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