Observa(on-‐Driven Studies Using GEOS-‐5 Earth System Modeling and
Analysis: Some Examples
Steven Pawson Global Modeling and Assimila(on Office Earth Sciences Division, NASA GSFC
Transla(ng Process Understanding to Improve Climate Models: Princeton, NJ, 10/15-‐16/2015
GMAO: Themes
Weather Analysis and Predic(on
Seasonal-‐to-‐Decadal Analysis and Predic(on
Global Mesoscale Modeling Reanalysis Observing System
Science
• These (non-‐orthogonal) themes decribe GMAO’s main ac(vi(es • GEOS-‐5 is a modular system, encompassing much of the Earth System • All themes include mul(ple components of the Earth System • Strong emphasis on NASA’s Earth Observa(ons
GEOS-‐5: An Overview Modular Earth System Model: • Atmosphere, land, ocean, cryosphere (physics, chemistry, biology) • Aerosols, chemistry, carbon cycle, … • Early adopter of a modular infrastructure (ESMF) • Used at resolu(ons of about 100km to a few km Data Assimila(on: • Atmospheric assimila(on developed jointly with NCEP • Ocean and land are developed in house Observa(ons: • Use a broad range of NASA and non-‐NASA data • Ability to synthesize observing systems from model fields
Sea-‐Ice Predic(on from Seasonal Forecasts GEOS-‐5 Mid-‐May Ensemble: Valid September 2014 GSFC SSMIS – DMSP-‐F17 September 2014
Relevance for this Mee(ng
Data Assimila(on: • confronta(on of the GEOS-‐5 model with observa(ons is the central
theme of GMAO’s work Spa(o-‐temporal structure: • the broad range of (me (hours to decades) and space (few-‐km to
100-‐km grids) demands drives GMAO to use resolu(on-‐aware parameteriza(ons
Example 1: Surface Drag over the Ocean
Surface wind speed versus zo over ocean A change to the func(onal rela(onship between ocean roughness and wind stress was introduced into GEOS-‐5. This was based on oceanic observa(ons (Edson, 2011) implying that the drag in GEOS-‐5 was too weak. Objec(ve was to improve surface wind speeds over the oceans Work led by Andrea Molod
Impact on Simulated Surface Winds Surface Wind Speed m/sec
MODEL
GSSTF
Observa(on-‐derived surface wind speed
Old Model minus Observa(ons New Model minus Observa(ons
The increased surface roughness over oceans leads to a substan(al improvement in surface winds in the DJF season (impact extends into the stratosphere). [Garfinkel et al., 2013)
An Addi(onal Improvement
Surface wind speed versus zo over ocean
Z0 (m
)
Wind Speed (m/sec)
Few observa(ons of surface roughness exist in the vicinity of tropical storms. In GEOS-‐5, the roughness increased with wind speed, reaching unrealis(c values. An upper bound was imposed, following work by Emanuel (1996, 2003), Donelan (2004), and others.
Impact: Typhoon Megi in 2010 was more realis(c with capped drag (Molod, 2012)
GEOS-‐5 AGCM: Resolu(on-‐Aware Cumulus Convec(on Approach adopted by Molod et al. (2015) in GEOS-‐5 builds on the “Stochas(c Tokioka” model by using introducing a limit that depends on resolu(on of the GCM. (See also Lim et al., 2015.)
PDF of Minimum Allowable Entrainment “Stochas(c Tokioka” (Bacmeister and Stephens, 2011) describes the use of a variable Tokioka (1988) parameter, which places a minimum on the entrainment into deep convec(ve clouds. Tallest (non-‐entraining) large mass flux convec(ve events are eliminated, and total cumulus mass flux for deep convec(on is restricted. Values are sampled from a PDF with prescribed parameters).
Impact on resolved/parametrized precipita(on
~25 km ~50 km
~100 km Total Conv+Anvil LargeScale
~200 km
Improved Tropical Cyclones in MERRA-‐2
Even through the spatial resolution of MERRA-2 is not much better than that of MERRA (close to 50km), this plot shows that the hurricane is developed much more realistically in MERRA-2. Largely a consequence of the model improvements. (This is a height section through the core.)
Category-5 Hurricane David (1979) caused widespread damage from the Caribbean to the U.S.. It was virtually undetected in models of the day. It is one of several historical hurricanes in MERRA-2 that is realistically depicted for the first time in a gridded data set.
MERRA MERRA-‐2
Diagnosis led by Oreste Reale
Example 2: The Quasi-‐Biennial Oscilla(on
1990 1995 2000 2005 2010 2015
-40
-20
0
20
40
U (
ms
-1)
Mean Diff (ms-1): -1.01 1.98 Sdev Diff (ms-1): 4.15 8.26 10 hPa (104oE, 1oN)aSingaporeMERRAMERRA-2
1990 1995 2000 2005 2010 2015
-40
-30
-20
-10
0
10
20
U (
ms
-1)
Mean Diff (ms-1): 0.666 0.759 Sdev Diff (ms-1): 2.87 2.19 30 hPa (104oE, 1oN)b
1990 1995 2000 2005 2010 2015
-30
-20
-10
0
10
20
U (
ms
-1)
Mean Diff (ms-1): -1.55 0.158 Sdev Diff (ms-1): 2.68 1.74 50 hPa (104oE, 1oN)c
Singapore (gray shading) MERRA MERRA-‐2
MERRA-‐2 fits the Singapore observa(ons bemer, especially during the westerly phase of the QBO.
Work led by Larry Coy
Zonal wind forcing terms in MERRA
0 1 2 3 4 5Years
100
10
1
Pre
ssu
re (
hP
a)
DUDT GWD (m s-1 month-1)
-8-8
4
4
4
12 12
-2
-2
0 0
0
0
00 0
2 2
a
0 1 2 3 4 5Years
100
10
1
Pre
ssu
re (
hP
a)
DUDT ANA (m s-1 month-1)
-8-8
4
4
4
12 12
0 00
0
0
0 02 2
b
0 1 2 3 4 5Years
100
10
1
Pre
ssu
re (
hP
a)
DUDT DYN (m s-1 month-1)
-8-8
4
4
4
12 12
-2 -2
0
00
0
0
0 0
2 2
c
0 1 2 3 4 5Years
100
10
1
Pre
ssu
re (
hP
a)
DUDT SUM (m s-1 month-1)
-8-8
4
4
4
12 12-2 -2
0 0
0
0
0 0
2
2
2
d
-36 -32 -28 -24 -20 -16 -12 -8 -4 4 8 12 16 20 24 28 32 36
U (m s-1)
Tropical winds in MERRA are forced in roughly equal measure by resolved dynamics, gravity-‐wave drag, and analysis increments Excessive reliance on analysis to force these winds suggests that a forcing term is missing Developments of the GWD scheme (adding an extra wave spectrum in the Tropics) was a part of the development from MERRA to MERRA-‐2.
Zonal wind forcing terms in MERRA-‐2
0 1 2 3 4 5Years
100
10
1
Pre
ssu
re (
hP
a)
DUDT GWD (m s-1 month-1)
-8-8
4
4
4
12
12
-6 -6-4
-4
-4
-2
-2
-2
-2
0 0
0
0
0 0
2
2
2
2
2
4
4
4
6 6
a
0 1 2 3 4 5Years
100
10
1
Pre
ssu
re (
hP
a)
DUDT ANA (m s-1 month-1)
-8-8
4
4
4
12
12
0 0
0
0
0
0
0
0
0
b
0 1 2 3 4 5Years
100
10
1
Pre
ssu
re (
hP
a)
DUDT DYN (m s-1 month-1)
-8-8
4
4
4
12
12
-4 -4
-2 -2
-2 -20 0
0
0
0 0
22
2
2
2 2
2
2
4
4
c
0 1 2 3 4 5Years
100
10
1
Pre
ssu
re (
hP
a)
DUDT SUM (m s-1 month-1)
-8-8
4
4
4
12
12-2
-2
0 0
0
0
0 0
2
2
2
d
-36 -32 -28 -24 -20 -16 -12 -8 -4 4 8 12 16 20 24 28 32 36
U (m s-1)
The impact of these changes to GWD is high The physical forcing (GWD driving) now dominates the QBO wind forcing in MERRA-‐2 Analysis increments play a much smaller role
Coy et al. (2015)
Example 3: Aerosol Analysis in MERRA-‐2
Diagnosis led by Cynthia Randles
Ø Unique amongst its peers, the MERRA-‐2 reanalysis includes an aerosol reanalysis for the modern satellite era.
Ø Constrained by observed aerosol op(cal depth (AOD), MERRA-‐2 simulates major aerosol events (i.e. volcanic erup(ons) as well as the temporal and spa(al variability of major aerosol species.
Sea Salt Dust
Sulfate Carbon
Aircra3 observa6ons of ex6nc6on ver6cal profile
Aircra3 observa6ons of AOD R2 = 0.72
Historical Cruises
[data from A. Smirnov]
Emerging Applica(on of MERRA-‐2 Aerosols to Climate Studies Du
st C
once
ntra
tion
[μg
m-3
]
0
40
80
120
160
200
240
J F M A M J J A S O N D
2006 Daily-Mean Dust Surface Concentration at Barbados
MERRA-2Prospero et al.
R2 = 0.692
240
High correla(on with daily surface dust at Barbados
Similar long-‐term seasonal cycle of surface dust
Saharan Air Layer (SAL) and Aerosol-‐Climate Interac(ons
[Figure from H. Yu]
Zonal decay in AOD from African source to Caribbean matches MODIS C5
AOD zonally-averaged between 0o
- 30o
N
-100 -80 -60 -40 -20 0
longitude
0.0
0.2
0.4
0.6
0.8
1.0
AO
D
MERRA-2 MODIS Aqua
Africa
Carib
bean [Figure from P. Colarco]
OMI AAOD
M2 AAOD
OMI – M2
Aerosol absorp(on effects on radia(on and climate
[Figure from V. Buchard]
Summary GEOS-‐5 modeling work is in(mately linked to observa(ons: • Timescales of hours to decades, includes predic(on of weather
and seasonal-‐to-‐decadal varia(ons • Data assimila(on, including reanalysis, is a core element Three examples of model-‐data combina(ons, with a focus on the new GEOS-‐5 reanalysis, called MERRA-‐2: • Observa(onally derived implementa(on of surface drag over
oceans – beneficial impacts (when care is taken) • Physical forcing of the QBO by gravity wave drag • Emerging capabili(es in aerosol assimila(on – direct effects
included in MERRA-‐2