thermal regime and water-atmosphere interactions of shallow mid-latitude lakes: a case study within...
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Thermal regime and water-atmosphere Thermal regime and water-atmosphere interactions of interactions of
shallow mid-latitude lakes: shallow mid-latitude lakes: a case study within the framework of the a case study within the framework of the
Lake Model Intercomparison ProjectLake Model Intercomparison Project
Stepanenko, V.Stepanenko, V.11, A.Martynov, A.Martynov22, K.J, K.Jööhnkhnk33, ,
M.PerroudM.Perroud44, X.Fang, X.Fang55, Z.Subin, Z.Subin66, F.Beyrich, F.Beyrich77, , A.NordboA.Nordbo88, D.Mironov, D.Mironov99 and J.Huotari and J.Huotari1010
(1) Moscow State University, (1) Moscow State University, (2) Universite du Quebec a Montreal, (3) CSIRO Land and Water, Canberra, (2) Universite du Quebec a Montreal, (3) CSIRO Land and Water, Canberra,
(4) University of Michigan, (5) Auburn University, (4) University of Michigan, (5) Auburn University, (6) University of California, (7) Meteorologisches Observatorium(6) University of California, (7) Meteorologisches Observatorium
Lindenberg, (8) University of Helsinki, (9) DeutscherLindenberg, (8) University of Helsinki, (9) DeutscherWetterdienst, (10) Lammi Biological Station of University of HelsinkiWetterdienst, (10) Lammi Biological Station of University of Helsinki
EGU General Assembly 2011,
Session HS10.2/OS2.3 - Lakes and inland seas,8 April 2011
• Although numerous lake models exist, their validity range is rarely properly estimated by developers and by model users...
But: in regional and, especially, in global models simulation domains often cover different climatic and geomorphological zones with various types of lakes.
How can we be sure that lake models are valid in all these lakes?
LakeMIP: motivation
LakeMIP1 To assess the range of applicability of existing 1D model formulations, i.e. their capacities and limitations in reproducing lake-atmosphere interactions as well as internal lake thermodynamics. This includes the identification of the key physical processes to be taken into account in lake models in order to further improve their performance in lake-atmosphere interaction and in limnological studies.
LakeMIP2 To simulate the interaction mechanisms between lakes and the atmosphere in the framework of weather and climate models of different spatial domains, resolution and dimensionality.
• The LakeMIP web site: http://www.lakemip.net
LakeMIP goals
Kossenblatter Lake (Germany)Kossenblatter Lake (Germany)• Shallow (mean depth 2 m, max 5 m)
• Very turbid (extinction coef. ~7 m-1)
• Size 168 hectares
• Altitude 43 m ASL
• Observation data - Lindenberg Meteorological Observatory - Richard Aßmann Observatory, 2003
Observational dataVariables (sampling frequency/averaging interval)
• Conventional meteorological variables (1 Hz/10 min)
• Radiation (1 Hz/10 min)
• Turbulent heat and momentum fluxes (EC) (20 Hz/10 min)
• Water temperatureat 0, 0.02, 0.1, 0.2,0.5 and 1 m(1 Hz/10 min)
Physically-based quality controls
Mast 90 m
from shore
The set of 1D lake modelsThe set of 1D lake modelsLake model
The type of model
Soil scheme
Source
“Completely-mixed”
One-layer model No -
FLake Two-layer model Yes Mironov et al. 2010
Hostetler (CRCM)
Multilayer model No Hostetler et al., 1993
CLM-VRLS(Hostetler_CLM)
Multilayer model Yes Subin et al., submitted
MINLAKE96 Multilayer model Yes Fang and Stefan, 1996
LAKE Multilayer, K-ε model
Yes Stepanenko et al., 2011
Simstrat Multilayer, K-ε model
No Goudsmit et al., 2002
LAKEoneD Multilayer, K-ε model
No Jöhnk and Umlauf, 2001
Focus on effects on models' performanceof two features:
vertical mixing treatment (turbulence closure) heat exchage with bottom sediments
Setup of numerical experimentsSetup of numerical experiments
• warm season of 2003 (1 May–11 November)
• depth – mean lake depth (2m)
• extinction coefficient 7.08 m-1 (Secchi disk 0.24 m)
• timestep <10 min, MINLAKE96 – 24 h
• “native” surface turbulent flux schemes
• zero heat flux at the bottom or explicit
soil treatment if available
Reference experiment
Sensitivity experiments1) depth — local depth in point of measurements (1.2 m), maximal lake depth (5 m)2) model experiments with neglecting bottom sedimets
Two periodsTwo periods
• 1 – high temperature period (summer, stable stratification):
1 May – 10 August• 2 - temperature decrease period (late
summer, autumn, unstable stratification)
10 August – 10 November
1 — high T 2 — T decreases
Surface temperature errorsSurface temperature errorsfor temperature rise periodfor temperature rise period
K-ε models
During the period of stable stratification in a lake K-ε models have less errors. This does not depend on whether they includebottom sediments parameterization or assume zero bottom heat flux.
Lake stratificationLake stratification
Hostetler and Flake produce too strong stratification in summer
The 0 – 1 m depth temperature difference
Bottom temperatureBottom temperature
Hostetler and Flake produce almostconstant bottom temperature insummer, which is likely dueto very reduced turbulent mixing
Surface temperature errorsSurface temperature errorsfor temperature decrease periodfor temperature decrease period
there is no clear evidence whether turbulence closureor explicit soil parameterization play crucial role in models' performance k-ε models lacking soil parameterization produce negative mean difference
Models including soil/sediments block
K-ε models
Surface temperature in the fallSurface temperature in the fall
Underestimating the temperatureup to 4-5 °C
Modelsunderestimating temperature:two k-ε models (without soil),complete-mixed (without soil),MINLAKE96 (with soil!)
Time, days
Conclusions for Conclusions for Kossenblatter experimentKossenblatter experiment
• All models captured well diurnal and seasonal variability of lake surface temperature
• During most of summer correct parameterization of turbulent mixing is a major factor for simulating both surface and bottom temperatures
• During fall neglecting heat exchange with bottom sediments leads to systematic underestimation of surface temperature but the magnitude of this effect is model-dependent
Valkea-Kotinen lake (Finland)
Observation data gathered and processed by University of Helsinki
2 May — 31 December 2006 with hourly resolution meterorology radiation fluxes eddy covariance for heat and momentum fluxes water temperature at 13 levels from 0.2 m to 4 m
Depth:Maximal 6 mMean 3 mArea 4.1 hectares
First results of Valkea-Kotinen experiment
Small and relatively deep lake — beyond an applicability of 1D k-ε models?
LAKE (k-ε) model
Hostetler_CLM model
Conclusions• 3D lake modeling guidance is needed for
more physically-based determination of 1D models «applicability area»
• LakeMIP is open for new participants wishing to validate their lake models using
comprehensive observational datasets
““Completely-mixed” modelCompletely-mixed” model
ρc p∂T∂ t
=∂∂ zk T
∂T∂ z
−∂ S∂ z
ρc p∂ T∂ t
=−kT ∂ T
∂ t+S∣z=hz= 0
h=
1h F 0+S0− F b−S b
Assumes complete mixing in vertical, Fb = 0 additionally neglects heat flux through water-sediments interface
The influence of depth on The influence of depth on surface temperature errorsurface temperature error
Depths: local(1.2m), mean(2m) and maximal (5m)
Errors when using local and mean depths are close, but with 5 m they increase due the increase of lake heat capacity
Surface temperature errorsSurface temperature errorsfor the whole periodfor the whole period
K-ε models
Models including soil/sediments block
For the entire period models demonstrate comparable error values,with slightly lower RMSEs for K-ε models. The exception is complete-mixed model that produce significant underestimation of mean surface temperature.
Surface flux schemes testSurface flux schemes testSurface schemes areforced by
1) surface layermeteorology;
2) measuredwater surfacetemperature.
All meandifferencesare positive.
Heat fluxes at the lake surfaceHeat fluxes at the lake surfaceMeans:
SHF = 8 W/m2,
LHF = 67 W/m2
All meandifferencesarepositive.
The lake 1D heat balanceThe lake 1D heat balance
δ=ρc ph∂ T∂ t
−F 0+S0−Fb−S bNegligibledue to high water turbidity
Mean δ-24…-26 W/m2
depending onFb = 0 or Fb takenfrom LAKE model
Mean difference of computed total heat flux from observed:
Scheme d(mean), W/m2
FLake 9.48
Hostetler 15.1
LAKE 25.08
Simstrat 31.32
LAKEoneD 17.84
ConsistentwithValkea-KotinenLake (Finland),Nordbo et al., 2011
Errors of sensible and latent Errors of sensible and latent heat fluxesheat fluxes
The model Sensible heat flux Latent heat flux
D(MEAN) RMSE D(MEAN) RMSE
Coupled Decoupled Coupled Decoupled Coupled Decoupled
Coupled Decoupled
Flake_passive
5.70 3.46 13.95 9.34 15.96 6.02 42.52 35.99
Flake_active 6.16 3.46 14.06 9.34 16.96 6.02 43.09 35.99
Hostetler 5.82 5.04 12.75 10.22 15.99 10.60 44.04 41.15
MINLAKE96 6.75 0.02 - - 22.33 0.58 - -
LAKE 5.51 5.94 11.55 12.63 18.35 19.14 41.28 48.48
Simstrat 5.22 6.63 12.18 11.44 21.72 24.69 37.64 40.04
LAKEoneD 2.29 3.47 10.74 8.57 13.84 14.37 33.94 32.71