Use of RegCM Results in Climate Change Impacts Studies

Linda O. Mearns

NCAR/ICTP

Workshop on the Theory and Use of Regional Climate Models

ICTP, Trieste, June 2003

“Most GCMs neither incorporate nor provide information on scales smaller than a few hundred kilometers. The effective size or scale of the ecosystem on which climatic impacts actually occur is usually much smaller than this. We are therefore faced with the problem of estimating climate changes on a local scale from the essentially large-scale results of a GCM.”

Gates (1985)

“One major problem faced in applying GCM projections to regional impact assessments is the coarse spatial scale of the estimates.”

Carter et al. (1994)

But, once we have more regional detail, what difference does it make in any given impacts assessment?

What is the added value?

Do we have more confidence in the more detailed results?

• Agriculture:

*Brown et al., 2000 (Great Plains – U.S.)

Guereña et al., 2001 (Spain)

*Mearns et al., 1998, 1999, 2000, 2001, 2003 (Great Plains, Southeast, and continental US)

*Carbone et al., 2003 (Southeast US)

*Doherty et al., 2003 (Southeast US)

*Tsvetsinskaya et al., 2003 (Southeast U.S.)

*Easterling et al., 2001, 2003 (Great Plains, Southeast)

Thomson et al., 2001 (U.S. Pacific Northwest)

*Pona et al., (in Mearns, 2001) (Italy)

Use of Regional Climate Model Results for Impacts Assessments

Use of Regional Climate Model Results for Impacts Assessments 2

• Water Resources:

Hassell and Associates, 1998 (Australia)

Leung and Wigmosta, 1999 (US Pacific Northwest)

*Stone et al., 2001, 2003 (Missouri River Basin)

Arnell et al., 2003 (South Africa)

Miller et al., 2003 (California)

• Forest Fires:

Wotton et al., 1998 (Canada – Boreal Forest)

Selected RegCM Impacts Studies

• Mearns et al., 1997 - climate scenario formation incorporating changes in daily and interannual climate variability

• Mearns et al., 1999, 2001 - application to corn, wheat, and soybeans in Great Plains

• Pona et al., 2001 – wheat in Italy • Mearns et al., 2003 – multiple crops and

economics in continental US • Stone et al., 2003 - water yield in the

Missouri River basin

Mearns et al., 1999, 2001

-10.7% -10.7 % 2.3 %

Wheat in Italy

Where there is no coarse scale scenario

CCM1

W&M 93

Land-sea mask

G&M 1996

Integrated Assessment of Agriculture in the

Southeastern U. S.

Extension from impacts on crop yields to regional and national

agricultural economics

Special Issue of Climatic Change: Climatic Variability, Change

and Agriculture in the Southeast 1. Mearns, L. O., Introduction to the Special Issue on Climatic Variability, Change

and Agriculture in the Southeast: An overview.

2. Mearns, L. O., F. Giorgi, C. Shields, and L. McDaniel, Climate Scenarios for the Southeast US based on GCM and Regional Model Simulations.

3. Carbone, G., W. Kiechle, C. Locke, L. O. Mearns, and L. McDaniel, Response of Soybeans and Sorghum to Varying Spatial Scales of Climate Change Scenarios in the Southeastern United States.

4. Doherty, R. M., L. O. Mearns, R. J. Reddy, M. Downton, and L. McDaniel, A Sensitivity Study of the Impacts of Climate Change at Differing Spatial Scales on Cotton Production in the SE USA.

5. Tsvetsinskaya, E., L. O. Mearns, T. Mavromatis, W. Gao, L. McDaniel, and M. Downton,The Effect of Spatial Resolution of Climate Change Scenarios on Simulated Corn, Wheat, and Rice Production in the Southeastern United States.

6. Adams, R. M., B. A. McCarl, and L. O. Mearns, The Economic Effects of Spatial Scale of Climate Scenarios: An Example From U. S. Agriculture.

Climate ChangeScenarios

Crop ModelsCERES,

CROPGRO, GOSSYM

Ag Economic Modeling

ASM Assumptions

Direct CO2 Effect

TechnologicalAdaptations

Wheat, Corn, Rice, Sorghum,

Cotton, Soybean

CSIROCoarse

RegCMFine

CoarseScale

FineScale

EconomicImpacts

Schematic of the Southeast Agricultural Project

Crop Yield Change

for rest of US

Climate ChangeScenarios

ModelValidation

CSIROCoarse

RegCMFine

ObservedClimate

Data

Climate Model Simulations and Scenario Formation

Models Employed

• Commonwealth Scientific and Industrial Research Organization (CSIRO) GCM – Mark 2 version• Spectral general circulation model

• Rhomboidal 21 truncation (3.2 x 5.6); 9 vertical levels

• Coupled to mixed layer ocean (50 m)

• 30 years control and doubled CO runs

• NCAR RegCM2• 50 km grid point spacing, 14 vertical levels

• Domain covering southeastern U.S.

• 5 year control run

• 5 year doubled CO runs

Domain of RegCM

denotes study area + denotes RegCM Grid Point (~ 0.5o)X denotes CSIRO Grid Point (3.2 o lat. 5.6 o long)

RegCM Topography (meters)

Contour from 100 to 4000 by 100 (x1)

Southeast domain average seasonal climate changes (2xCO2 versus control) of the CSIRO

and RegCM (5 years each)

Summer

Fall

Maximum TemperatureMinimum Temperature

CSIRO RegCM CSIRO RegCM

5.00 to

6.00

3.00 to

4.00

-1.00 to

0.00

7.00 to

10.00

6.00 to

7.00

2.00 to

3.00

4.00 to

5.00

1.00 to

2.00

0.00 to

1.00

Climate Change - Δ Temperature (oC)

Process of Forming Scenarios on Two Different Spatial Scales

• 36-year observed climatology (max & min temp, precip, solar radiation) 1960-1995 – gridded on a 0.5º grid;

• In the coarse resolution change, monthly changes, ratios from CSIRO climate change (2xCO2 – control) are appended to the observed climatology (i.e., all 0.5º grids falling within a CSIRO grid receive the same changes);

• In the fine resolution change, changes from RegCM2 (thus higher resolution changes – each grid gets unique set of changes).

Crop ModelsCERES,

CROPGRO, GOSSYM

Direct CO2 Effect

TechnologicalAdaptations

Wheat, Corn, Rice, Sorghum,

Cotton, Soybean

ObservedClimate

Data

Schematic of the Southeast Crop Modeling

Crop Model Runs

Models: CERES, CROPGRO, GOSSYM

Crops: corn, cotton, rice, sorghum, soybean, wheat

Best agricultural soil used for each 0.5º grid based on STATSGO database.

All crop models run over entire domain (0.5º grid):• with climate observations 1960-1995, CO2 at 330 ppm;

For coarse and fine scenarios, three cases:• climate change only, CO2 at 330 ppm;• climate change + direct CO2 fertilization effect (540 ppm);• climate change + direct CO2 fertilization effect + adaptations.

Management inputs:• Spatially varied sowing dates and cultivars;• No nitrogen stress;• Dryland and Irrigated.

At What Spatial Scales Do Contrasts in Simulated Crop Yields Matter?

e.g., region – whole Southeast;State – GA, MS, etc.

County – ~ to 50km grid

South East Mean Dryland Yield Comparisons

irrigated (paddy)

* CSIRO and RegCM yields are NOT significantly different (α = 0.05)

CROPSimulatedObserved

(T/ha)

% Change from Base Yield

CSIRO 330CO2

RegCM330CO2

CSIRO540CO2

RegCM 540CO2

CSIRO 540+A

RegCM540+A

Corn 8.1 -13 -16 * _ 0 -2 * +7 +6 *

Cotton 1.2 -4 -17 _ +8 -3 +29 +18 _

Rice 9.6 -16 -19 _ -3 -5 +2 +6

Sorghum 6.0 -36 -51 _ -26 -42 _ -17 -28 _

Soybean 2.4 -49 -69 _ -26 -54 _ -8 -46 _

Wheat 4.5 -36 -32 * -26 -21 * -25 -21 *

Corn - Climate Change Only

Wheat - Climate Change + CO2 Fertilization

Summary of Changes in Crop Yields for the Southeast

• In general, on a state level, changes in crop yields are significantly less negative, or more positive, with the coarse scale climate scenario than the fine scale.

Exception is corn for the south central area, Arkansas for soybeans.

• Wheat shows least contrast in yields with spatial scale.

• Cotton fares best of all crops - largest increases for all three cases.

• Soybean fares poorest - even with adaptation, yields still decrease substantially - more so for fine scale scenario.

• Climate variables that explain the contrasts in climate changed yields based on spatial scale vary based on the crop.

• Adaptation decreases the contrasting effect of the scenario spatial scales in terms of changes in crop yields.

Does regionalization of the climate change scenario matter in terms of economic indicators of the ASM?

Ag Economic Modeling

ASM Assumptions

Crop Yield Change

for rest of US

CoarseScale

FineScale

EconomicImpacts

Schematic of Agricultural Economic Modeling

Overview of Agricultural Sector Model (ASM)

• Represents production and consumption of major U.S. crops and livestock commodities;

• Solved as a spatial equilibrium model;• Maximizes net economic welfare;• Includes processing of agricultural commodities and

foreign trade;• Includes 63 production regions: region defined by

soils, water, and other resource availability;• Effects of climate change in this assessment based

on changes in yields and water use (from CERES and other crop models);

• Has been used in many studies of climate change effects on agriculture.

Crop Modeling for the Rest of the United States

Crop Yield Change

for rest of US

GCM Consumer Surplus

Producer Surplus

Foreign Surplus

Total Surplus

CSIRO 5.96 -3.31 0.40 3.05

RegCM 3.47 -3.41 0.26 0.32

CSIROA 8.94 -3.87 0.62 5.69

RegCMA 7.76 -4.67 0.51 3.61

Changes in Welfare Results in Billion $

Regionalization of the climate change scenarios matters in terms of the economic indicators of the ASM

• Shows up in aggregate economic welfare (different orders of magnitude);

• Regional patterns of agricultural production are altered;- more spatial variability with RegCM;- Southern states are more negatively affected by RegCM.

Conclusions

• Adaptation decreases the contrasting effect of scenario

spatial scale on changes in the net economic effects.

• The contrast in economic net welfare based on spatial scale

of climate scenarios is similar in magnitude to the economic

contrast resulting from use of two very different AOGCM

simulations in the US National Assessment.

Conclusions (Con’t.)

Water Yield Response to Climate Model Scale in the

Missouri River Basin

Study Region

• Missouri River Basin• 2,540 miles long• 529,000 miles2 • 10 US states and 2

Canadian provinces• 75,000 cfs• Highly regulated

SWAT Hydrologic Model

• Models the hydrologic cycle

• Continuous time - daily time step

• Model objective: predict the effect of management decisions on water and sediment yields on large river basins

Models Employed

• Commonwealth Scientific and Industrial Research Organization (CSIRO) GCM – Mark 2 version• Spectral general circulation model

• Rhomboidal 21 truncation (3.2 x 5.6), about 400 km; 9 vertical levels

• Coupled to mixed layer ocean (50 m)

• 30 years control and doubled CO runs

• NCAR RegCM2• 50 km grid point spacing, 14 vertical levels

• Domain covering western two thirds U.S. (Giorgi et al., 1998)

• 5 year control run

• 5 year doubled CO runs

Climate Grids

GCM

RegCM

Climate: July Precipitation

GCM RegCM

Water Yield: 6-Digit Subbasins, 25-Years

GCM from Base RegCM from Base

Conclusions

Scale of climate change model affects

estimates of water yield

Needed Activities1) Longer regional climate model runs (and higher

spatial resolutions).2) Applications to other regions of the world (e.g.,

island nations, tropical regions).3) Application of RCM and driving GCM results to other

impacts models (e.g., human health, natural ecosystems).

4) Quality control of regional climate model output.

5) Inclusion of uncertainty of spatial scale within context of uncertainty of large scale future climates (different emissions scenarios and different GCMs).e.g., 3 emissions scenarios x 3 GCMs x 3 nested regional models and applications to impacts models

Socio-Economic Assumptions

Emissions Scenarios

Concentration Projections

Radiative Forcing Projections

Climate Projections

Global Change Scenarios

Impacts

Sea-Level Projections

Inte

ract

ions

and

Fee

dbac

ks

Land

Use

Cha

nge

Pol

icy

Res

pons

es: A

dapt

atio

n an

d M

itiga

tion

Climate Scenarios

Impacts Models

Regional Climate Scenarios

Natural Perturbations

(I.e.,volcanoes)

Needed Activities (cont.)

6) Conducting regional modeling experiments that further support current evidence that response of regional models to external forcings may be more realistic than that of the GCM providing boundary conditions.

Interdisciplinary Research:

Activities that produce knowledge from integrating over more than one

discipline. True interdisciplinary research involves melding the input of

disciplines into both the design and execution of a unified project.

Integrated Assessment:

Method of analysis that combines results and models from the

physical, biological, economic, and social sciences, and the interactions

between these components in a consistent framework to evaluate the

status and consequences of environmental change.