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Impact of lake surface temperatures simulated by the FLake scheme in the CNRM-CM5 climate model Cover

Impact of lake surface temperatures simulated by the FLake scheme in the CNRM-CM5 climate model

Open Access
|Jan 2016

Figures & Tables

Fig. 1

(a) Fraction of lake cover (%) in CNRM-CM5. (b) Location and depth of the 200 selected lakes for the ARC-Lake database.

Fig. 2

Impact of depth limitation on the mean annual cycle of surface temperature: (a) Lake Superior, (b) Lake Baikal, (c) Lake Malawi, (d) Lake Buenos-Aires, (e) map of biases difference XPR minus XPD and (f) map of RMSEs difference XPR minus XPD.

Table 1. Summary of offline calibration experiments

Exp.Max depth (m)Ice albedoLight extinction coefficient (m−1)Skin temperature
XPRUnlimited0.63.0OnXPD600.63.0OnXPA600.43.0OnXPE600.40.5OnXPF600.40.5Off

Table 2. Comparison of seasonal biases for XPR and XPD for four selected lakes

DJFJJA

LakesXPRXPDXPRXPD
Baikal//−8.7−5.5Superior2.60.3−1.90.7Malawi−0.50.51.8−0.0Buenos-Aires−1.51.90.1−3.0
Fig. 3

Impact of ice albedo reduction on mean annual cycle of the surface temperature: (a) Lake Athabasca, (b) Lake Ngoring, (c) Lake Baikal, (d) map of reduction of thaw delay (days) for XPD minus XPA and (e) map of reduction of thaw delay as a function of height.

Fig. 4

Taylor diagram summarising the improved surface temperature results for experiments XPR, XPD, XPA and XPE.

Fig. 5

Histogram of RMSE differences of surface temperatures XPE minus XPF for which skin temperature parameterisation has been turned off.

Fig. 6

Evaluation of Offline-FLake and Inline-FLake versus ARC-Lake data. Top: Mean annual surface temperature (°C) of lakes. Bottom: Number of days per year (%) for which each lake is frozen. Left panel: Offline-FLake – ARC-Lake. Right panel: Inline-FLake – ARC-Lake.

Fig. 7

Mean annual cycles of surface temperatures (°C) over 12 lakes whose characteristics are given in Table 3. Black line: ARC-Lake data; blue line: Offline-FLake; and red line: Inline-Flake.

Table 3. Characteristics of lakes

Latitude Longitude Altitude (m)Mean lake depth (m)
Fagnano−54.55−68.0316970Victoria−1.3033.23114040Nicaragua11.57−85.363113Tai31.21120.2422Tahoe39.09−120.041996249Aral45.1360.084216Constance47.659.2843190Superior47.72−88.23184149Baikal53.63108.14450680Ladoga60.8431.391152Great Bear65.91−121.3015772Taymyr74.48100.76243Malawi−11.9634.59485273Buenos-Aires−46.66−72.50451290Ngoring34.9397.71424018Athabasca59.10−109.9621226
Fig. 8

Seasonal mean differences with and without FLake (Inline-FLakeInline-noFLake) of Albedo and surface temperatures (°C). Left panel: Surface albedo over lakes. Central panel: Surface temperature over lakes. Right panel: Total surface temperature.

Fig. 9

Seasonal mean daily maximum 2-meter temperature (°C). Left panel: Inline-noFLake – CRU. Central panel: Inline-FLake – CRU. Right panel: Inline-FLake – Inline-noFLake. The grey-stippled areas on the right panel indicate a 95 % significance.

Fig. 10

Same as Fig. 9 for daily minimum 2-m temperature.

Fig. 11

Seasonal mean differences with and without FLake (Inline-FLakeInline-noFLake) of specific humidity (103 kg/m3) on the left panel, latent heat flux (W/m−2) on the central panel and sensible heat flux (W/m−2) on the right panel. The grey-stippled areas indicate a 95 % significance.

Fig. 12

Meridian and zonal slices of potential temperature (°C) and specific humidity (103 kg/m3) differences (Inline-FlakeInline-noFLake) over the following three boxes: Canadian tundra (120°W–100°W/60°N–70°N), Canadian taiga (150°W–120°W/55°N–65°N) and Great Lakes (95°W–75°W/42°N–50°N). (a) Winter (DJF); (b) summer (JJA); and (c) autumn (SON).

Fig. 13

Typical vertical profiles of potential temperature (°C) (a, c, e, g) and specific humidity (103 kg.m−3) (b, d, f, h). (a) and (b) correspond to the winter (DJF) mean over Lake Michigan; (c) and (d) are the winter means (DJF) over south Alaska (145°W–130°W/56°N–61°N); (e) and (f) are the summer (JJA) mean over Lake Michigan; (g) and (h) are the autumn (SON) means over Lake Michigan.

B.1. Description of the 200 lakes used for the evaluation

LAKELat (N) Lon (E)Height (m)Depth (m) Name
1−54.55−68.0316970FAGNANO2−50.33−73.03225150ARGENTINO3−49.59−72.5629775.8VIEDMA4−48.75−72.84330170SAN MARTIN5−46.66−72.5451290BUENOS-AIRES6−45.47−68.762512COLHUE HUAPI7−41.14−72.7956182LLANQUIHUE8−40.92−71.52810157NAHUEL HUAPI9−38.81175.939591TAUPO10−35.52139.09202.8ALEXANDRINA11−33.16−52.8483.95MANGUEIRA12−30.74−62.61667.3CHIQUITA13−18.81−67.0636790.5POOPO14−15.3235.716292CHILWA15−11.9634.59485273MALAWI16−8.7129.729452CHISHI17−8.6526.45860.9UPEMBA18−6.95141.53595MURRAY19−6.0729.46837572TANGANYIKA20−3.5835.0410231EYASI21−2.79121.52230100TOWUTI22−2.3436.025832.3NATRON23−2.0429.231500240KIVU24−1.333.23114040VICTORIA25−0.8217.983384TUMBA26−0.3929.6178134EDWARD271.533.0110436KYOGA281.7232.6510436KWANIA292.6198.9898184.6TOBA305.23−3.23733.8ABY315.3−4.26744.5EBRIE325.8337.5512496CHAMO336.337.8312497ABAYA347.46100.38671LUANG3511.1741.794148.6ABE3611.57−85.363113NICARAGUA3711.9537.3117868TANA3812.32−86.35567.8MANAGUA3912.54−83.6702.5PERLAS4014.36121.26123BAY4115.35−83.8505CARATASCA4215.57−89.114712IZABAL4318.49−71.58814ENRIQUILLO4419.6985.38161.35CHILKA4520.21−103.0515737.2CHAPALA4621.66−97.5702TAMIAHUA4724.64−97.6601.1MADRE4826.95−80.8643OKEECHOBEE4928.24−80.6400.9INDIAN RIVER5028.9790.76444623.6YAMDROK5130.7190.66470434NAM5230.985.6147434TERINAM5331.0586.59464925TANGRA5431.1388.32464015KYARING5531.21120.2421.9TAI5631.5235.49−404149DEAD5731.57117.5713CHAO5831.7788.95455733ZILING5932.87119.3117.9GAOYOU6033.3−115.83−748SALTON6133.34118.5331.4HUNGTZE6233.8278.61420016PANGONG6334.61117.2474.2WEISHAN6434.9297.27425613.1GYARING6534.9397.71424017.6NGORING6635.25136.086941BIWA6736.89100.18317617QINGHAI6837.7831.5211485BEYSEHIR6938.01−118.96195516.5MONO7038.0730.859469EGRIDIR7138.3197.5942394HAR-HU7238.6642.98163857VAN7338.7−118.71133924WALKER7439.02−122.774746.5CLEAR7539.09−120.041996249TAHOE7640.03−119.55116160PYRAMID7740.26−121.19140327.5ALMANOR7840.3945.29187641SEVAN7941.2353.54−2210KARA-BOGAZ-GOL8041.8550.36−22182CASPIAN8141.9887.0710629.7BOSTEN8242.25−81.1617419ERIE8342.4677.251619320ISSYKKUL8442.5−82.731743SAINT CLAIR8543.34−118.8312420.6MALHEUR8643.85−77.777586ONTARIO8743.86−87.0917685MICHIGAN8844.02−88.422294.7WINNEBAGO8944.45−73.273522.8CHAMPLAIN9044.47−79.4223315SIMCOE9144.78−82.2117659HURON9244.8328.9713RAZELM9344.8682.921661.5EBI9445.1360.084216ARAL9545.9173.953297BALKHASH9645.95−60.8335150BRAS D'OR9745.9634.7421SYVASH9846.1181.7533622ALAKOL9946.24−79.922124.5NIPISSING10046.24−93.653816.4MILLE LACS10146.2642.98150.6MANYCH-GUDILO10246.376.25466154.4GENEVA10346.5880.913423.3SASYKKOL10446.8817.831264BALATON10547.2287.34528ULUNGUR10647.659.2843190CONSTANCE10747.72−88.23184149SUPERIOR10847.81117.695978.1BUYR10948.04−95.083589RED11048.0593.2111604HAR11148.0692.311914HAR US11248.61−92.973439.9RAINY11348.66−72.029711.4SAINT JEAN11448.97117.385395HULUN11549.1393.3102720HYARGAS11649.8−88.5528354.9NIPIGON11750.07115.815954.6BARUN-TOREY11850.4468.92977TENGIZ11950.82−73.8137475MISTASSINI12050.97−77.022315EVANS12150.99−98.824712MANITOBA12251.02100.481640138HOVSGOL12351.27−99.772592.4DAUPHIN12451.48136.51802.5EVORON12552.12−97.2521713WINNIPEG12652.37−100.052524.24WINNIPEGOSIS12752.9879.58923.9KULUNDINSKOYE12853−93.032825.36SANDY12953.22−76.751748SAKAMI13053.2373.18661.9SELETYTENIZ13153.33−100.142564.18CEDAR13253.63108.14450680BAIKAL13353.77−90.0221010.5BIG TROUT13453.83−100.042564.1SOUTH MOOSE13553.85−94.723020.1ISLAND13654.05−100.162574.1NORTH MOOSE13754.07−97.752174PLAYGREEN13854.19−64.314724.3SMALLWOOD13954.62−94.2118113.2GODS14054.71−97.582038.2CROSS14154.76−107.2845910.9DORE14254.78−103.453296.2DESCHAMBAULT14355.05−72.9842313BIENVILLE14455.11−104.8337112.7RONGE14555.43−115.4960411.7LESSER SLAVE14655.4780.051312UBINSKOYE14755.84−108.5542313.7PETER POND14855.96−108.294239CHURCHILL14956.15−74.42515.2EAU CLAIRE15056.37−108.224235.5FROBISHER15156.39162.7714.4NERPICH'YE15256.789.1737LIMFJORDEN15357.19−102.2733717REINDEER15457.47−106.6448414.9CREE15557.85−156.42256BECHAROF15658.3−103.3339620.6WOLLASTON15758.3314.579139VATTERN15858.59−112.082121.2CLAIRE15958.64−155.673941NAKNEK16058.8813.224527VANERN16159.1−109.9621226ATHABASCA16259.4416.191811.9MALAREN16359.56−154.93544ILIAMNA16459.57−133.7570486ATLIN16560.1837.641224.15BELOYE16660.22−115.492642.8BUFFALO16760.54−117.642771TATHLINA16860.8431.391152LADOGA1696122.28525.5PYHAJARVI17061.0925.39966VESIJARVI17161.37−97.2918418SOUTH HENIK17261.7125.499517PAIJANNE17361.7729.027912PURUVESI17461.935.355630ONEGA17562.09−114.3715841GREAT SLAVE17662.128.528017HAUKIVESI17762.3529.597917ORIVESI17862.8925.991077KEITELE17963.1629.711139.9PIELINEN18063.17−107.823592.13ARTILLERY18163.3233.7612929SEGOZERO18263.33−117.912659MARTRE18363.5434.84747.4VYGOZERO18463.93−97.661152.5PRINCESS MARY18564.53174.4426.8KRASNOE18664.54−110.3845428GRAS18764.55−98.597618ABERDEEN18865.6232.0911216TOPOZERO18965.91−121.315771.7GREAT BEAR19065.95−99.41506.1GARRY19166.0730.9811815PYAOZERO19266.42−70.283020NETILLING19366.51−160.73115SELAWIK19467.79−97.7315.8SHERMAN19568.3691.185473KHANTAYSKOE19669.0427.8312614.4INARI19769.7787.78174PYASINO19870.59−153.601TESHEKPUK19974.48100.76242.8TAYMYR20081.8−70.9418285HAZEN
Language: English
Page range: 31274 - 31274
Submitted on: Jan 4, 2016
Accepted on: Jul 20, 2016
Published on: Jan 1, 2016
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2016 Patrick Le Moigne, Jeanne Colin, Jeanne Colin, Bertrand Decharme, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.