Arctic: Energy sink of the Earth

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Evaluation of simulated Arctic cloud cover and PBL heights with satellite observations

D. Klaus, K. Dethloff, W. Dorn, A. Rinke, and M. Mielke

(Daniel.Klaus@awi.de)

Alfred Wegener Institute for Polar and Marine Research, Potsdam, Germany

January 16, 2013

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Strong anthropogenic signal (Polar Amplification) and decadal variability Insufficient availability of measurements in polar regions

Global Earth System Models (GESMs) show largest biases in polar regions Arctic regional climate model (RCM) as magnifier (higher resolution) Added value: Development of adapted/improved model physics

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Introduction of AWI Motivation Outline Model description Results from HIRHAM5-SCM Results from HIRHAM5 Summary/Outlook

Arctic: Energy sink of the Earth

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Arctic: Energy sink of the Earth

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Arctic: Energy sink of the Earth

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Helmholtz Association

8 German research centers in the research field ”Earth and Environment”

AWI Alfred Wegener Institute for Polar and Marine Research FZJ Research Center J¨ulich

GEOMAR Helmholtz Center for Ocean Research Kiel KIT Karlsruhe Institute of Technology

GFZ Helmholtz Center Potsdam, German Research Center for Geo-sciences HZG Helmholtz Center Geesthacht, Center for Materials and Coastal Research HMGU Helmholtz Center Munich, German Research Center for Environmental Health

UFZ Helmholtz Center for Environmental Research (Leipzig)

Networking to resolve highly complex environmental and climate problems

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AWI as part of the Helmholtz Association

Helmholtz Association

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Helmholtz Association

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AWI research units

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AWI research unit Potsdam (Telegrafenberg)

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AWI research unit Potsdam (Telegrafenberg)

Research unit Potsdam

. . .started work in 1992

. . .accommodates two sections:

Atmospheric Circulationsand Periglacial Research

. . .employs 105 staff members

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Goal: Integration of atmospheric observations/measurements and model simulations of climate processes into the coupled atmosphere-ocean -cryosphere (permafrost-soil, sea-ice) system

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AWI research section ”Atmospheric Circulations”

Goal: Integration of atmospheric observations/measurements and model simulations of climate processes into the coupled atmosphere-ocean -cryosphere (permafrost-soil, sea-ice) system

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AWI research platforms

Tethered balloon, Lidar, ozone-/radiosonde, etc.

measurements at land stations (e.g. AWIPEV, Svalbard) or drifting sea-ice stations (e.g. NP-35) to reduce polar data gap

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AWI research platforms

Validation of CryoSat sea-ice thickness with EM-Bird on board of the Polar 5 aircraft

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Arctic Antarctic

Himalaya

The ”three poles” of the Earth in our atmospheric RCM simulations In this talk: Focus on the pan-Arctic integration domain

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Polar components of the Earth system at AWI

Arctic Antarctic 1.1: Arctic components of the Earth system

Arctic components of the Earth system at AWI:

The three poles of the Earth in RCM simulations

Himalaya

The ”three poles” of the Earth in our atmospheric RCM simulations In this talk: Focus on the pan-Arctic integration domain

HIRHAM5-SCM

Single‐column Climate Model

Parameterizations (Cloud Scheme)

(e.g. MODIS)

implement improved model physics

Evaluation of HIRHAM5ctrl (with NP‐35, SHEBA, MODIS,

GPS‐RO, CALIOP, … )

Compare HIRHAM5ctrl with HIRHAM5sens

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Outline

1

Model description

Regional climate model HIRHAM5

Single-column climate model HIRHAM5-SCM

2

Results from HIRHAM5-SCM

Modeled vs. observed total cloud cover Parameter sensitivity studies

Modification of the PS-Scheme

3

Results from HIRHAM5

Used observational PBL height datasets Calculation of PBL height in HIRHAM5

Definition of PBL height in observational datasets General performance of HIRHAM5

Shortcomings in satellite PBL heights over land Evaluation of simulated PBL heights I + II

4

Summary/Outlook

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1

Model description

Regional climate model HIRHAM5

Single-column climate model HIRHAM5-SCM

2

Results from HIRHAM5-SCM

Modeled vs. observed total cloud cover Parameter sensitivity studies

Modification of the PS-Scheme

3

Results from HIRHAM5

Used observational PBL height datasets Calculation of PBL height in HIRHAM5

Definition of PBL height in observational datasets General performance of HIRHAM5

Shortcomings in satellite PBL heights over land Evaluation of simulated PBL heights I + II

4

Summary/Outlook

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Outline

1

Model description

Regional climate model HIRHAM5

Single-column climate model HIRHAM5-SCM

2

Results from HIRHAM5-SCM

Modeled vs. observed total cloud cover Parameter sensitivity studies

Modification of the PS-Scheme

3

Results from HIRHAM5

Used observational PBL height datasets Calculation of PBL height in HIRHAM5

Definition of PBL height in observational datasets General performance of HIRHAM5

Shortcomings in satellite PBL heights over land Evaluation of simulated PBL heights I + II

4

Summary/Outlook

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1

Model description

Regional climate model HIRHAM5

Single-column climate model HIRHAM5-SCM

2

Results from HIRHAM5-SCM

Modeled vs. observed total cloud cover Parameter sensitivity studies

Modification of the PS-Scheme

3

Results from HIRHAM5

Used observational PBL height datasets Calculation of PBL height in HIRHAM5

Definition of PBL height in observational datasets General performance of HIRHAM5

Shortcomings in satellite PBL heights over land Evaluation of simulated PBL heights I + II

4

Summary/Outlook

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Regional climate model HIRHAM5

Atmospheric RCM with pan-Arctic integration domain (>53.5^◦N) Comprises dynamical core of the HIRLAM NWP model and physical parameterizations of the ECHAM5 GCM coupled by an interface

HIRLAM (Und´en et al., 2002)

Hydrostatic model solves 7 prognostic equations

Surface pressure (ps) Temperature (T) Horizontal wind (u,v) Specific humidity (q) Cloud water content (ql) Cloud ice content (qi)

0.25^◦horizontal resolution (∼25 km) 40 hybrid levels (≤10 hPa; 10 in PBL) Semi-implicit Euler time scheme (∆t=2 min) ERA-Interim initialization/lateral boundary forcing

ECHAM5 (Roeckner et al., 2003) Subgrid-scale parameterizations:

SW and LW radiation transfer Stratiform cloud scheme Cumulus convection

Surface fluxes and vertical diffusion Sea and sea-ice surface processes Land surface processes Gravity wave drag

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Atmospheric RCM with pan-Arctic integration domain (>53.5N) Comprises dynamical core of the HIRLAM NWP model and physical parameterizations of the ECHAM5 GCM coupled by an interface

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Regional climate model HIRHAM5

Atmospheric RCM with pan-Arctic integration domain (>53.5^◦N) Comprises dynamical core of the HIRLAM NWP model and physical parameterizations of the ECHAM5 GCM coupled by an interface

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dynamical tendency (ERA-Interim)

physical tendency (ECHAM5)

phy dyn tot



 











 







 



 







t t t

i i

i ψ ψ

ψ ^_phy

 







 t

ψi ^dyn



 







 t ψi

102.81°E; 81.40°N

Model setup

Predefined geographic location 60 hybrid levels (≤0.1 hPa; 10 in PBL) Euler forward time scheme (∆t=10 min) Initialization with ERA-Interim data set Physical tendencies explicitly computed by ECHAM5 parameterizations p_sand dynamical tendencies ofT,q,u, andvare prescribed 3-hourly from ERA-Interim

Cloud cover parameterization

Prognostic equations for vapor, liquid, and ice phase

Bulk cloud microphysics according to Lohmann and Roeckner (1996) Relative humidity cloud scheme (RH-Scheme; Sundquist et al., 1989) Prognostic statistical cloud scheme (PS-Scheme; Tompkins, 2002)

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Single-column climate model HIRHAM5-SCM

dynamical tendency (ERA-Interim)

physical tendency (ECHAM5)

phy dyn tot



 











 







 



 







t t t

i i

i ψ ψ

ψ ^_phy

 







 t

ψi ^dyn



 







 t ψi

102.81°E; 81.40°N

Model setup

Predefined geographic location 60 hybrid levels (≤0.1 hPa; 10 in PBL) Euler forward time scheme (∆t=10 min) Initialization with ERA-Interim data set Physical tendencies explicitly computed by ECHAM5 parameterizations p_sand dynamical tendencies ofT,q,u, andvare prescribed 3-hourly from ERA-Interim

Cloud cover parameterization

Prognostic equations for vapor, liquid, and ice phase

Bulk cloud microphysics according to Lohmann and Roeckner (1996) Relative humidity cloud scheme (RH-Scheme; Sundquist et al., 1989) Prognostic statistical cloud scheme (PS-Scheme; Tompkins, 2002)

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Monthly means of C at NP-35 start position (102.81 E; 81.40 N)

20 30 40 50 60 70 80 90 100

Aug07 Sep07 Oct07 Nov07 Dec07 Jan08 Feb08 Mar08 Apr08 May08 Jun08 Jul08 Aug08

Ctot[%]

Month

ERA-Interim MODIS NP-35

MODIS features moderate (high) cloudiness during winter period (summer period) In general, HIRHAM5-SCM agrees qualitatively but systematically overestimatesC^tot PS-Scheme shows reduced biases and good agreement from November 2007 to January 2008 Transition seasons worst reproduced with largest biases in October 2007 and May 2008

Best(worst) agreement between MODIS andHIRHAM5-SCM(PS)(ERA-Interim) but systematic overestimation of cloudiness regardless of whether model or reanalysis

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Modeled vs. observed total cloud cover

Monthly means of C

^tot

at NP-35 start position (102.81

^◦

E; 81.40

^◦

N)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

ERA-Interim MODIS NP-35 HIRHAM5-SCM (PS) HIRHAM5-SCM (RH)

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Monthly means of C at NP-35 start position (102.81 E; 81.40 N)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

ERA-Interim MODIS NP-35 HIRHAM5-SCM (PS) HIRHAM5-SCM (RH) HIRHAM5 (PS)

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Modeled vs. observed total cloud cover

Monthly means of C

^tot

at NP-35 start position (102.81

^◦

E; 81.40

^◦

N)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

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Monthly means of C at NP-35 start position (102.81 E; 81.40 N)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

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Modeled vs. observed total cloud cover

Monthly means of C

^tot

at NP-35 start position (102.81

^◦

E; 81.40

^◦

N)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

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Monthly means of C at NP-35 start position (102.81 E; 81.40 N)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

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Modeled vs. observed total cloud cover

Monthly means of C

^tot

at NP-35 start position (102.81

^◦

E; 81.40

^◦

N)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

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20 30 40 50 60 70 80 90 100

Aug07 Sep07 Oct07 Nov07 Dec07 Jan08 Feb08 Mar08 Apr08 May08 Jun08 Jul08 Aug08 Ctot[%]

Month

MODIS 2.0 1.5

(a) Lower ˜q0(˜q^def₀ =2)

20 30 40 50 60 70 80 90 100

Month

MODIS 0.1 mg/kg 1 mg/kg

(b) Higher CWmin(CW_min^def=0.1 mg kg⁻¹)

20 30 40 50 60 70 80 90 100

Month

MODIS 15 100

(c) Higherγ1(γ^def₁ =15)

20 30 40 50 60 70 80 90 100

Month

MODIS 0.5 mg/kg 0.05 mg/kg

(d) Lowerγthr(γ^def_thr=0.5 mg kg⁻¹)

20 30 40 50 60 70 80 90 100

Month

MODIS p = q0 = 2 p = F(q) = (q+1)/(q-1)

20 30 40 50 60 70 80 90 100

Month

MODIS p = q0 = 2 p = F(q), gthr=0.05mgkg-1

Suitable tuning parameters

˜

q₀−Shape parameter threshold

Controls the shape of the symmetric beta distribution acting as probability density function (PDF)

CWmin−Cloud water threshold

Avoids negative cloud water/ice contents and controls the occurrence of clear-sky conditions in the PS-Scheme

γ1−Autoconversion rate

Controls the efficiency of rain drop formation by collision and coalescence

γthr−Cloud ice threshold

Controls the efficiency of the Bergeron-Findeisen process

Reduction ofC^totthroughhigherCWminorγ1as well aslower˜q₀orγthr

Most significant improvement through lowerγthrthat also correct the ratio of liquid to solid water content

Klaus et al. (2012): Evaluation of Two Cloud Parameterizations and Their Possible Adaptation to Arctic Climate Conditions,Atmosphere2012, 3, 419 – 450.

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Parameter sensitivity studies

Monthly means of C

^tot

at NP-35 start position

20 30 40 50 60 70 80 90 100

Month

MODIS 2.0 1.5

20 30 40 50 60 70 80 90 100

Month

MODIS 0.1 mg/kg 1 mg/kg

20 30 40 50 60 70 80 90 100

Month

MODIS 15 100

20 30 40 50 60 70 80 90 100

Month

MODIS 0.5 mg/kg 0.05 mg/kg

20 30 40 50 60 70 80 90 100

Month

MODIS p = q0 = 2 p = F(q) = (q+1)/(q-1)

20 30 40 50 60 70 80 90 100

Month

˜

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20 30 40 50 60 70 80 90 100

Month

MODIS 2.0 1.5

20 30 40 50 60 70 80 90 100

Month

MODIS 0.1 mg/kg 1 mg/kg

20 30 40 50 60 70 80 90 100

Month

MODIS 15 100

20 30 40 50 60 70 80 90 100

Month

MODIS 0.5 mg/kg 0.05 mg/kg

20 30 40 50 60 70 80 90 100

Month

MODIS p = q0 = 2 p = F(q) = (q+1)/(q-1)

20 30 40 50 60 70 80 90 100

Month

˜

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Parameter sensitivity studies

Monthly means of C

^tot

at NP-35 start position

20 30 40 50 60 70 80 90 100

Month

MODIS 2.0 1.5

20 30 40 50 60 70 80 90 100

Month

MODIS 0.1 mg/kg 1 mg/kg

20 30 40 50 60 70 80 90 100

Month

MODIS 15 100

20 30 40 50 60 70 80 90 100

Month

MODIS 0.5 mg/kg 0.05 mg/kg

20 30 40 50 60 70 80 90 100

Month

MODIS p = q0 = 2 p = F(q) = (q+1)/(q-1)

20 30 40 50 60 70 80 90 100

Month

˜

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Default formulation Tompkins (2002)

˜

p= ˜q₀=2 (˜q≥˜p) positively skewed or symmetricalG(qt)

(PS-Schema;

) ergibt





_q^b

q q C

s

G(

t

) d

t

G(qt)

q_t qsqt

a b

q_t G(qt)

b – a qs

G(qt)

q_t

ࢗ෥> ࢖෥

G(qt)

q_t qt

ࢗ෥< ࢖෥ Changed formulation

Tompkins’ idea

˜

p=F(˜q) =^˜^q+1_˜_q−1 now negatively skewed G(q_t)permitted, too

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Modification of the PS-Scheme

˜

Prognostisch-Statistisches Schema

(PS-Schema;

Tompkins, 2002)

t

) d

t

G(qt)

q_t qsqt

a b

q_t G(qt)

b – a qs

G(qt)

q_t

ࢗ෥> ࢖෥

G(qt)

q_t qt

Tompkins’ idea

˜

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Modification of the PS-Scheme

˜

Prognostisch-Statistisches Schema

(PS-Schema;

Tompkins, 2002)

t

) d

t

G(qt)

q_t qsqt

a b

q_t G(qt)

b – a qs

G(qt)

q_t

ࢗ෥> ࢖෥

G(qt)

q_t qt

Tompkins’ idea

˜

Monthly means of C

^tot

at NP-35 start position

20 30 40 50

Ctot

Month

MODIS 2.0 1.5

(a) Lower ˜q0(˜q^def0 =2)

20 30 40 50

Cto

Month

MODIS 0.1 mg/kg 1 mg/kg

(b) Higher CWmin(CWmin^def=0.1 mg kg⁻¹)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

MODIS 15 100

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

MODIS 0.5 mg/kg 0.05 mg/kg

(d) Lowerγthr(γ_thr^def=0.5 mg kg⁻¹)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

MODIS p = q0 = 2 p = F(q) = (q+1)/(q-1)

(e) Permit negative skewness, i. e. ˜p=F(˜q)

20 30 40 50 60 70 80 90 100

Ctot[%]

Month

(f) Lowerγthrand negative skewness

Reduction of clouds through the introduction of negatively skewed beta distributions is of the same order of magnitude as for lowerγthr