OK NO NO NO NO NO NO OK OK NO NOINGV-ECHAM4 OK OK NO NO OK OK NO NO NO NO NO NOINM-CM3.0 OK OK OK OK OK OK OK OK OK OK OK OKIPSL-CM4 OK OK NO NO OK OK NO NO OK OK NO NOMIROC3.2.3-HIRES OK OK OK OK NO NO NO NO OK OK OK OKMIROC3.2.3-MEDRES OK OK OK OK OK OK OK OK OK OK OK OKMIUB-ECHO-G OK OK NO NO OK OK NO NO OK OK NO NOMPI-ECHAM5 OK OK NO NO OK OK NO NO OK OK NO NOMRI-CGCM2.3.2A OK OK NO NO OK OK NO NO OK OK NO NONCAR-CCSM3.0 OK OK OK OK OK OK OK OK OK OK OK OKNCAR-PCM1 OK OK OK OK OK OK OK OK OK OK OK OKUKMO-HADCM3 OK OK NO NO OK OK NO NO OK OK NO NOUKMO-HADGEM1 OK OK NO NO OK OK NO NO NO NO NO NO

Issues in GCMs

• Third: limited ability to represent present climates

Issues in GCMs

• Finally, they involve uncertainty– Averages: do they mislead?

Research areas: Available and usable climate data

BCCR-BCM2.0 CCCMA-CGCM3.1-T47

CNRM-CM3

CSIRO-MK3.0 CSIRO-MK3.5 GFDL-CM2.0

GFDL-CM2.1 INGV-ECHAM4 INM-CM3.0

IPSL-CM4 MIROC3.2-MEDRES MIUB-ECHO-G

MPI-ECHAM5 MRI-CGCM2.3.2A NCAR-CCSM3.0

NCAR-PCM1 UKMO-HADCM3 UKMO-HADGEM1

+++ UNCERTAINTY

So, what do we use currently?

• Input climate data used for climate change impact on agriculture assessments?

No researchers use GCM data “as is”

© CCAFS

Key messages…

1. Future climate predictions need to be improved (IPCC 5th AR)

2. GCMs are still not useful for agricultural researchers (CCAFS + partners)

So we need downscaling

• Even the most precise GCM is too coarse (~100km)

• To increase resolution, uniformise, provide high resolution and contextualised data

• Different methods exist… from interpolation to neural networks and RCMs– DELTA (empirical-statistical)– DELTA-VAR (empirical-statistical)– DELTA-STATION (empirical-statistical)– RCMs (dynamical)– …

Why do we need higher resolution data?

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The delta methodHay et al. 2007

• 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

Downscaling The delta method

Delta-VARMitchell et al. 2005

• AKA pattern scaling– Climate baseline: CRU– Provided by Tyndall Centre (UK)– Use captured variability in GCMs (MAGICC)

and anomalies– Run a new GCM pattern at a higher

resolution (CLIMGEN)– Calculate averages over specific periods

using the GCM scaled time-series

Delta-StationSaenz-Romero et al. 2009

• Most similar to original methods in WorldClim– Climate baseline: weather stations– Calculate anomalies over specific periods

(i.e. 2020s, 2050s) in coarse GCM cells– “Update” weather station values using GCM

cell anomalies within a neighborhood (400 km)

– Inverse distance weighted– Use thin plate smoothing splines with

LAT,LON,ALT as covariates for interpolation

RCMs: PRECISGiorgi 1990

• RCMs (Giorgi 1990)– Climate baseline: GCM boundary conditions– Develop complex numerical models to simulate

climate behaviour– “Nest” the RCM into a coarse resolution model

(GCM) and apply equations to re-model processes in a limited geographic domain

– Resolution varies between 25-50km– Takes several months to process– Requires a new validation (on top of the GCM

validation)

Disaggregation

• Similar to the delta method, but does not use interpolation– Climate baseline: CRU, WorldClim– Calculate anomalies over periods in GCM cells– Sum anomalies to climate baseline

Which one is best?Method Pros Cons

Delta

*Quick to implement* resolution*Applicable to ALL GCMs*Uniformise baselines

* Assumes changes only occur at broad scales* Assumes variables don’t change relationships in time* variables

Delta-VAR*Quick to implement*Applicable to ALL GCMs*Uniformise baselines* Reproduces GCM pattern

* Max. 50km resolution (CRU)* Reduces original variance in GCMs* variables

Delta-STATION

* Relatively quick to implement* More robust interpolation* Any resolution* Applicable to ALL GCMs

* Assumes changes only occur at broad scales* Assumes variables don’t change relationships in time* Each station represents changes in a 400km range * variables

RCMs

* Most climatologically robust*Applicability depends upon availability of GCM BC* variables

*Few platforms (PRECIS)*Massive storage and processing*Limited resolution (25-50km)*More development is required*Uncertainties difficult to assess

But, can we downscale (statistically)?

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Our databases

• Empirically downscaled, disaggregated for the whole globe at 1km to 20km

• Dinamically downscaled (PRECIS) for South America

• All will be at our portal (soon) http://gisweb.ciat.cgiar.org/GCMPage.html

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Reaching users globallyhttp://gisweb.ciat.cgiar.org/GCMPage

Downscaled 30 seg = 100% Resample 2.5min, 5min, 10min = 100%Convert to ascii and compress 30 seg = 30 % (19/63)Convert to ascii and compress resampled = 100%Compress grids resampled = 100%Publising compressed asciis and grids = 0%

Downscaled GCMs 7 periods for 63 scenarios (≈ 20 GCMs x 3 scenarios)

Downscaled 30 seg = 100% Resample 2.5min, 5min, 10min = 100%Convert to ascii and compress 30 seg = 33 % (21/63)Convert to ascii and compress resampled = 100%Compress grids Resamples = 100%Publising compressed asciis and grids = 0%

Dissagregated GCMs 7 periods for 63 scenarios (≈ 20 GCMs x 3 scenarios)

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100% (150/150y)HadCM3Q16 (SRES – A1B)

HadCM3Q0 (SRES – A1B)

ECHAM 5 (SRES – A1B)

HadCM3Q3 (SRES – A1B)

ECHAM 4 (SRES – A2)

ECHAM 4 (SRES – B2)

HadAM3P (Baseline)

HadAM3P (SRES – A2)

HadAM3P (SRES – B2)

ERA40 (Reanalisys)

NCEP:R2 (Reanalisys)

ERA – Interim (Reanalisys)

ERA 15 (Reanalisys)

PRECISruns

A quick comparison

1 interpolation (37 steps)

x 7 periods x 20 GCMs

= 1 week

210 weeks

x 3 scenarios

÷ 4 servers÷ 4 processes

= 26 weeks= 6 months!!

1 PRECIS run (10 year)

x 15 periodsx 1 GCM

= 2 weeks

30 weeks

x 1 scenario

÷ 3 servers÷ 2 processes

= 5 weeks

= 300 weeks = 6 years!!

x 20 GCM sx 3 scenarios

Hypothetically..

Capabilities and limitations

• Our in-house capacity:– Four 8-core processing servers in a blade array

under Windows (empirical downscaling)– Three 16-core processing servers in a blade array

under Linux (PRECIS)– ~80TB storage

• Publishing data is a lengthy process and requires massive storage and network capacity (esp. 1km global datasets)

TEMP. (JJA) RAINFALL (JJA)

Ethiopia

What’s next: validation of GCM simulations

What’s next?• Contextualising / validating GCM and RCM

predictionsPrecipitation Annual 1979

MRI Datasets vs. GHCN Stations

R² = 0.0516

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Precipitation Annual 2003 MRI Datasets vs. GHCN Stations

Máx: 4151.01

Mín: 3.454

Máx: 4724.028

Mín: 1.1344

Máx: 4796.844

Mín: 1.1839

Máx: 28.8573

Mín: -8.3415

Máx: 28.99

Mín: -9.22

Máx: 30.541

Mín: -7.413

Legend

t_2070_2099

Value

High : 9,22064

Low : 1,65393

t_2070_2099

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High : 7,92572

Low : 1,1236

Legend

t_2070_2099

Value

High : 9,22064

Low : 1,65393

t_2070_2099

Value

High : 7,92572

Low : 1,1236

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t_2070_2099

Value

High : 9,22064

Low : 1,65393

t_2070_2099

Value

High : 7,92572

Low : 1,1236

RCM PRECISBaseline Average 1961 – 1990 Total Precipitation (mm/yr)

Baseline Average Annual Mean Temperature (°C)

ECHAM5 HadCM3Q0 HadCM3Q16

High : 28,7984

Low : -24,2223

High : 28,7984

Low : -24,2223

High : 28,7984

Low : -24,2223

ECHAM5 HadCM3Q0 HadCM3Q16

What’s next?

Seiler 2009

What’s nextCCAFS climate data strategy

• Improve baseline data and metadata (incl. uncertainties)

• Gather and process AR5 projections• Downscale with desired methods• Evaluate (against weather stations) and assess

uncertainties• Publish all datasets (original and downscaled)

and results• Use the AMKN platform to link climate data,

and modelling outputs

In summary• CIAT and CCAFS data to be one single product

(other datasets are being added)• Downscaling is inevitable, so we are aiming to

report caveats on the methods• Continuous improvements are being done• Strong focus on uncertainty analysis and

improvement of baseline data• Reports and publications to be pursued…

grounding with climate science