Upload
margery-nichols
View
226
Download
0
Tags:
Embed Size (px)
Citation preview
Global and SE U.S. Assessment of Precipitation: Comparison of Model Simulations with Reanalysis-based Observations
Eduardo Ponce MojicaPolytechnic University of Puerto Rico
Dr. Auroop R. GangulyComputational Sciences and Engineering Division
August 2009
2 Managed by UT-Battellefor the U.S. Department of Energy
Overview
• Introduction– Climate change– Precipitation
• Objectives
• Resources– Climate models– Climate observations
• Methodology
• Conclusions
• Future work
3 Managed by UT-Battellefor the U.S. Department of Energy
Background
• Climate changes have been a BIG concern for the past decades– Global warming– Climate extremes– Anthropogenic effects
• Processes and materials derived from human activities
• Atmospheric concentration of greenhouse gases
4 Managed by UT-Battellefor the U.S. Department of Energy
Introduction
• Climate changes may cause or worsen precipitation events– Floods– Droughts– Precipitation extremes
• Long-duration
• Short-duration
5 Managed by UT-Battellefor the U.S. Department of Energy
Introduction
• Precipitation is difficult to predict– Too many parameters to take into account
• Ocean circulation
• Land surface
• Sea ice
• Concentration of atmospheric gases
• Electromagnetic radiation
– Complex meteorological physics• Mass and energy transfer
• Radiant exchange
6 Managed by UT-Battellefor the U.S. Department of Energy
Introduction
• Precipitation events may be studied for a specific region, or across the whole Earth
Southeast United States Earth
7 Managed by UT-Battellefor the U.S. Department of Energy
Objectives
• Compare two climate models with observations
• Use statistical analyses to describe models
• Obtain uncertainties from climate model and observations
8 Managed by UT-Battellefor the U.S. Department of Energy
What is …?
• Climate
• Precipitation
9 Managed by UT-Battellefor the U.S. Department of Energy
What is climate?
• Phenomena occurring in the atmosphere in a long period of time– Ranges from months to thousand or million of years
• Composed of numerous meteorological elements– Temperature– Atmospheric pressure– Wind– Rainfall– Evapotranspiration
• Affected by latitude and longitude
10 Managed by UT-Battellefor the U.S. Department of Energy
What is precipitation?
• Products due to condensation of atmospheric water vapour deposited on Earth's surface– Rain– Ice pellets– Snow– Hail
11 Managed by UT-Battellefor the U.S. Department of Energy
Resources
• Climate models simulations
• Climate observations data
• MATLAB– Statistical analysis– Graph global and regional data
• Microsoft Excel– Construction of data plots– Construction of data tables
CCSM3CCSM3HadCM3HadCM3NCEP1NCEP1
12 Managed by UT-Battellefor the U.S. Department of Energy
Climate models
• Community Climate System Model, version 3 (CCSM3)
– United States– United States Department of Energy (DOE) Earth
System Grid (ESG)
• Hadley Centre Coupled Model, version 3 (HadCM3)
– United Kingdom– Intergovernmental Panel on Climate Change (IPCC)
Project for Climate Model Diagnosis and Intercomparison (PCMDI)
13 Managed by UT-Battellefor the U.S. Department of Energy
Climate observations
• National Centers for Environmental Prediction, reanalysis 1 (NCEP1)– United States– National Oceanic and Atmospheric Administration
(NOAA)
14 Managed by UT-Battellefor the U.S. Department of Energy
Methodology
• Interpolate climate models data– Different latitudes and longitudes precision– CCSM3 with NCEP1– HadCM3 with NCEP1
Interpolated model
94 x 192
CCSM3/HadCM3
128 x 256
NCEP1
94 X 192
15 Managed by UT-Battellefor the U.S. Department of Energy
Methodology
• Case study regions– Global– Southeast United States
• Latitudes: 24° N – 41° N• Longitudes: 95° W - 74° W
• Time range (1948 – 1999)
16 Managed by UT-Battellefor the U.S. Department of Energy
Methodology
• Apply statistical methods– Mean– Standard deviation– Skewness– Median– Bias = observations - models
17 Managed by UT-Battellefor the U.S. Department of Energy
Climate graphs
Global mean – NCEP1 and CCSM3
-150 -100 -50 0 50 100 150
-80
-60
-40
-20
0
20
40
60
80
Longitude
Latit
ude
NCEP1 Average Precipitation Rate in mm/s from 1948 to 1999
0
1
2x 10
-4
-150 -100 -50 0 50 100 150
-80
-60
-40
-20
0
20
40
60
80
Longitude
Latit
ude
CCSM3 Average Precipitation Rate in mm/s from 1948 to 1999
0
1
2x 10
-4
NCEP1 average precipitation rate in mm/s from 1948 to 1999
CCSM3 average precipitation rate in mm/s from 1948 to 1999
18 Managed by UT-Battellefor the U.S. Department of Energy
Climate graphs
Global mean – NCEP1 and HadCM3
-150 -100 -50 0 50 100 150
-80
-60
-40
-20
0
20
40
60
80
Longitude
Latit
ude
NCEP1 Average Precipitation Rate in mm/s from 1948 to 1999
0
1
2x 10
-4
-150 -100 -50 0 50 100 150
-80
-60
-40
-20
0
20
40
60
80
Longitude
Latit
ude
HadCM3 Average Precipitation Rate in mm/s from 1948 to 1999
0
1
2x 10
-4
NCEP1 average precipitation rate in mm/s from 1948 to 1999
HadCM3 average precipitation rate in mm/s from 1948 to 1999
19 Managed by UT-Battellefor the U.S. Department of Energy
Climate graphs
SE U.S. mean – NCEP1 and CCSM3
-95 -90 -85 -80 -7524
26
28
30
32
34
36
38
40
Longitude
Latit
ude
NCEP1 Southeastern U.S. Average Precipitation Rate in mm/s from 1948 to 1999
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-4
-95 -90 -85 -80 -7524
26
28
30
32
34
36
38
40
Longitude
Latit
ude
CCSM3 Southeastern U.S. Average Precipitation Rate in mm/s from 1948 to 1999
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-4
NCEP1 Southeastern U.S. average precipitation rate in mm/s
from 1948 to 1999
CCSM3 Southeastern U.S. average precipitation rate in mm/s
from 1948 to 1999
20 Managed by UT-Battellefor the U.S. Department of Energy
Climate graphs
SE U.S. mean – NCEP1 and HadCM3
-95 -90 -85 -80 -7524
26
28
30
32
34
36
38
40
Longitude
Latit
ude
NCEP1 Southeastern U.S. Average Precipitation Rate in mm/s from 1948 to 1999
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-4
-95 -90 -85 -80 -7524
26
28
30
32
34
36
38
40
Longitude
Latit
ude
HadCM3 Southeastern U.S. Average Precipitation Rate in mm/s from 1948 to 1999
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1x 10
-4
NCEP1 Southeastern U.S. average precipitation rate in mm/s
from 1948 to 1999
HadCM3 Southeastern U.S. average precipitation rate in mm/s
from 1948 to 1999
21 Managed by UT-Battellefor the U.S. Department of Energy
Climate graphs
CCSM3 and HadCM3 SE U.S. bias graphs
-95 -90 -85 -80 -7524
26
28
30
32
34
36
38
40
Longitude
Latit
ude
Southeastern U.S. Average of Biased Precipitation Rate in mm/s between the CCSM3 and the NCEP1 from 1948 to 1999
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2x 10
-5
Southeastern U.S. average of biased precipitation rate in mm/s between the
CCSM3 and the NCEP1 from 1948 to 1999
-95 -90 -85 -80 -7524
26
28
30
32
34
36
38
40
Longitude
Latit
ude
Southeastern U.S. Average of Biased Precipitation Rate in mm/s between the CCSM3 and the NCEP1 from 1948 to 1999
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2x 10
-5
Southeastern U.S. average of biased precipitation rate in mm/s between the HadCM3 and the NCEP1 from 1948 to
1999
22 Managed by UT-Battellefor the U.S. Department of Energy
Plots
23 Managed by UT-Battellefor the U.S. Department of Energy
Plots
24 Managed by UT-Battellefor the U.S. Department of Energy
Plots
25 Managed by UT-Battellefor the U.S. Department of Energy
Results
• Global scope– CCSM3 more accurate
• Southeast United States– HadCM3 more accurate
26 Managed by UT-Battellefor the U.S. Department of Energy
Research conclusions
• Global scope– CCSM3 over predicts precipitation rate– HadCM3 over predicts precipitation rate– CCSM3 more accurate model
• Southeast U.S.– CCSM3 under predicts precipitation rate– HadCM3 under predicts precipitation rate– HadCM3 more accurate model
• Study small regions with climate models– Reduces uncertainties– Outputs statistics more accurately
27 Managed by UT-Battellefor the U.S. Department of Energy
Future research
• Test accuracy of CCSM3 and HadCM3 in other regions
• Propose safety measures for high precipitation areas
• Simulate precipitation rates from 2000 to 2100
28 Managed by UT-Battellefor the U.S. Department of Energy
Bibliography
• Auroop R. Ganguly, Shih-Chieh Kao, Karsten Steinhaeuser, Esther S. Parish, Marcia L. Branstetter, David J. Erickson III, and Nagendra Singh. Uncertainties in the Assessments of Climate Change Impacts on Regional Hydrology and Water Resources. (2009: In Review).
• Intergovernmental Panel on Climate Change (IPCC). Fourth Assessment Report: 2007.
29 Managed by UT-Battellefor the U.S. Department of Energy
Acknowledgments
Special thanks go to…
•The Research Alliance in Math and Science program, sponsored by the Office of Advanced Scientific Computing Research, U.S. Department of Energy
•Dr. Auroop R. Ganguly for the opportunity to work on this project.
•Shih-Chieh Kao, Karsten Steinhaeuser, the GIST Group, and Rashida E. Askia for their continued support
•Debbie McCoy, who made provisions for this research experience along with exceptional professional support
30 Managed by UT-Battellefor the U.S. Department of Energy
QUESTIONS