Radiation in ACME To explore issues ACME is facing associated

with radiation, and possible improvements

Philip Cameron-Smith,

ACME meeting, Summer 2017 (Bolger Center)

LLNL-PRES-708641 Prepared by LLNL under Contract DE-AC52-07NA27344.

Radiation affects Atm, Land, Ocn, Ice

Radiation is still uncertain

Key points:

High-resolution surface radiation, temperature, precipitation, humidity, winds are likely to be important drivers of surface processes, especially in the Arctic.

Topography and microtopography controls on surface radiation budgets have large potential impacts in permafrost landscapes (next slide).

(Fig 6.20, IPCC AR5, WG I, 2013)

CO2 feedbacks

Climate feedbacks

Permafrost and

wetlands feedbacks

3

What does “high-resolution ESM gridcell” mean for the Arctic?

Image credits: Jiafu Mao, Salil Mahajan, Michele Thornton

Observations illustrate

interactions among terrain,

vegetation distribution, and snow.

Surface radiation plays an

important role in these

interactions.

Terrain and Microtopography

VegetationSnow

Surface Hydrology

Subsurface Thermal-Hydrology

Soil Biogeochemistry

Detailed studies in several Arctic tundra watersheds on Alaska’s Seward Peninsula (NGEE-Arctic)

Teller watershed: 2.3 km2 HUC-12 containing the Teller watershed: 140 km2

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Impact of surface heterogeneities on land surfacefluxes and states in simulations using an

uncoupled, hyper-resolution land surface model

Gautam Bisht and William Riley

Earth & Environmental Sciences Division,Lawrence Berkeley National Laboratory

June 2, 2017

2/9

Research objectives

Local surface topographic features (e.g. slope, aspect), as well as,non-local topographic features (e.g. terrain shading, sky viewfactor), impacts total amount of solar radiation reaching theEarth’s surface. Yet, the ACME land model assumes a flat Earthwith an unobstructed view of sky.

1. How does surfaceheterogeneities due tosoils, vegetation cover, andtopography impactcoarse-scale surface fluxesand states?

2. What is the relative impactof various sources ofsurface heterogeneities oncoase-scale surface fluxesand states?

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Methodology

I Sources of heterogeneities

1. Soils (POLARIS30)

2. PFT (MODIS)

3. Surface elevation (GTOP30)

I ALM is modified to account for effects of topography (slope andaspect) on downwelling solar radiation

I Surface dataset created at 1km horizontal resolution

I Each watershed was driven by 1× 1 CRUC forcing dataset

I Simulation length was 20-years

I Surface dataset contained 100% naturally vegetated land

I Watersheds

1. Rio Grand headwaters watershed, CO

2. Snake headwater watershed, Wyoming

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Methodology (continued)

I Following set of ALM simulations were performed:

Code Soil PFT Surface elevation

UUF Uniform Uniform FlatVUF Variable Uniform FlatUVF Uniform Variable FlatVVF Variable Uariable FlatUUT Uniform Uniform TopographyVUT Vniform Uniform TopographyUVT Uariable Variable TopographyVVT Variable Variable Topography

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Rio Grande Headwaters

6/9

Rio Grande Headwaters: Monthly R↓shortwave

J F M A M J J A S O N D0

100

200

300

400[W

m−2

]

Rsdown: Mean

UUFVVT

J F M A M J J A S O N D0

5

10

15

20

25

30

[Wm

−2]

Rsdown: Standard dev

UUFVVT

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Rio Grande Headwaters: Monthly sensible heat flux

J F M A M J J A S O N D

0

50

100

150

200[W

m−2

]

SH: Mean

UUFVVT

J F M A M J J A S O N D0

10

20

30

40

50

[Wm

−2]

SH: Standard dev

UUFVVT

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Rio Grande Headwaters: Monthly latent heat flux

J F M A M J J A S O N D0

20

40

60

80

100[W

m−2

]

LH: Mean

UUFVVT

J F M A M J J A S O N D0

5

10

15

20

[Wm

−2]

LH: Standard dev

UUFVVT

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Conclusion

I Surface heterogeneities have negligible impact on domainaverage fluxes and states.

I Surface heterogeneities lead to spatial variability in simulatedfluxes and states.

Conclusion

• Sub-gridscale Land heterogeneity will affect

climate response.

• Correlated land-use with snow affects the

mean response.