Module 17 MM5: Climate Simulation BREAK. Regional Climate Simulation for the Pan-Arctic using MM5...
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Transcript of Module 17 MM5: Climate Simulation BREAK. Regional Climate Simulation for the Pan-Arctic using MM5...
Module 17 Module 17 MM5: Climate SimulationMM5: Climate Simulation
BREAKBREAK
Regional Climate Simulation for the Pan-Arctic
using MM5
William J. Gutowski, Jr., Helin Wei, Charles Vörösmarty, Balazs Fekete
& Steven Frolking
Iowa State UniversityUniversity of New Hampshire
Regional Climate Simulation for the Pan-Arctic
using MM5
FocusLand-Atmosphere Coupling
in Pan-Arctic Hydrologic Cycle
NCAR/Penn State Non-hydrostatic MM5 (V2) • Grell cumulus convection• Mixed-phase microphysics• CCM2 radiation• Blackadar high resolution PBL
NCAR Land Surface Model (LSM)
Simple Thermodynamic Sea Ice Model
Model
•Period: 1 Oct. 1985 - 30 Sep. 1986
•Sensitivity runs: Oct 85 or July 86
•Computation: DOE/Ames ALICE (32-nodes) NCAR C90 (4-nodes) [2.5 cpu-hr/month] NCAR Blackforest
Model domain(grid: 51 x 91; 120 km)
Historical Arctic Rawinsonde Archive (HARA) NCEP reanalysis (upper air) TOVS
sfc. (skin) temperaturevertical temp profilePBL stratification
Xie-Arkin precipitation Polar Radiation Flux Project (PRF) The Arctic 2-Meter Air Temperature data set
Observational comparisons
Asian Arctic Watershed
North American Arctic Watershed
European Arctic Watershed
Analysis Sectors
= selected HARA sites
Central Arctic Ocean
Cloud calibration(Oct. 1985 & July 1986)
Experiment CC algorithm Result
(non-convective)
1 RH > 75% (MM5 std.) too much
2 RH > 98% too much
3 CLW > 10-2 kg/m3 too littleCIW > 5.10-3 kg/m3
=> CC=75%
4 CLW > 10-2 kg/m3 ~ OKCIW > 5.10-3 kg/m3
=> CC=90%
Surface incident solar radiation[W/m2]
Polar Rad. Flux Obs. MM5/LSM
Central Arctic Ocean
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep
MM5/LSMPRF
European Arctic Watershed
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep
MM5/LSMPRF
North American Arctic Wateshed
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep
MM5/LSMPRF
Asian Arctic Watershed
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep
MM5/LSMPRF
0
10
20
30
40
50
60
Asia N. America Europe
1
2
3
4
OBS
Region
Cloud calibration - Precipitation simulation(July 1986)
0
10
20
30
40
50
60
Asia N. America Europe
2 4 OBS
Region
Cloud calibration - Precipitation simulation(Oct. 1985)
500 hPa Heights(Dec85 - Jan86 - Feb86)
NCEP MM5
500 hPa Heights(Mar - Apr - May 86)
NCEP MM5
500 hPa Heights(Jun - Jul - Aug 86)
NCEP MM5
500 hPa Heights(Oct85 - Nov85 - Sep86)
NCEP MM5
latitude
45N 65N 85N
RMS Differences (NCEP - simulation)
(a) Z(500 hPa)
(b) u(850 hPa)
(c) v(850 hPa)
(a)
45N 65N 85N
45N 65N 85N
OCTJANAPRJUL
12
10
8
[m/s] 6
4
2
0
(b)
(c)
12
10
8
[m/s] 6
4
2
0
200
150
[m] 100
50
0
500 hPa Heights: RMS Difference vs. Time(MM5-NCEP)
[m]
150
100
50
01 OCT 1 NOV
MYSZHELANIA
0
5
10
15
Oct Jan Apr Jul
MM5/LSMNCEP
MYSZHELANIA
0
5
10
15
Oct Jan Apr Jul
850 hPa Wind: RMS Difference vs. HARA
MM5/LSM NCEP
U component V component
850 hPa Wind: RMS Difference vs. HARA
MM5/LSM NCEP
U component V componentTUKVKHANSK
0
5
10
15
Oct Jan Apr Jul
MM5/LSMNCEP
TUKVKHANSK
0
5
10
15
Oct Jan Apr Jul
Obs.MM5/LSM
Asian Arctic Watershed
240
260
280
300
MM5/LSMOBS
North American Arctic Watershed
240
260
280
300
MM5/LSMOBS
European Arctic Watershed
240
260
280
300
MM5/LSMOBS
2-Meter Air Temperature
Obs. MM5/LSM
Stratification Parameter: (950 hPa) - (900 hPa)
Precipitatble Water: MM5 vs.
NCEP/NCAR Reanalysis &
Sat. Obs.
Precipitation: MM5 vs. Xie-Arkin
Global, Composite Runoff
0.5˚ climatology Composite based on
• Observed river discharge
• 0.5˚ river network (STN-30p)
• Climatology-driven water balance model
River Networking & Runoff
Mackenzie River
Global, Composite Runoff
Global, Composite Runoff
Hudson Hope Gauging Station
1. Water Balance Model• driven by climatological precip. & temp.• computes runoff in 0.5˚ grids
2. Runoff vs. discharge
3. Correct runoff in 0.5˚ grids by discharge
Surface Runoff [mm/month]______________Post-processing assumption:No infiltration over frozen soil
AAW
0
20
40
60
80
Oct Dec Feb Apr Jun Aug
UNH-CLIMMM5/LSM
NAAW
0
20
4060
80
100
Oct Dec Feb Apr Jun Aug
UNH-CLIMMM5/LSM
EAW
020406080
100120140
Oct Dec Feb Apr Jun Aug
UNH-CLIMMM5/LSM UNH-CLIM
MM5/LSM
What is error vs. region size?
Averaging Grids:Compute average error variance for...
What is error vs. region size?
Averaging Grids:Compute average error variance for grids ...
What is error vs. region size?
Averaging Grids:Compute average error variance for grids of different spacings
What is error vs. region size?
Average RMS difference scaled by amplitude of field’s annual cycle
Module 17 Module 17 MM5: Climate SimulationMM5: Climate Simulation
Examples of MM5 climate simulation:Examples of MM5 climate simulation: North AmericaNorth America
- good near-surface simulation- good near-surface simulation- shortcomings of narrow boundary- shortcomings of narrow boundary zonezone- generic errors in precipitation bias- generic errors in precipitation bias- shortcomings of gridded observations- shortcomings of gridded observations
ArcticArctic- benefits of variety of observations- benefits of variety of observations- importance of cloud cover to all simulation- importance of cloud cover to all simulation- shortcomings of reanalyses- shortcomings of reanalyses- scale-dependence of errors- scale-dependence of errors