Research on Climate Change Impacts in the United States€¦ · 04/10/2012 · 2 Emissions 11/2/12...

Research on Climate Change Impacts in the United States

James McFarland, Jeremy Martinich, Marcus Sarofim Climate Change Division, US EPA

Stephanie Waldhoff, PNNL

Latin American Modeling Project San José, Costa Rica

4 October 2012

Overview

•  Goals and motivation

•  Methodology

•  Illustrative results

•  Next steps

U.S. Environmental Protection Agency 2

Goals and Motivation for Impacts Research •  Our goal is to quantify where possible and communicate the

benefits (i.e., avoided or reduced impacts) of mitigation & adaptation actions.

•  The research explores how impacts and damages may change under a consistent set of scenarios, data, and assumptions.

–  Existing impacts literature is largely based on inconsistent assumptions along the causal chain from socio-economics to emissions to climatic effects and impacts.

•  Initial focus is on: –  Risks and impacts within the U.S., without ignoring key global linkages

or key regional components. •  Impacts and benefits across a range of sectors, e.g., water resources, human health,

ecosystems, energy.

–  Potential benefits of mitigation scenarios (adaptation later). –  Analyzing key sources of uncertainty, including emissions pathway,

climate sensitivity, climate models, etc. 3

Our ideal tools and results •  Integrated model(s) with internally

consistent emissions drivers, impact sectors, and economic valuation

–  Climate impacts feed back into the economy and climate

•  Identify, quantify, and be transparent about key uncertainties along the causal chain

•  Multiple future scenarios, BAU and policies

•  Outputs that communicate effectively to multiple audiences about how impacts and risks change from one scenario to another.

4

Socio-economic, policy drivers

Emissions

Atmospheric concentrations

Radiative forcing

Historical and projected temperature, precipitation,

sea level rise, etc.

Potential risks and impacts, economic damages

GHG mitigation and adaptation measures

U.S. Environmental Protection Agency

Methodology

5 U.S. Environmental Protection Agency

Analytical Goals •  Develop estimates of climate change impacts and damages in

multiple sectors that can be synthesized –  Begin with integrated assessment (IA) models to develop three internally

consistent socio-economic, emissions, and climate scenarios (BAU, RF 4.5, RF 3.7)

–  All sectoral models use consistent population, GDP, and emissions data –  Climate inputs consistent with all socio-economic and emissions scenarios

•  Explore uncertainties around impacts estimates –  Scientific: Multiple climate sensitivities (2.0, 3.0, 4.5, and 6.0) –  Model: Use of multiple IA and sectoral models where possible –  Variability: Analysis of changing temperature and precipitation patterns

•  Understand what drives differences in model results –  Comparison of data inputs and outputs –  Discussions about model structures, methods, etc.,.


Methodology •  Begin with IA models (IGSM and GCAM) to develop three

internally consistent socio-economic, emissions, and climate scenarios –  Reference: Business as usual

•  GDP and population harmonized with US (EIA) data through 2035, EPPA projections through 2100

–  Policy scenarios: •  4.5 W/m2 and 3.7 W/m2, stabilization in 2100

•  Multiple climate sensitivities (2.0, 3.0, 4.5, and 6.0)

•  Climate data from MIT’s 3D (CAM) component of IGSM

•  Sectoral models develop estimates with these consistent socio-economic and climate data


Impacts Research Operational Schematic

8

Yield Changes (Crops, forests)

Yield Changes (Crops, forests)

Data Flow

•  Inputs –  Reference GDP and population (EIA

through 2035)

•  IA Model Outputs –  Global GHG concentrations –  Global and domestic emissions

•  CO2, non-CO2 GHG, criteria pollutants

–  Sea Level Rise

9

–  Policy scenario, RF targets

–  Temperature change

•  Global annual average •  Gridded monthly, daily, hourly

–  Precipitation •  Gridded monthly, daily, hourly

•  Temperature-related mortality •  SLR property damages and adaptation

response costs •  Road and bridge infrastructure

adaptation •  Inland flooding damages •  Water supply and demand •  Drought risk (not monetized) •  Electricity supply •  Energy demand •  Population

•  Crop yields projections •  Vegetative carbon sequestration and

provisioning of grazing lands •  Forest fire frequency/magnitude and

suppression costs •  Coral reef cover and recreational/

existence values •  Freshwater fish habitat and recreational

fishing impacts •  Air quality

•  Changes in impact sectors (use IA outputs as inputs)


Examples of Data Needs for Sectoral Modeling

10

Sector/Model Socio-‐economic Climate OtherCOMBO Global avg ΔTSLR/Coastal Property Model GDP growth Global avg SLR

Ecoservices/forest fires ΔLU (Developed Land)Monthly avg T, daily max T, monthly mean precip

CO2 Concentrations, elevation

Inland flooding Population growth Monthly ΔPrecip

Heat healthPop growth, demographic changes, VSL = f(GDP/cap)

Max and min daily T

Bridge vulnerabilityDaily precip to calculate 2 y and 100 y 24 hour max precip

Land cover type

Drought riskMonthly avg temp and precip

Freshwater fisheriesValue of fishing day, Population growth

Monthly avg max T and avg precip

Water supply-‐demand Population growth

Monthly avg, max, and min T, total monthly precip, cloud cover, wind, relative humidity

FASOM Demand (population, GDP)Yield changes due to climate changes (EPIC)

IPM Population, GDP ΔT (daily/hourly)

Inputs

Illustrative Results


CO2 Emissions

11/2/12 12

0

2,000

4,000

6,000

8,000

10,000

12,00020

05

2015

2025

2035

2045

2055

2065

2075

2085

2095

MIT ReferenceGCAM ReferenceMIT Policy4.5GCAM Policy4.5MIT Policy3.7GCAM Policy3.7

U.S. Annual CO2Emissions

Mt-‐CO

2/year

010,00020,00030,00040,00050,00060,00070,00080,00090,000

100,000

2005

2015

2025

2035

2045

2055

2065

2075

2085

2095

MIT Reference

GCAM Reference

MIT Policy4.5

GCAM Policy4.5

MIT Policy3.7

GCAM Policy3.7

Global Annual CO2 Emissions

Mt-‐CO

2/year 0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

2000 2020 2040 2060 2080 2100

GCAM GlobalFossil fueland LU CO2 Emissions (Mt-‐CO2)

Annual CO

2Emiaaions (Mt-‐CO2)

CO2 Concentrations


350

370

390

410

430

450

470

490

2000 2020 2040 2060 2080 2100

ppm

CO

2

MIT-CS2

MIT-CS6

PNNL-CS2

PNNL-CS6

350

450

550

650

750

850

2000 2020 2040 2060 2080 2100

ppm

CO

2

MIT-CS2 MIT-CS6 PNNL-CS2 PNNL-CS6

Reference 3.7 Policy

Forcing


0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

2000 2020 2040 2060 2080 2100

W/m

2 Si

nce

Prei

ndus

tria

l

3.7MIT-2

3.7MIT-3

3.7MIT-4.5 3.7MIT-6

3.7PNNL

0

2

4

6

8

10

12

2000 2020 2040 2060 2080 2100

W/m

2 si

nce

Prei

ndus

tria

l

Ref-MIT-2 Ref-MIT-3 Ref-MIT-4.5 Ref-MIT-6 Ref-PNNL-2 Ref-PNNL-6


Temperature


0

1

2

3

4

5

6

7

8

9

1990 2010 2030 2050 2070 2090

Deg

rees

C S

ince

199

0

PNNL-Ref,CS2 PNNL-Ref,CS6 MIT-Ref,CS2 MIT-Ref,CS6

0

0.5

1

1.5

2

2.5

3

3.5

1990 2010 2030 2050 2070 2090

Deg

rees

C S

ince

199

0

PNNL-3.7CS2

PNNL-3.7CS6

MIT-CS2

MIT-CS6


0.0

0.2

0.4

0.6

0.8

1.0

0 2 4 6 8 10

Reference Policy 4.5 Policy 3.7

ObservedΔT in 2100 (above 1990)

Prob

ability

Reference

0-‐2

2-‐3

3-‐4

4-‐5

5-‐6

6-‐8

>8

Policy 4.5

0-‐2

2-‐3

3-‐4

4-‐5

5-‐6

6-‐8

>8

Policy 3.7

0-‐2

2-‐3

3-‐4

4-‐5

5-‐6

6-‐8

>8


Presentation of Results (Global Average ΔT from 1990, GCAM)

0%

10%

20%

30%

40%

50%

60%

0 2 4 6 8 10




Reference

0-‐2

2-‐3

3-‐4

4-‐6

6-‐8

>8

Policy 4.5

0-‐2

2-‐3

3-‐4

4-‐6

6-‐8

>8

Policy 3.7

0-‐2

2-‐3

3-‐4

4-‐6

6-‐8

>8

Presentation of Results (Global Average ΔT from 1990, IGSM)

0.0

0.2

0.4

0.6

0.8

1.0

0 2 4 6 8 10



Prob

ability

0%

10%

20%

30%

40%

50%

60%

0 2 4 6 8 10



Sea Level Rise (meters)

11/2/12 U.S. Environmental Protection Agency 18

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1990 2010 2030 2050 2070 2090

Ref,CS2

Ref,CS3

Ref,CS4.5

Ref,CS6

MIT-2

MIT-3

MIT-4.5

MIT-6

Change in # of days above present day 95th percentile

19

Daily Max Temperature

Changes in Temperature Extremes

Business As Usual

Less Stringent Policy (RF4.5)

More Stringent Policy (RF3.7)

Frost Frequency

Cha

nge

in #

of d

ays

abov

e pr

esen

t day

95t

h per

cent

ile

# of future frost days per year Without mitigating GHGs, today’s hottest days become more frequent, and

the number of frosts will decrease.

20

Changes in Extreme Precipitation

Business As Usual

More Stringent Policy (RF3.7)

Less Stringent Policy (RF4.5)

Cha

nge

in #

of d

ays

abov

e pr

esen

t day

95t

h per

cent

ile C

hange in # of days above present day 95th percentile

Winter

Summer

Without mitigating GHGs, extreme precipitation will become more common.

21

Cha

nge

in n

umbe

r of d

roug

ht m

onth

s w

ithin

a 3

0yr w

indo

w (d

ifere

nce

betw

een

1980

-200

9 an

d 20

85-2

115)

More stringent (RF 3.7) Less stringent (RF 4.5) BAU

Change in num

ber of drought months w

ithin a 30yr window

(difference between 1980-2009 and 2085-2115)

Changes in Drought Risk Through 2100

22

Estimated Decline in U.S. Coral Reefs

S. Florida

Loss of Hawaiian Coral Cover

Sum of Lost Annual Rec. Benefits in Hawaii

•  GHG mitigation delays Hawaiian coral reef loss compared to BAU. –  The more stringent policy scenario (RF3.7) avoids ~$9B in lost

recreational value for Hawaiian reefs, compared to the BAU. •  GHG mitigation provides only minor benefit to coral cover in South

Florida and Puerto Rico (not shown), as these reefs are already being affected by climate change, acidification, and other stressors.

Energy: National HDD & CDD

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

2012 2020 2030 2040 2050

-70.00%

-60.00%

-50.00%

-40.00%

-30.00%

-20.00%

-10.00%

0.00% 2012 2020 2030 2040 2050

% Change in Heating Degree Days Relative to 2005

% Change in Cooling Degree Days Relative to 2005

Business as usual: Policy (RF3.7):

Next Steps


Peer Review of Impacts Research

•  Peer review of methods and results –  Special issue

•  Overview of methodology and goals •  Individual papers on each component of the project

–  Individual papers for each topic/impacts sector •  Overall approach and scenario development •  Extreme events and assessing uncertainty of regional climate change •  Coastal development, infrastructure, and heat health •  Energy supply/demand and water resources (drought, flooding damages, water supply/

demand) •  Ag/forestry and ecosystems (coral reefs, freshwater fish, vegetation/wildfire)

–  Key methods and results assembled in a single paper •  Will require a significant amount of supplementary material.


Communication of Results

•  Estimating impacts and economic damages in an analytically rigorous and consistent way will enable clear communication of climate change impacts and risks to a variety of audiences –  Researchers

•  Distribute findings through peer reviewed publication and conference presentations

–  Policy makers •  Schedule briefings with interested committees •  Incorporate results into legislative analyses

–  Public •  Share results through EPA's updated climate change website •  Summary report


Other Potential/Future Impact Analyses

•  Population –  Leverage EPA-ORD’s ICLUS model to examine climate change

impacts on regional population growth –  Disaggregated data may be used in future iterations as inputs to

other sectoral models (e.g. land use, energy)

•  Energy Supply –  NREL’s ReEDS model to look at climate change impacts on energy

transmission, including extreme events

•  State-level impacts –  Penn State, Boston University developing a state-level impacts

model using sectoral damage functions to examine impacts with interstate trade


Appendix: Supplementary Materials


Coordination with Integrated Assessment (IA) Models

•  EPA/OAP supports number of global climate/economic modeling groups:

–  MIT Joint Program’s IGSM framework (IA) –  PNNL-JGCRI’s GCAM (IA)

•  Coordinate with these groups to: –  Harmonize key inputs (GDP, pop. growth,

radiative forcing targets) –  Obtain climate projections for use in sectoral

models –  Utilize sectoral components of these broader

frameworks for impact and damage analyses

29

The MIT IGSM Model


Scenario Design •  Reference scenario: As discussed previously •  Policy: As discussed, target forcing in 2100

as a change from preindustrial –  MIT: Manual target –  PNNL: Automated

•  Climate Parameters: –  MIT: KV = 0.5 cm2/s, Aerosol in 80s = -0.25 to

-0.95 W/m2, depending on CS parameter –  PNNL: Kv = 2.3 cm2/s, Aerosol in 1990= about

-1.3 W/m2


MIT: Climate Sensitivity and Aerosol Forcing


Existing Data Sets for Integrated Analysis

•  IPCC Special Report on Emissions Scenarios (SRES, 2000) –  Insufficient regional disaggregation (only four world regions) –  Scenarios do not reflect explicit climate policies, but rather development paths with unclear

costs –  Critiqued for unrealistic narrowing of incomes across regions

•  IPCC Relative Concentration Pathways (RCPs) –  Break the link between socio-economics, emissions and atmospheric concentrations. –  This is a design feature to allow for socio-economic and climatic research to proceed in

parallel.

32

Data Flow

•  Inputs –  Reference GDP and population (EIA

through 2035)

•  IA Model Outputs –  Global GHG concentrations –  Global and domestic emissions

•  CO2, non-CO2 GHG, criteria pollutants

–  Sea Level Rise

33

–  Policy scenario, RF targets

–  Temperature change

•  Global annual average •  Gridded monthly, daily, hourly

–  Precipitation •  Gridded monthly, daily, hourly

•  Temperature-related mortality •  SLR property damages and adaptation

response costs •  Road and bridge infrastructure

adaptation •  Inland flooding damages •  Water supply and demand •  Drought risk (not monetized) •  Electricity supply •  Energy demand •  Population

•  Crop yields projections •  Vegetative carbon sequestration and

provisioning of grazing lands •  Forest fire frequency/magnitude and

suppression costs •  Coral reef cover and recreational/

existence values •  Freshwater fish habitat and recreational

fishing impacts •  Air quality

•  Changes in impact sectors (use IA outputs as inputs)


What is being measured? •  GDP estimates that include the cost of the policy (i.e. GDPPolicy < GDPReference)

GDP/cap is lower under policy scenarios

•  Economic damages are calculated using two components: –  Impacts that measure physical units (e.g. deaths) –  A measure of the economic value of those impacts

•  Damages measure the economic value of those impacts

•  When the economic value is correlated with GDP/cap (e.g. VSL or property values), the damages under a policy scenario will be lower for two reasons:

–  Impacts are smaller –  Economic value is smaller

•  Therefore, the benefits of the policy are larger than if GDP/cap was constant

•  Is this a problem? –  Only because the climate change impacts are not themselves included in the GDP measures—this

work is intended to enable inclusion of these damages


35

74.8%

20.9%

4.4%

0-‐3.6

3.6-‐5.4

5.4-‐7.2

7.2-‐9.0

9.0-‐10.8

10.8-‐14.4

Full Participation1.18%

29.3%

37.8%

18.9%

8.1%

4.7%

Reference

0-‐2

2-‐3

3-‐4

4-‐5

5-‐6

6-‐8

11.1%

47.2%

27.0%

10.1%

4.0% 0.6%

Developing Country Delay

•  The pie charts show the approximate probability of observed global mean temperature changes in 2100, relative to pre-industrial, falling within specific temperature ranges under reference, developing country action delayed until 2050, and G8 international action scenarios.

–  The figures were developed using MAGICC 5.3 and the truncated (at 10° C) Roe and Baker (2007) distribution over climate sensitivity. –  Observed temperature change is that resulting from the concentration levels in a specific year. –  See appendix 5 for equilibrium temperature results.

•  Observed temperature change does not equal the change in equilibrium temperature because –  CO2e concentrations rise after 2100: Equilibrium temperature change is not achieved until after CO2e concentrations are stabilized. In

this analysis, CO2e concentrations will continue to rise after 2100. Therefore, changes in equilibrium temperature will differ from the observed temperature changes.

–  Ocean temperature inertia: This inertia causes the equilibrium global mean surface temperature change to lag behind the observed global mean surface temperature change by as much as 500 years. Even if CO2e concentrations in 2100 were stabilized, observed temperatures would continue to rise for centuries before the equilibrium was reached.

•  Under the Reference scenario (1st chart), the probability of the observed temperature change in 2100 being below 2 degrees C is approximately 1%, while there is a nearly 75% probability associated with this under the Full Participation scenario (3rd chart).

•  The probability of being above 4 degrees C is about 32% in the Reference case, while it is just under 15% in the Delayed Participation scenario (2nd chart) and zero under Full Participation (3rd chart).

Example of limited benefits analysis to date: Probability of Observed Temperature Changes in 2100 (S. APA)

° Celsius ° Fahrenheit

11/2/12 36

$30,000

$50,000

$70,000

$90,000

$110,000

$130,000

$150,000GCAM All

MIT ReferenceMIT Policy4.5MIT Policy3.7

U.S. GDP per capita

GDP

/cap

(2005$)

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

$35,000

$40,000

GCAM All

MIT ReferenceMIT Policy4.5MIT Policy3.7

Gross World Product per capitaGDP

/cap

(2005$)

GDP per capita

Example: Heat Deaths


GCAM IGSM GCAM IGSMGDP/cap-‐Ref 80,258 86,209 152,445 159,252GDP/cap-‐Policy 77,764 137,943VSL-‐Ref 10.1 10.1 13.0 12.9VSL-‐Policy 9.7 12.2Value-‐Ref deaths 123,401 123,161 289,262 285,501 Value-‐Pol deaths 89,761 85,967 129,895 121,047 Policy Benefit 33,641 37,195 159,367 164,454

Using Policy VSL times change in # deaths:32,219 148,512

2050 2100

Date post:	06-Oct-2020
Category:	Documents
Upload:	others
View:	0 times
Download:	0 times

Research on Climate Change Impacts in the United States€¦ · 04/10/2012 · 2 Emissions 11/2/12...

Documents