[email protected]© University of Reading 2007rpa Surface radiative fluxes: comparison of...

[email protected] © University of Reading 2007www.nerc-essc.ac.uk/~rpa

Surface radiative fluxes: comparison of NWP/Climate models/reanalyses with

remote sensing estimates

Richard P. Allan

Environmental Systems Science Centre, University of Reading, UK


Earth’s energy balance

Kiehl and Trenberth, 1997; Also IPCC 2007 tech. summary, p.94


Determinants of surface radiation

Field Climatology Diurnal decadal change

Insolation

Cloud

Aerosol

Ozone

Water vapour

Temperature

Water vapour

Cloud

aerosol

GHG

SW

LW


Methods of model surface flux evaluation

NWP/Climate model

Surface flux observations

Physics

Reanalyses

Satellite data

Conventional observations

RT modelsEmpirical models

Other models


Ground Based ObservationsEvaluation of NWP/Climate models

ARM site ARM Model

Barrow ◊ ◊

Lamont + +

Darwin □ □

Manus * *

Peter Henderson et al.

Atm

osp

her

ic e

mis

sivi

ty

Column water vapour (cm)


Bodas et al. (2008) J. Climate (see also e.g., Wild et al. (2001) J Climate, etc)

• Excellent time resolution

• Direct observations

• Scaling up issues• Poor spatial

coverage• Instrumental

uncertainty


Empirical estimates

• Based on physics

• Use surface observations to calibrate– e.g. Prata (1996) QJ Royal Meteorol Soc

Clear-sky surface down longwave

Column integrated water vapour

Screen-level temperature

Atmospheric emissivity


NCEP clear and cloudy surface down longwave and Prata empirical estimate using observed T2m and column integrated water vapour

Niamey, Niger

Empirical formulas are valuable tools in understanding physical processes determining radiative flux variations


• Good quality clear-sky fluxes? Range in estimates of clear-sky surface net longwave radiation…

SRB (82 Wm-2) > NCEP (80 Wm-2) > ERA40 (73 Wm-2) > SSM/I empirical

Reanalyses

Allan (2006) JGR


Robust relationship between clear-sky net surface LW flux (SNLc)

and column water vapour (CWV)

ERA40 NCEP

Allan (2006) JGR

dCWV (mm)

~1.3 Wm-2 mm-1

Clear ~1.5 Wm-2 mm-1

CWV (cm)

S

NL

(W

m-2)

Slingo et al (2008) JGR

Global: reanalyses Sahel, Africa: observations


Interannual/Decadal changes: Homogeneity an issue

• Surface fluxes available globally on model grids• Observational basis through data assimilation• Model/observational errors; require validation• Changes in quality of observing system may

lead to spurious variability

Allan (2007) Tellus


Reanalysis cloud properties unrealistic

Cloud components of surface fluxes poor

ERA40-ISCCP total cloud difference

ERA40 – satellite data (below)

Allan et al. (2004) JGR


Remote sensing of surface fluxes

• Use satellite (and other) retrievals of important parameters (e.g. cloud, T, q)

• Input to radiative transfer codes

• Surface fluxes on model/satellite grids– e.g. ISCCP clouds/reanalysis atmosphere: Zhang et

al. (2004) JGR, Stackhouse et al (GEWEX), Pavlakis et al. (2004) Atmos Chem Phys


Bodas et al. (2008) J. Climate

HadGAM1-Obs: Albedo net SW

Surface Down LW Column Water Vapour


Spurious changes in ISCCP cloudsSurface fluxes: Issues with cloud-overlap, calibration and coverage/angular effects

Norris and Slingo (2008) FIAS


Remote sensing of surface fluxese.g. surface longwave

What the surface sees

Cloud baseColumn water vapour

Tair

IR satellite

Cloud top

Tskin (when clear)

microwave satellites

Humidity temperature (when clear)

Cloud liquid water

precipitation, wind .

Atmospheric temperature / water vapour


• Comparisons of NWP model and satellite estimates of:

Cloud liquid waterWater vapour

• Indirect evaluation of surface fluxes– Parameters important

for surface LW (and SW) radiation

– Allan et al. (2008) QJRMS


Constraining model (based on remote sensing estimates) using surface/satellite observations

Work with:

Nicky Chalmers & Robin Hogan

Mo

del

v G

ER

B/M

SG

Mo

del

v A

RM


RADAGAST/AMMA case study

1200GMT, 8 March 2006

RADAGAST project: http://radagast.nerc-essc.ac.uk


Shortwave fluxes Longwave fluxes

• Diurnal cycle in surface fluxes– Solar/geometry; Temperature response; Atmosphere response

• Daily variability– Advection of air-masses; Aerosol cloud effects

• Important to simulate in models• Important to correct for in remote sensing estimates


Radiative transfer models underestimate the solar absorption in the atmosphere during March 2006 dust storm

Slingo et al. (2006) GRL, 33, L24817


Special issue on RADAGAST

under review for JGR-Atmospheres (A. Slingo et al.)

Using surface observations (and models) improves understanding of physical processes;

An indirect method of model evaluation


Evaluating model climate change responses

CMIP3

CMIP3 volcanic

NCEP ERA40

SSM/I-derived~ +0.7 Wm-2 decade-1

∆SNLc (Wm-2)

Changes in clear-sky surface net longwave flux in coupled climate models, reanalyses and empirical estimates


Linear fit

dSNLc/dTs ~ 3.5±1.5 Wm-2K-1

dCWV/dTs ~ 3.0±1.0 mm K-1

CMIP3 non-volcanic CMIP3 volcanic

Reanalyses/ Obs AMIP3

Models, reanalyses and observations show increased surface net downward longwave with warming due to increased water vapour


Increases in water vapour enhance clear-sky longwave radiative cooling of atmosphere to the surface

This is offset by enhanced absorption of shortwave radiation by water vapour

Changes in greenhouse gases, aerosol and cloud alter this relationship…

Tropical oceans


Sensitivity test: tropical oceans

Clear-sky Longwave shortwave

TOA SFC ATM ATM

1K increase in tropospheric T, constant RH

Greenhouse gas changes from 1980 to 2000 assuming different rates of warming


Conclusions

• Evaluation of surface fluxes in models crucial but problematic (climatology, diurnal cycle, trends)

• Surface observations:– Excellent time-resolution– Upscaling issues, spatial coverage poor

• Reanalyses limitations: clouds/variability• Remote sensing estimates

– Good spatial (and temporal) coverage– Measure accurately quantities important for surface fluxes; need

to consider variety of time-scales

• Analysis of surface/satellite data can help to improve physical processes in models better surface fluxes


Extra Slides


Evaluation of diurnal cycle in NWP model using surface observations

Milton et al. (2008) JGR accepted

Niamey ARM station

(RADAGAST/AMMA)


Diurnal effects: near surface temperature

Night Day

Temperature Temperature

Alti

tude

Alti

tude


Near surface temperature:

diurnal cycle error

Missing physics?


• Diurnal skin temperature effects are also apparent for oceans (clear, calm conditions)

Allan (2000) J.Climate


• Surface downward LW sensitive to moisture changes in lowest levels and temperature changes close to the surface

Sensitivity of surface downwelling LW to temperature and moisture changes in 50 hPa vertical levels

1K temperature increase; moisture increased to conserve Relative Humidity


Window region crucial in determining changes in surface net LW flux

Spectral signatude of clear-sky surface net longwave radiation


Increased moisture enhances atmospheric radiative cooling to surface

ERA40 NCEP

Allan (2006) JGR 111, D22105

SNLc = clear-sky surface net down longwave radiation

CWV = column integrated water vapour

dCWV (mm)

~1.4 Wm-2 mm-1


Evaluation of climate model sensitivity

SNLc = clear-sky surface net down longwave radiation

CWV = column integrated water vapour

dSNLc/dCWV ~ 1 ─ 1.5 W kg-1


• Also true for unique meteorological environments (e.g. Niamey, Radagast project, Slingo et al.)– Here water vapour & temperature anti-correlated over the seasonal cycle

Clear ~1.5 Wm-2 mm-1


Impact of clouds on surface LW radiation

Smaller cloud LW effect in cloudy deep tropics due to water vapour path


Surface cloud LW effect:observations and NWP model

- Higher water path: smaller cloud effect

- More cloud, lower/warmer cloud-base: higher cloud effect



Dimming to brightening simulated in HadGEM1 climate model (Bodas et al.)



Direct evaluation of models using surface observations

Allan (2000) J Climate

Barrow, Alaska








Bodas et al. (2008) J. Climate


SST

Water vapour

Clear net LW down at surface

Testing climate model simulations of current variability (tropical oceans)

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