Transcript ppt - ICTP

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?
Use of Regional Climate Model Results for Impacts
Assessments
• 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 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
-10.7 %
-10.7%
Mearns et al., 1999, 2001
2.3 %
Wheat in Italy
Where there is no coarse scale
scenario
Land-sea mask
CCM1
W&M 93
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.
Schematic of the Southeast Agricultural Project
Direct CO2
Effect
Climate Change
Scenarios
CSIRO
Coarse
RegCM
Fine
Technological
Adaptations
Crop Models
CERES,
CROPGRO,
GOSSYM
Ag Economic
Modeling
ASM
Assumptions
Economic
Impacts
Fine
Scale
Wheat, Corn,
Rice, Sorghum,
Cotton, Soybean
Coarse
Scale
Crop
Yield Change
for rest
of US
Climate Model Simulations and Scenario Formation
Climate Change
Scenarios
CSIRO
Coarse
Model
Validation
RegCM
Fine
Observed
Climate
Data
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)
Climate Change - Δ Temperature (oC)
CSIRO
RegCM
CSIRO
RegCM
Summer
Fall
Minimum Temperature
7.00
to
10.00
6.00
to
7.00
5.00
to
6.00
4.00
to
5.00
Maximum Temperature
3.00
to
4.00
2.00
to
3.00
1.00
to
2.00
0.00
to
1.00
-1.00
to
0.00
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).
Schematic of the Southeast Crop Modeling
Direct CO2
Effect
Technological
Adaptations
Crop Models
CERES,
CROPGRO,
GOSSYM
Wheat, Corn,
Rice, Sorghum,
Cotton, Soybean
Observed
Climate
Data
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
CROP
Simulated
Observed
(T/ha)
% Change from Base Yield
CSIRO
330CO2
RegCM
330CO2
CSIRO
540CO2
RegCM
540CO2
CSIRO
540+A
RegCM
540+A
-2 *
+7
+6 *
+18 _
Corn
8.1
-13
-16 * _
0
Cotton
1.2
-4
-17 _
+8
-3
+29
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 *
 irrigated (paddy)
* CSIRO and RegCM yields are NOT significantly different (α = 0.05)
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?
Schematic of Agricultural Economic Modeling
Ag Economic
Modeling
ASM
Assumptions
Economic
Impacts
Fine
Scale
Coarse
Scale
Crop
Yield Change
for rest
of US
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
Changes in Welfare Results in Billion
$
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
Conclusions
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 (Con’t.)
• 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.
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 Activities
1) 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
Concentration Projections
Climate Projections
Sea-Level Projections
Natural
Perturbations
(I.e.,volcanoes)
Climate Scenarios
Regional Climate
Scenarios
Global Change Scenarios
Impacts Models
Impacts
Land Use Change
Radiative Forcing Projections
Interactions and Feedbacks
Policy Responses: Adaptation and Mitigation
Emissions Scenarios
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.