Third level Future climate projections of NYC watershed: GCM selection and downscaling A.Anandhi 1, A. Frei 1, D.C. Pierson 2, H. Markensten 3, D. Lounsbury

Future climate projections of NYC watershed: GCM selection and downscaling

A.Anandhi 1, A. Frei 1, D.C. Pierson 2, H. Markensten 3, D. Lounsbury 2, M.S. Zion 2, A.H. Matonse 1, and E.M. Schneiderman2.

1 CUNY Institute for Sustainable Cities/Hunter College, CUNY, NY.2 Bureau of Water Supply, New York City Department of Environmental Protection3 Upstate Freshwater Institute, Syracuse, NY.

New York City Department of Environmental ProtectionBureau of Water Supply

Water Quality

Watershed Science & Technical Conference 2009

9/14/2009 WSTC 2009 2/33

Outline of the presentation

• Overview of Climate Models and Emission Scenarios

• GCM selection : – Phase I

– Phase II

• Downscaling :– Phase I

– Phase II

• Conclusions

• Future direction

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Overview of Climate Models and Emission Scenarios

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Introduction

• Nature of climate system is chaotic and its exact state cannot be predicted years ahead.

• Global climate models (GCM) simulate the climate by representing the climate system mathematically using equations and parameters.

• Scenarios are defined to represent future climate, which are alternative images of how the future climate might unfold.

• Multiple scenarios allow uncertainty in the possible future climate to be estimated.

• A set of Global climate models (GCM) provide our best information to future realizations (time series) of climate.

• There are multiple GCM models available that are developed by independent teams, and which use different modeling algorithms and approachs to simulate basic climate processes

• Uncertainty in future climate simulations can be evaluated by using different GCM models to simulate the same future period

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The GCMs are models that represent the climate system

mathematically by using the ...

.... Using basic laws of physics, fluid motion & chemistry etc; ……parameterization of the processes that are not explicitly taken into account.

Earth’s shape, rotation, revolution

Processes

Image source: IPCC http://ipcc-wg1.ucar.edu/wg1/FAQ/wg1_faq-1.2.html

Components

Responses to forcings

their interactions

Lithosphere

Atmosphere

Hydrosphere

CryosphereBiosphere

Solar radiation

The physical processes can have different approximations and simplifications because they are(1) too complex to include in the model and still have the model run fast on a computer, or

(2) because our understanding of those processes is still too poor to accurately model them with equations.

Lack of consensus as to which approximations are most important to modeling results, is also another reason for different modeling approaches to be developed.

Overview of a GCM

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•IPCC’s (Intergovernmental Panel for Climate Change) Special Report on Emissions Scenarios (SRES) have become the standard scenarios.

Describe different future climate • using 4 storylines (SRES A1,

A2, B1, B2)• In terms of Greenhouse gases

(GHGs) emitted in future based on how

– world population, – economy,– new technologies, – energy resources,– land use changes &– political structure

may evolve over the next few decades.

SRES - Emission scenarios

http://www.grida.no/publications/other/ipcc_sr/?src=/climate/ipcc/emission/

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Scenarios in SRES

http://www.grida.no/climate/ipcc/emission/images/spm1.gif

Highest emissionsMedium emissions Low emissions

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GCM Selection

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Phase I Climate Change Simulations• We used the future climate projections from GCM models pertaining to

Intergovernmental Panel for climate change (IPCC’s) AR4 report– Four GCMs (NCAR, ECHAM, GISS & CGCM3)– All the three SRES emission scenarios that are available in the report were

used (A1B, A2 & B1 representing medium, high and low emissions in future)

GCM - Emission Scenario

Current Conditions Scenario (20C3M)

65 Year into Future Scenario

(2045-2065)

100 Year into Future Scenario

(2081-2100)

CGCM3-A1B 1981-2000 2046-2065 2081-2100

CGCM3-A2 “ “ “

CGCM3-B1 “ “ “

ECHAM-A1B 1981-2000 2046-2065 2081-2100

ECHAM-A2 “ “ “

ECHAM-B1 “ “ “

GISS-A1B 1981-2000 2046-2065 2081-2100

GISS-A2 “ “ “

GISS-B1 “ “ “

NCAR-A1B 1980-1999 2046-2065 2080-2099

NCAR-A2 “ “ “

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Phase II Climate Change Simulations - need for GCM selection

• Future climate projections in NYC watersheds are subjectedto uncertainty due to :– different GCMs &

– climate scenarios

• One way to represent this uncertainty is use a larger variety of GCMs & scenarios to represent the various future projections

• However, with the increase in the number of future climate projections, the watershed and reservoir runs for impact studies increases exponentially with each addition.

• Hence there is a need to select the GCMs & scenarios for impact assessments by evaluating the GCM simulations

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Many methods divide the world into 22 regions for evaluation

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The evaluation of GCMs for Eastern North America (ENA)

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Challenges in evaluation of climate models

• There are many aspects of model behavior that can be compared with the real world

– Morphology of climate – by comparing statistics such as means, variances, covariance etc

– Budgets, balances & cycles – e.g energy budget, water balance, carbon cycle– Process studies of climate e.g comparing monsoons, convective processes

• Evaluation can be limited by errors and/or lack of measured data

• To some extent models are tuned to reproduce the observed climate – E.g Models to a certain degree may appear realistic in some respects, but this may be

a result of compensating errors

• There is no generally accepted metric for measuring model performance as a whole (Gleckler et al 2008)

– Wide variety of variables are of interest– Observational uncertainties are often substantial but poorly estimated– Some aspects of climate model simulations are deterministic, while others are not,

making quantitative verification more complex

• Different models show varying strengths & weakness

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Evaluation/Reliability of climate models – Along 3 main lines

Model Performance

How well a model simulates the observed climate record (Girogi and Mearns, 2002; Johnson and Sharma, 2009)

orThe skill of models in simulating present-day climate (Raisanen 2007)

Model Convergence

How consistent the predictions are from a range of models in time & space (Girogi and Mearns, 2002; Johnson and Sharma, 2009)

orInter-model agreement on future climate changes (Raisanen 2007) climate changes = future climate –simulated present day climate

Ability of models to simulate observed large scale changes (Raisanen 2007)

• E.g. Decrease in Arctic Ocean ice cover• E.g. Changes in water temperature within the top 700m of the worlds oceans since

1960

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Methods of evaluation in climate models

Component level System level

Numerical methods & physical parameterizations in GCMs are compared

Ensembles Individual Models

the outputs from GCM compared with observed values

1. Simple Ensemble Average (Lambert & Boer, 2001)

2. Simple Ensemble Median

3. Ensemble variance

4. Their combinations e.g mean of median etc

1. Mean,

2. Median,

3. Variance

Methods Presently Under Evaluation at DEP

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Data UsedDaily precipitation, temperature (av., max, min), wind speed & solar radiation data from 23 GCMs are obtained from IPCC’s data archive

GCM Scenarios:

Baseline : 20C3M

Future : A1B A2 B1

Time slice:Baseline : 1981-2000Future : 2045-2065 : 2081-2100

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Methodology followed in the study to obtain future projections of climate variables for NYC watershed

Select climate variablesto be evaluated

Download data for these variables from different GCM runs in IPCC AR4

Regrid the data from different GCMs to a common grid

Select one or more methods of evaluation

Evaluate the GCM models for the region of interest

Downscaling

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Need for regridding?

Different GCMs have different grid resolutions

Regridding brings the values of variables to a common grid for their comparison with other GCMs

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Results – GCM selection

Average precipitation (1981-2000)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Bcc

r-pr

cccm

a-T

47

cccm

a-T

63

cnrm

-cm

3

csiro

_mk3

_0

csiro

_mk3

_5

cfdl

_cm

2_0

giss

_aom

iap_

fgoa

ls

ingv

_ech

am4

ipsl

GCM model names

Pre

cip

ita

tio

n (

mm

/da

y)

Average

Observed : Average

Ensemble : AverageEuclidian distance

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Results – GCM Selection

Precipitation variance (1981-2000)

0

10

20

30

40

50

60

70

80B

cc

r-p

r

cc

cm

a-T

47

cc

cm

a-T

63

cn

rm-c

m3

cs

iro

_m

k3

_0

cs

iro

_m

k3

_5

cfd

l_c

m2

_0

gis

s_

ao

m

iap

_fg

oa

ls

ing

v_

ec

ha

m4

ips

l

GCM model names

Pre

cip

ita

tio

n (

mm

2 /da

y)

Variance

Observed : Variance

Ensemble : Variance

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Results – GCM selection …contd

Median precipitation (1981-2000)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80B

ccr-

pr

cccm

a-T

47

cccm

a-T

63

cnrm

-cm

3

csir

o_

mk3

_0

csir

o_

mk3

_5

cfd

l_cm

2_

0

gis

s_a

om

iap

_fg

oa

ls

ing

v_e

cha

m4

ipsl

GCM model names

Pre

cip

ita

tio

n (

mm

/da

y) Median

Observed : Average

"Ensemble : Average"

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Results – Model selection

Ensemble Mean Median Variance

Mean 3.22 3.20 0.30

Median 0.82 0.90 0.14

Variance 35.38 34.65 177.20

Ranking Criteria : Euclidian Distance of Individual model mean from observed mean.

GCMi iGCM

Euclidian Distance

Observed mean

GCM meanObs

GCM Model Rank

csiro_mk3_5 1

cfdl_cm2_0 2

cccma-T63 3

cccma-T47 4

ingv_echam4 5

cnrm-cm3 6

Bccr-pr 7

giss_aom 8

iap_fgoals 9

csiro_mk3_0 10

ipsl 11

Ranking Criteria : Euclidian Distance of Individual model variance from observed variance.

)var()var( iGCMi GCMObs

iGCMi GCMObs

GCM Model Rank

cccma-T47 1

cccma-T63 2

cnrm-cm3 3

Bccr-pr 4

ingv_echam4 5

cfdl_cm2_0 6

iap_fgoals 7

csiro_mk3_0 8

csiro_mk3_5 9

giss_aom 10

ipsl 11

Preliminary rankings

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GCM Downscaling

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• The future climate projections from GCMs may not be directly used for NYC watershed because they are at coarser resolution & scale

Watershed scale

Marshes

Sea

Vegetation

http://www.windows.ucar.edu/physical_science/basic_tools/images/ipcc_ar4_wg1_ch1_fig_1_4_big.gif

The future climate projections at watershed scale

Rock

beach

GCM scale

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Need for downscaling

Watershed scale GCM scale

Change factor

Type of downscaling

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• Change Factor Methodology

• Additive factors (CFadd) used to adjust historical air temperature record

• Multiplicative factors (CFmul) used to adjust historical precipitation records and other variables

• Factors calculated on a monthly basis and applied to observed values

Phase I Downscaling

• Change Factor Methodology

• Additive factors (CFadd) used to adjust historical air temperature record

• Multiplicative factors (CFmul) used to adjust historical precipitation record

• Factors calculated on a monthly basis and applied to observed values

GCMbGCMfCFadd

GCMbGCMfCFmul

muliimul CFLObLSf *, addiiadd CFLObLSf ,

GCMbGCMfCFadd GCMbGCMfCFadd

9/14/2009 WSTC 2009 27/33

Downscaling: Phase 1- Change Factor MethodExample of Calculating a Single Factor

Source: Anandhi et al 2009

GCMf

GCMb

GCMb GCMf

Time series of GCM Baseline ( GCMb ) &GCM future ( GCMf )

Quantification of additive / multiplicative change factors ( CFadd / CFmul )

Local observed baseline climate ( LOb )

Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,

muliimul CFLObLSf *,

Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf


GCMb GCMfGCMb GCMf



Local observed baseline climate ( LOb )Local observed baseline climate ( LOb )

Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul


GCMbGCMfCFmul GCMbGCMfCFmul

addiiadd CFLObLSf ,


addiiadd CFLObLSf , addiiadd CFLObLSf ,

muliimul CFLObLSf *, muliimul CFLObLSf *,

Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMfGCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)









Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul



addiiadd CFLObLSf ,




Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMfGCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)









Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul



addiiadd CFLObLSf ,




Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMfGCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)









Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul



addiiadd CFLObLSf ,




Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMfGCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)









Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul



addiiadd CFLObLSf ,




Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMfGCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)









Eq. (4)

Eq. (6)

GCM future –A1B

GCM baseline –20C3M

Estimating Change factor

Measured data

GCMf

GCMb

GCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul



addiiadd CFLObLSf ,




Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMfGCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)









Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMf

GCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul

GCMbGCMfCFadd

GCMbGCMfCFmul

addiiadd CFLObLSf ,


addiiadd CFLObLSf ,


Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)

GCMbGCMfCFadd

GCMbGCMfCFmul



addiiadd CFLObLSf ,




Eq. (4)

Eq. (6)

GCMfGCMf

GCMbGCMb

GCMb GCMfGCMb GCMf


GCMb GCMfGCMb GCMf




Eq. (3)

Eq. (5)









Eq. (4)

Eq. (6)

9/14/2009 WSTC 2009 28/33

40

20

0

-20

-40

2000 2000.083 2000.167 2000.250

de

gre

es C

Daily Air Temperature Input Data for Model Simulations

Jan Feb Mar

Degrees C

0

40

-40

2000 ->

Baseline

Future +65yr

20

-20

Application of Climate Change Delta Method – Air Temp

Average GCM-Projected Air Temperature by Month for Control and Future Periods

Month J F M A M J J A S O N D

Baseline(1981-2000) -5.4 -3.3 2.4 7.5 12.2 17.7 20.3 19.2 15.0 9.6 2.2 -2.5

Future GCM(2046-2065) -1.6 -0.2 4.0 9.3 15.4 20.0 22.8 21.9 18.6 11.4 4.9 0.2

Temp. Change Factor = Future-Control 3.8 3.1 1.6 1.8 3.3 2.3 2.5 2.8 3.6 1.8 2.8 2.6

(Future = Measured + Factor)

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Example of Phase I downscaled data

30

15

0

-15

-30

1966 1976 1987 1997 2007Base Calendar Year

C

Min Temp in : MetPre_Can_ECHAM_A2_8100_0907311334Delta T min : MetPre_Can_ECHAM_A2_8100_0907311334

Minimum Daily Air Temperature Cannonsville Watershed

Baseline Historical Data2080 – 2081 ECAM A2 Future Climate Simulation

9/14/2009 WSTC 2009 30/33 Source: Anandhi et al 2009

GCMb GCMf

Time series of GCMb & GCMf

Estimate CDF. Fix the value of n and r

n = 1

n = 3

n = 2

r = r3

r = r2

r = r1

3,3 rGCMf

3,3 rGCMb

2,2 rGCMf

2,2 rGCMb

1,1 rGCMf

1,1 rGCMb

Time series of LOb

n = 1

n = 3

n = 2

Estimate CFs in each n using the values of GCMf & GCMb in that n

Divide the CDF based on n,r

Estimate CDF

3,3,,3,3,3,3, raddjrjradd CFLObLSf



Estimate LOf

r = r3

r = r2

r = r1

3,3,,3,3,3,3, * rmuljrjrmul CFLObLSf



3,33,33,3, rrradd GCMbGCMfCF

3,33,33,3, / rrrmul GCMbGCMfCF





GCMb GCMfGCMb GCMf

Time series of GCMb & GCMf

Estimate CDF. Fix the value of n and r

n = 1

n = 3

n = 2

n = 1

n = 3

n = 2

r = r3

r = r2

r = r1

3,3 rGCMf

3,3 rGCMb

2,2 rGCMf

2,2 rGCMb

1,1 rGCMf

1,1 rGCMb

Time series of LOb

n = 1

n = 3

n = 2

n = 1

n = 3

n = 2

n = 1

n = 3

n = 2

Estimate CFs in each n using the values of GCMf & GCMb in that n

Divide the CDF based on n,r

Estimate CDF




Estimate LOf

r = r3

r = r2

r = r1





3,33,33,3, / rrrmul GCMbGCMfCF 3,33,33,3, rrradd GCMbGCMfCF








Downscaling : Phase II - Improved CFM type of downscaling Multiple Change Factors Calculated over Portions of Frequency Distribution

9/14/2009 WSTC 2009 31/33

Example of Phase II downscaled data

Similar studies were carried out for precipitation, Av. & max. temperature, wind speed & solar radiation

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Conclusions• GCM Selection Phase I

– Used GCMs readily available to DEP – Columbia GISS project

• GCM Selection Phase II

– 11 individual GCM models were evaluated based on mean, median & variance

– The mean, median & variance of the ensemble of 11 models were also estimated

– Models are ranked based on their Euclidian distance from the observed values

• The rest of the available GCM models ( from the total 23) will be evaluated

• Downscaling Phase I

– Change factors applied on a monthly basis

• Downscaling Phase II

– Multiple change factors applied by frequency distribution

– Other method will be investigated• Statistical downscaling• Dynamic downscaling ie Regional Climate Models

9/14/2009 WSTC 2009 33/33

Future Direction

• Evaluate the GCMs using different evaluation techniques

• Uncertainty Analysis in GCM evaluation

• Use different downscaling techniques

• Uncertainty Analysis in downscaling

• Analysis of extreme values in a changed climate

• Similar studies on other meteorological variables such as average, maximum & minimum temperatures, wind speed & solar radiation

Questions ?

Thank You

Documents

Third level Future climate projections of NYC watershed: GCM selection and downscaling A.Anandhi 1, A. Frei 1, D.C. Pierson 2, H. Markensten 3, D. Lounsbury