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COSMO-CLM-ART Training, Langen, Germany, 23 – 31 March 2015
Parameterization of Lakes in NWP and Climate Models
Dmitrii V. Mironov
German Weather Service, Offenbach am Main, Germany
Hermann Asensio, Erdmann Heise, Ekaterina Machulskaya, Bodo
Ritter(German Weather Service, Offenbach am Main, Germany) Sergey
Golosov(Institute for Lake Research, Russian Academy of Sciences,
St. Petersburg, Russia) Georgy Kirillin (Leibniz Institute of
Freshwater Ecology and Inland Fisheries, Berlin, Germany) Ekaterina
Kourzeneva(Finnish Meteorological Institute, Helsinki, Finland, and
Russian State Hydrometeorological University, St. Petersburg,
Russia) Arkady Terzhevik(Northern Water Problems Institute, Russian
Academy of Sciences, Petrozavodsk, Russia)
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Outline
• Parameterization of lakes in NWP and climate models – the
problem
• The lake parameterization scheme “FLake” • FLake in the COSMO
model • COSMO-FLake performance • Conclusions and outlook
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Parameterization of Lakes in NWP and Climate Models A Twofold
Problem
(1a) The interaction of the atmosphere with the underlying
surface is stronglydependent on thesurface temperature and its
time-rate-of-change. (Most) NWPmodels assume that the water surface
temperature can be keptconstant overthe entire forecast period. The
assumption is doubtful for small-to-medium sizerelatively shallow
lakes, where the diurnal variations of the surfacetemperature reach
several degrees. A large number of such lakes will
becomeresolved-scale features as the horizontal resolution is
increased.
(1b) Apart from forecasting the lake surface temperature,
itsinitialization isalso an issue.
(2) Lakes strongly modify thestructure and the transport
properties of theatmospheric surface layer. A major outstanding
question is theparameterization of the water-surface roughness with
respect to wind (e.g.limited fetch) and to scalar quantities.
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Lake Regions: Finland, Karelia
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Lake Regions: Khanty-Mansiisk region
(middle Ob’ river)
Lake Regions: Canada
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Lake Parameterization Schemes for NWP and Climate Models
(e.g. Croley 1989, 1992, Croley and Assel 1994, Hostetler and
Bartlein 1990, Hostetler 1991, Hostetler et al. 1993, 1994,
Barrette and Laprise 2005, Bates et al. 1993, 1995, Ljungemir et
al. 1996, Goyette et al. 2000, Tsuang et al. 2001, Song et al.
2004, León et al. 2005, 2007, Long et al. 2007, Mackay 2005,
Mackay et al. 2009, Stepanenko and Lykosov 2005, Stepanenko 2007,
Stepanenko et al. 2010, Subin et al. 2012)
• One-layer models, complete mixing down to the bottom Neglect
stratification ⇒ large errors in the surface temperature
• Turbulence closure models, multi-layer (finite-difference)
Describe the lake thermocline better ⇒ expensive
computationally
A compromise betweenphysical realism and computational
economy
is required
A two layer-model with a parameterized vertical temperature
structure
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The Concept
Put forward by Kitaigorodskii and Miropolsky (1970) to
describethe temperature structure of the oceanic seasonal
thermocline.The essence of the concept is that the temperature
profile in thethermocline can be fairly accurately parameterized
through a“universal” function of dimensionless depth, using
thetemperature difference across the thermocline, ∆θ=θs-θb,
anditsthickness, ∆h, asappropriate scalesof temperature and
depth:
.)(
)(),(
)(
),()(
th
thz
t
tzts∆−==
∆− ςςϑθ
θθ
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Analogy with the Mixed -Layer ConceptUsing θs(t) and h(t) as
appropriate scalesof temperature and depth, thetemperature profile
in the upper mixed layer is represented as
.)(
),()(
),(
th
z
t
tz
s
=Φ= ξξθ
θ
Since the layer is well mixed, the “universal” functionΦ(ξ) is
simply a constant equal to1.Then, integrating the heat transfer
equation (partial differential equation inz, t)
z
w
t ∂′′∂−=
∂∂ θθ
over z from 0 to h(t), reduces the problem to the solution of
anordinarydifferential equationfor θs(t),
h
hQQ
dt
d ss )(−=θ
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The Lake Model “FLake”
• the mean temperature of the water column,• the surface
temperature,• the bottom temperature,• the mixed-layer depth,• the
shape factor with respect to the temperature profile inthe
thermocline,• the depth within bottom sediments penetrated by the
thermal wave, and• the temperature at that depth.
The model is based on the idea of self-similarity (assumed
shape) of the evolvingtemperature profile. That is, instead of
solvingpartial differential equations(in z, t) forthe temperature
and turbulence quantities (e.g. TKE), the problems is reduced
tosolving ordinary differential equationsfor
time-dependentparameters (variables) thatspecify the temperature
profile. These are (optional, modules can be switched off)
In case of ice-covered lake, additional prognostic variables are
• the ice depth, • the temperature at the ice upper surface, • the
snow depth, and the temperature at the snow upper surface.
Important! The model does not require (re-)tuning.
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(a) The evolving temperature profile is characterised by several
time-dependent variables, namely, thetemperatureθs(t) of the mixed
layer, its depthh(t), the bottom temperatureθb(t), and
thetemperature-profile shape factorCθ(t). Optionally, the depthH(t)
within bottom sedimentspenetrated by the thermal wave and the
temperatureθH(t) at that depth can be computed.
θs(t)θb(t)
(a)
θL
θH
(t)
h(t)
D
L
H(t)
Cθ(t)
Schematic representation of the evolving temperatur e
profile
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(b) For ice-covered lakes, additional variables are the
temperatureθI(t) at theice upper surface and the ice
thicknessHI(t), and (optionally) thetemperatureθS(t) at the snow
upper surface and the snow thicknessHS(t).
θs(t) θb(t)θI(t)θS(t)
(b)θ
L
θH
(t)
h(t)
D
L
H(t)
-HI(t)
-HI(t)-H
S(t)
Snow
Ice
Water
Sediment
Cθ(t)
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FLake in NWP and Climate Models: External Parameters
• geographical latitude(easy)
• lake fractionof the NWP model grid-box (not so easy)
• lake depth(not easy at all, lack of data, etc.)
• typicalwind fetch
• optical characteristics of lake water(extinction coefficients
with
respect to solar radiation)
• depth of the thermally active layer of bottomsediments,
temperature
at that depth(cf. soil model parameters)
Default values of thelast fourparameters can be used.
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Lake Fraction
Lake-fraction external-parameter field for the COSMO-EU
numerical domain of the COSMO model (ca. 7 km horizontal mesh
size).
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Lake Depth
Lake depths for “Northern Europe” region of the COSMO-EU
numerical domain (ca. 7 km horizontal mesh size).
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Lake Depth
Lake depths based on COSMO-DE external-parameter field (ca. 2.8
km horizontal mesh size).
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FLake in the COSMO Model: Configuration
• bottom sediment module is switched off (heat flux through the
water-
bottomsediment interface is zero)
• snow above the lake ice is not considered explicitly, the
effect of snow is
accounted for implicitly through the temperature dependence of
the ice
surface albedo (Mironov and Ritter 2003, 2004, Mironov et al.
2012)
• turbulent fluxes at the surface are computed with the current
COSMO-
model surface-layer scheme (Raschendorfer 2001); optionally, the
new
surface-layer scheme (Mironov et al. 2003) can be used
• no tile approach: lakes are the COSMO-model grid-boxes
with
FR_LAKE>0.5, otherwise land or sea water
• 2D fields of lake fraction and of lake depth based on data
(Kourzeneva
2009, 2010, Kourzeneva et al. 2012), default values of other
lake-specific
parameters
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FLake in the COSMO Model: Parallel Experiment
• COSMO-model parallel experiment over one year, 1 January
through 31 December 2006, using the LM1 numerical
domain of DWD
• The entire COSMO-model data assimilation cycle except
that the lake surface temperature is not re-initialised
through
the SST analysis but is predicted by FLake
• “artificial” initial conditions at the cold start, where the
lake
surface temperature is equal to the COSMO-model SST
from the analysis
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FLake in COSMO: Results from Parallel Experiment 56 32
1 January – 31 December 2006
Lake Hjälmaren, Sweden (mean depth = 6.1 m)• Black – lake
surface temperature from the COSMO SST analysis • Green– lake
surface temperature computed with FLake
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FLake in COSMO: Results from Parallel Experiment 56 32
1 January – 31 December 2006
Lake Balaton, Hungary (mean depth = 3.3 m)• Black – lake surface
temperature from the COSMO SST analysis • Green– lake surface
temperature computed with FLake
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FLake in COSMO: Results from Parallel Experiment 56 32
1 January – 31 December 2006
Lake Balaton, Hungary (mean depth = 3.3 m). Ice thickness
computed with COSMO-FLake.
Ice melting: very beginning of March
Freeze-up: 10 January
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FLake in COSMO: Results from Parallel Experiment 56 32
1 January – 31 December 2006
Lough Neagth, UK (mean depth = 8.9 m)• Black – lake surface
temperature from the COSMO SST analysis • Green– lake surface
temperature computed with FLake
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FLake within COSMO -EU/DE of DWD
Flake is used operationally at DWDsince 15 December 2010within
COSMO -EU (ca. 7 km horizontal mesh size), andsince 18 April 2012
within COSMO-DE (ca. 2.8 km meshsize).
• Results of testing of FLake within COSMOare neutral to
slightlypositive.
• Verification against observational data indicate an
improvement ofsome scores such as 2m-temperature in regions where
many lakesare present (e.g. Scandinavia).
• The use of FLake allows to avoid some unwanted situations,
e.g. anartificial cold air outbreak. This may occur in winter when
a lakethat is frozen in reality (lowsurface temperature) is treated
as openwater (high surface temperature) within COSMOdue to
theshortcomings of water surface temperature analysis scheme.
The performance of FLake within COSMO-EU/DE is monitored.
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Conclusions and Outlook• FLake is implemented into the COSMO
model (setllake=.FALSE. to run
without FLake)
• Since 15.12.2010/18.04.2012 Flake is operational at DWD within
COSMO-EU/DE (ca. 7 kmand ca. 2.8 kmmesh size, respectively)
• Documentation and synopsis of FLake routines are ready
• FLake is operational at DWD within global NWP model ICON since
20January 2015; tiled surface scheme is currently used in
ICON(effect of sub-grid scale lakes is accounted for)
• Monitor operational results
• Update external-parameter fields
• Account for SGS lakes within COSMO (tiled surface scheme, c/o
EkaterinaMachulskaya and Jürgen Helmert)
• Long termprospective: explicit treatment of snow over lake
ice, three-layertemperature profile, salinity, data on optical
propertiesof lake water
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External parameters and tiled surface scheme
The lake-fraction external-parameter field based on the
lake-depth data fromKourzeneva (2010) and GlobCover physiographic
data. The horizontal size of theCOSMO-model grid is ca. 7 km.
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InfoFLake Web Pagehttp://lakemodel.net(mirror
http://nwpi.krc.karelia.ru/flake),
c/o Georgiy Kirillin and Arkady Terzhevik
Online FLake versionat http://lakemodel.net(take a look and have
fun!)
References
Kirillin, G., J. Hochschild, D.Mironov, A. Terzhevik, S.Golosov,
and G. Nützmann, 2011: FLake-Global: Onlinelake model with
worldwide coverage.Environ. Modell. Softw., 26, 683-684.
Kourzeneva, E., 2010: External data for lake parameterization in
Numerical Weather Prediction and climatemodeling.Boreal Env. Res.,
15, 165-177.
Kourzeneva, E., H. Asensio, E. Martin, and S. Faroux, 2012:
Global gridded dataset of lake coverage and lakedepth for use in
numerical weather prediction and climate modelling. Tellus A, 64,
15640.doi:10.3402/tellusa.v64i0.15640
Mironov, D. V., 2008: Parameterization of lakes in numerical
weather prediction. Description of a lake model.COSMO Technical
Report, No. 11, Deutscher Wetterdienst, Offenbach am Main, Germany,
41 pp.
Mironov, D., E. Heise, E. Kourzeneva, B. Ritter, N. Schneider,
and A. Terzhevik, 2010: Implementation of thelake parameterisation
scheme FLake into the numerical weather prediction model
COSMO.Boreal Env. Res.,15, 218-230.
Mironov, D., B. Ritter, J.-P. Schulz, M. Buchhold, M. Lange,and
E. Machulskaya, 2012: Parameterization of seaand lake ice in
numerical weather prediction modelsof the German Weather Service.
Accepted for publicationin Tellus A.
Further references athttp://lakemodel.net
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FLake Applications
• Lake parameterization scheme for NWP and climate models
(computationally efficient, can be used to treat a large number
of
lakes)
• Single-column lake model in a stand-alone mode (assessment
of
response of lakes to climate variability, estimation of
evaporation
from the water surface, aid in design of ponds and reservoirs,
etc., a
cost-effective decision-making tool)
• Physical module in models of lake ecosystems (a
sophisticated
physical module is not requiredbecause of large uncertainties
in
chemistry and biology)
• Educational tool (simple but incorporates much of the
essential
physics)
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FLake in NWP and Climate Models
As a lake parameterization scheme, FLake is
• implemented, or on the way, into a number of NWP and
climate models (COSMO, ICON, HIRLAM, UK Met Office
Unified Model, NWP model suite of Meteo France, ECMWF
IFS, CLM, RCA, Canadian Regional Climate Model),
• used as a lake parameterization module in the surface
schemes TESSEL, SURFEX, and JULES,
• used operationally at the German Weather Service within
COSMO-UE/DE and ICON, used operationally at the Finish
Meteorological Institute within HIRLAM
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Thank you for your attention!
Acknowledgements: Frank Beyrich, Michael Buchhold, Ulrich
Damrath, Günther Doms, JochenFörstner, Helmut Frank, Thomas
Hanisch, Jürgen Helmert, Peter Meyring, Van Tan Nguyen,
UlrichSchättler, Christoph Schraff (DWD), Andrey Martynov (UQAM),
Burkhardt Rockel (GKSS), PatrickSamuelsson (SMHI), Laura Rontu
(FMI), Zachary Subin (UC Berkeley).
EU Commissions, Projects INTAS-01-2132 and INTAS-05-1000007-431;
Nordic Research Boardthrough the Nordic Networks on Fine-Scale
Atmospheric Modelling (NetFAM) and Towards Multi-Scale Modelling of
the Atmospheric Environment (MUSCATEN).
COSMO-CLM-ART Training, Langen, Germany, 23 – 31 March 2015
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...
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Single-Column Tests
• Kossenblatter See, Germany (52 N, depth = 2 m)
• Lake Krasnoye, Russia (60 N, depth = 8 m)
• Lake Pääjärvi, Finland (61 N, depth = 15 m)
• Ryan Lake, USA(45 N, depth = 9 m)
Forcing in single-column mode Known from observations: •
short-wave radiation flux, • long-wave radiation flux from the
atmosphere.
Computed as part of the solution (depend on lake surface
temperature):• long-wave downward radiation flux from the surface,
• fluxes of momentum and of sensible and latent heat, • for
ice-covered lakes, surface albedo.
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Kossenblatter See, 8-21 June 1998.
Water surface temperature (θf is the fresh-water freezing point)
• Dots- measured • Line - computed
0 48 96 144 192 240 288 33618
19
20
21
22
23
24
25
26
time, h
θs-
θ f ,
K
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Kossenblatter See, 8-21 June 1998.
Friction velocity in the surface air layer
• Symbols- measured
• Line - computed
216 240 264 288 3120
0.1
0.2
0.3
0.4
0.5
time, h
u*
, m⋅ s
-1
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Kossenblatter See, 8-21 June 1998.
• Sensible heat flux Qse• Latent heat flux Qla• Symbols–
measured
• Lines– computed
216 240 264 288 312-80
-60
-40
-20
0
20
time, h
Qse
, W
⋅ m-2
216 240 264 288 312-300
-250
-200
-150
-100
-50
0
time, h
Qla
, W
⋅ m-2
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Lake Krasnoye, 1 May - 31 October 1970.
Water surface temperature θs (θf is the fresh-water freezing
point)Dots– measured, line - computed
120 150 180 210 240 270 3000
5
10
15
20
25
time, day
θs-
θ f ,
K
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Lake Pääjärvi, 1 May 1999 - 31 August 2002.
Water surface temperature θs (θf is the fresh-water freezing
point) Dots– measured, line - computed
120 240 360 480 600 720 840 960 1080 12000
5
10
15
20
25
time, day
θs-
θ f ,
K
ice ice ice
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Lake Ryan, November 1989 – November 1990.
Surface temperature, mean temperature of the water column,
bottom temperature
Dotted – measured, solid – modelled
-30 0 60 120 180 240 3000
5
10
15
20
25
30
35
time, day
θ- θ
f , K
Ice
? (sensor malfunction)
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Lake Ryan, April – November 1990.
Mean temperature of the water column
Measuredvs. modelled
90 120 150 180 210 240 270 3000
5
10
15
20
time, day
θm
- θf ,
K
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Lake Ryan, December 1989.
• Solid - modelled ice surface temperature • Dotted- temperature
measured with the uppermost sensor
-30 -20 -10 0
-25
-20
-15
-10
-5
0
time, day
θi- θ
f , K
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Single-Column Tests: Perpetual Year Solution
• Lake Swente, Latvia (56 N, depth = 17.5 m, transparent
waterγ=0.3 m-1)
• computed atmospheric radiation fluxes
• climatologically mean forcing (1961 – 1964)
• measured water temperature at a number of depths
• no flux measurements
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Lake Swente, Perpetual Year.
Surface temperature, mean temperature of the water column,
bottom temperature
Symbols – measured, lines – modelled
0 60 120 180 240 300 360
0
5
10
15
20
time, day
θ- θ
f , K
Ice
-
...
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Lake Fraction
Lake-fraction external-parameter field for the LM1 numerical
domain (DWD) of the COSMO model based on the GLCC data set
(http://edcsns17.cr.usgs.gov/glcc/) with 30 arc sec resolution,
that is ca. 1 km at the equator.
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Lake Depth
Lake depths for the LM1 numerical domain of the COSMO model. The
field is developed using various data sets. Each lake is
characterised by its mean depth.
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...
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FLake in COSMO: Results from Parallel Experiment 56 32
1 January – 31 December 2006
Lake Hjälmaren, Sweden (mean depth = 6.1 m)
Ice thickness (left) and ice surface temperature (right)
computed with COSMO-FLake
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FLake in COSMO: Results from Parallel Experiment 56 32
1 January – 31 December 2006
Neisiedlersee, Austria-Hungary (mean depth = 0.8 m)• Black –
lake surface temperature from the COSMO SST analysis • Green– lake
surface temperature computed with FLake
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FLake in COSMO: Results from Parallel Experiment 56 32
1 January – 31 December 2006
Lake Vänern, Sweden (mean depth = 27 m)• Black – lake surface
temperature from the COSMO SST analysis • Green– lake surface
temperature computed with FLake
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COSMO-CLM-ART Training, Langen, Germany, 23 – 31 March 2015
Parameterization of Lakes in NWP and Climate Models
Dmitrii V. Mironov
German Weather Service, Offenbach am Main, Germany
Hermann Asensio, Erdmann Heise, Ekaterina Machulskaya, Bodo
Ritter(German Weather Service, Offenbach am Main, Germany) Sergey
Golosov(Institute for Lake Research, Russian Academy of Sciences,
St. Petersburg, Russia) Georgy Kirillin (Leibniz Institute of
Freshwater Ecology and Inland Fisheries, Berlin, Germany) Ekaterina
Kourzeneva(Finnish Meteorological Institute, Helsinki, Finland, and
Russian State Hydrometeorological University, St. Petersburg,
Russia) Natalia Schneider(University of Kiel, Kiel, Germany) Arkady
Terzhevik(Northern Water Problems Institute, Russian Academy of
Sciences, Petrozavodsk, Russia)