Articles | Volume 26, issue 4
https://doi.org/10.5194/npg-26-401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-26-401-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2019
Research article |
| 15 Nov 2019
| 15 Nov 2019
A prototype stochastic parameterization of regime behaviour in the stably stratified atmospheric boundary layer
University of Victoria, School of Earth and Ocean Sciences, P.O. Box 3065 STN CSC, Victoria, BC V8P 5C2, Canada
Amber M. Holdsworth
Institute of Ocean Sciences, Fisheries and Oceans Canada, 9860 W. Saanich Rd., P.O. Box 6000, Sidney, BC V8L 4B2, Canada
Adam H. Monahan
University of Victoria, School of Earth and Ocean Sciences, P.O. Box 3065 STN CSC, Victoria, BC V8P 5C2, Canada
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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
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Cited articles
Abraham, C. and Monahan, A. H.: Climatological Features of the Weakly and Very Stably Stratified Nocturnal Boundary Layers, Part I: State Variables Containing Information about Regime Occupation, J. Atmos. Sci., 76, 3455–3484, https://doi.org/10.1175/JAS-D-18-0261.1, 2019a. a
Abraham, C. and Monahan, A. H.: Climatological Features of the Weakly and Very Stably Stratified Nocturnal Boundary Layers. Part II: Regime Occupation and Transition Statistics and the Influence of External Drivers, J. Atmos. Sci., 76, 3485–3504, https://doi.org/10.1175/JAS-D-19-0078.1, 2019b. a
Abraham, C. and Monahan, A. H.: Climatological Features of the Weakly and Very Stably Stratified Nocturnal Boundary Layers, Part III: The Structure of Meteorological State Variables in Persistent Regime Nights and across Regime Transitions, J. Atmos. Sci., 76, 3505–3527, https://doi.org/10.1175/JAS-D-18-0274.1, 2019c. a
Abraham, C., Holdsworth, A. M., and Monahan, A. H.: Replication Data for: A prototype stochastic parameterization of regime behaviour in the stably stratified atmospheric boundary layer, https://doi.org/10.5683/SP2/ZUENCK, Scholars Portal Dataverse, 2019.
Acevedo, O. C. and Fitzjarrald, D. R.: In the Core of the Night-Effects of
Intermittent Mixing on a Horizontally Heterogeneous Surface, Bound-Lay.
Meteorol., 106, 1–33, https://doi.org/10.1023/A:1020824109575, 2003. a
Acevedo, O. C., Moraes, O. L. L., Degrazia, G. A., and Medeiros, L. E.:
Intermittency and the Exchange of Scalars in the Nocturnal Surface Layer,
Bound.-Lay. Meteorol., 119, 41–55, https://doi.org/10.1007/s10546-005-9019-3, 2006. a
Acevedo, O. C., Mahrt, L., Puhales, F. S., Costa, F. D., Medeiros, L. E., and
Degrazia, G. A.: Contrasting structures between the decoupled and coupled
states of the stable boundary layer, Q. J. Roy. Meteorol. Soc., 142, 693–702,
https://doi.org/10.1002/qj.2693, 2016. a
Acevedo, O. C., Maroneze, R., Costa, F. D., Puhales, F. S., Nogueira Martins,
L. G., Soares de Oliveira, P. E., and Mortarini, L.: The Nocturnal
Boundary Layer Transition from Weakly to Very Stable, Part 1: Observations,
Q. J. Roy. Meteorol. Soc., https://doi.org/10.1002/qj.3642, 2019. a
Ansorge, C. and Mellado, J. P.: Global Intermittency and Collapsing Turbulence
in the Stratified Planetary Boundary Layer, Bound.-Lay. Meteorol., 153,
89–116, https://doi.org/10.1007/s10546-014-9941-3, 2014. a, b
Baas, P., Bosveld, F. C., Baltink, H. K., and Holtslag, A. A. M.: A
Climatology of Nocturnal Low-Level Jets at Cabauw, J. Appl. Meteorol.
Climatol., 48, 1627–1642, https://doi.org/10.1175/2009JAMC1965.1, 2009. a
Banta, R. M., Mahrt, L., Vickers, D., Sun, J., Balsley, B. B., Pichugina,
Y. L., and Williams, E. J.: The Very Stable Boundary Layer on Nights with
Weak Low-Level Jets, J. Atmos. Sci., 64, 3068–3090,
https://doi.org/10.1175/JAS4002.1, 2007. a, b
Barthlott, C., Kalthoff, N., and Fiedler, F.: Influence of high-frequency
radiation on turbulence measurements on a 200 m tower, Meteorol. Z, 12,
67–71, https://doi.org/10.1127/0941-2948/2003/0012-0067, 2003. a
Basu, S., Porté-agel, F., Foufoula-Georgiou, E., Vinuesa, J.-F., and
Pahlow, M.: Revisiting the Local Scaling Hypothesis in Stably Stratified
Atmospheric Boundary-Layer Turbulence: an Integration of Field and Laboratory
Measurements with Large-Eddy Simulations, Bound.-Lay. Meteorol., 119,
473–500, https://doi.org/10.1007/s10546-005-9036-2, 2006. a
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M.,
Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating
atmospheric variability with the ECMWF model: From synoptic to decadal
time-scales, Q. J. Roy. Meteorol. Soc., 134, 1337–1351, https://doi.org/10.1002/qj.289,
2008. a
Beeken, A., Neumann, T., and Westerhellweg, A.: Five Years of Operation of the
First Offshore Wind Research Platform in the German Bight – FINO1, Tech.
rep., German Wind Energy Institute (DEWI GmbH), DEWEK, DEWI GmbH,
Ebertstraße 96, 26382 Wilhelmshaven, available at:
http://www.dewi.de/dewi/fileadmin/pdf/publications/Publikations/5_Beeken.pdf (last access: 12 November 2019),
2008. a
Blackadar, A.: Modeling the nocturnal boundary layer, in: Proceedings of the
Third Symposium on Atmospheric Turbulence, Diffusion and Air Quality,
46–49, American Meteorological Society, Boston, Mass., 1976. a
Blackadar, A.: High resolution models of the planetary boundary layer, Adv.
Environ. Sci. Eng., 1, 50–85, 1979. a
Blackadar, A. K.: The vertical distribution of wind and turbulent exchange in a
neutral atmosphere, J. Geophys. Res., 67, 3095–3102,
https://doi.org/10.1029/JZ067i008p03095, 1962. a
Blumen, W.: An observational study of instability and turbulence in nighttime
drainage winds, Bound.-Lay. Meteorol., 28, 245–269,
https://doi.org/10.1007/BF00121307, 1984. a
Blumen, W., Banta, R., Burns, S. P., Fritts, D. C., Newsom, R., Poulos, G. S.,
and Sun, J.: Turbulence statistics of a Kelvin–Helmholtz billow event
observed in the night-time boundary layer during the Cooperative
Atmosphere–Surface Exchange Study field program, Dyn. Atmos. Oceans, 34,
189–204, https://doi.org/10.1016/S0377-0265(01)00067-7, 2001. a
Bosveld, F. C., Baas, P., Steeneveld, G.-J., Holtslag, A. A. M., Angevine,
W. M., Bazile, E., de Bruijn, E. I. F., Deacu, D., Edwards, J. M., Ek, M.,
Larson, V. E., Pleim, J. E., Raschendorfer, M., and Svensson, G.: The Third
GABLS Intercomparison Case for Evaluation Studies of Boundary-Layer Models.
Part B: Results and Process Understanding, Bound.-Lay. Meteorol., 152,
157–187, https://doi.org/10.1007/s10546-014-9919-1, 2014. a, b
Bowen, B. M., Baars, J. A., and Stone, G. L.: Nocturnal Wind Direction Shear
and Its Potential Impact on Pollutant Transport, J. Appl. Meteorol. Climatol.,
39, 437–445, https://doi.org/10.1175/1520-0450(2000)039<0437:NWDSAI>2.0.CO;2, 2000. a
Brümmer, B., Lange, I., and Konow, H.: Atmospheric boundary layer
measurements at the 280 m high Hamburg weather mast 1995–2011: mean annual
and diurnal cycles, Meteorol. Z., 21, 319–335,
https://doi.org/10.1127/0941-2948/2012/0338, 2012. a
Businger, J.: A note on the Businger-Dyer profiles, Bound.-Lay. Meteorol.,
42, 145–151, https://doi.org/10.1007/BF00119880, 1988. a
Coulter, R. L. and Doran, J. C.: Spatial and Temporal Occurrences of
Intermittent Turbulence During CASES-99, Bound.-Lay. Meteorol., 105,
329–349, https://doi.org/10.1023/A:1019993703820, 2002. a
Deardorff, J. W.: Efficient prediction of ground surface temperature and
moisture, with inclusion of a layer of vegetation, J. Geophys.
Res.-Oceans, 83, 1889–1903, https://doi.org/10.1029/JC083iC04p01889, 1978. a
Dempster, A. P., Laird, N. M., and Rubin, D. B.: Maximum Likelihood from
Incomplete Data via the EM Algorithm, J. Roy. Stat. Soc., 39B, 1–38, 1979. a
Derbyshire, S. H.: Boundary-Layer Decoupling over Cold Surfaces as a Physical
Boundary-Instability, Bound.-Lay. Meteorol., 90, 297–325,
https://doi.org/10.1023/A:1001710014316, 1999. a
Dethloff, K., Abegg, C., Rinke, A., Hebestadt, I., and Romanov, V. F.:
Sensitivity of Arctic climate simulations to different boundary-layer
parameterizations in a regional climate model, Tellus A, 53, 1–26,
https://doi.org/10.1034/j.1600-0870.2001.01073.x, 2001. a
Donda, J. M. M., van Hooijdonk, I. G. S., Moene, A. F., Jonker, H. J. J., van
Heijst, G. J. F., Clercx, H. J. H., and van de Wiel, B. J. H.: Collapse of
turbulence in stably stratified channel flow: a transient phenomenon, Q. J.
Roy. Meteorol. Soc., 141, 2137–2147, https://doi.org/10.1002/qj.2511, 2015. a
Doran, J. C.: Characteristics of Intermittent Turbulent Temperature Fluxes in
Stable Conditions, Bound.-Lay. Meteorol., 112, 241–255,
https://doi.org/10.1023/B:BOUN.0000027907.06649.d0, 2004. a, b
Dörenkämper, M., Witha, B., Steinfeld, G., Heinemann, D., and Kühn,
M.: The impact of stable atmospheric boundary layers on wind-turbine wakes
within offshore wind farms, J. Wind Eng. Ind.
Aerodynam., 144, 146–153, https://doi.org/10.1016/j.jweia.2014.12.011, 2015. a, b
Edwards, J. M., McGregor, J. R., Bush, M. R., and Bornemann, F. J. A.:
Assessment of numerical weather forecasts against observations from
Cardington: seasonal diurnal cycles of screen-level and surface temperatures
and surface fluxes, Q. J. Roy. Meteorol. Soc., 137, 656–672,
https://doi.org/10.1002/qj.742, 2011. a
Fischer, J.-G., Senet, C., Outzen, O., Schneehorst, A., and Herklotz, K.:
Regional oceanographic distincions in the South-Eastern part of the North
Sea: Results of two years of monitoring at the research platforms FINO1 and
FINO3, in: German Wind Energy Conference DEWEK 2012, edited by: J.-G. Fischer,
Bremen, Germany, 2012. a, b
Floors, R., Peña, A., and Gryning, S.-E.: The effect of baroclinicity on
the wind in the planetary boundary layer, Q. J. Roy. Meteorol. Soc., 141,
619–630, https://doi.org/10.1002/qj.2386, 2014. a
Flores, O. and Riley, J. J.: Analysis of Turbulence Collapse in the Stably
Stratified Surface Layer Using Direct Numerical Simulation, Bound.-Lay.
Meteorol., 139, 241–259, https://doi.org/10.1007/s10546-011-9588-2, 2011. a
Franzke, C., Horenko, I., Majda, A. J., and Klein, R.: Systematic Metastable
Atmospheric Regime Identification in an AGCM, J. Atmos.
Sci., 66, 1997–2012, https://doi.org/10.1175/2009JAS2939.1, 2009. a
Fu, G., Charles, S. P., and Kirshner, S.: Daily rainfall projections from
general circulation models with a downscaling nonhomogeneous hidden Markov
model (NHMM) for south-eastern Australia, Hydrol. Process., 27,
3663–3673, https://doi.org/10.1002/hyp.9483,
2013. a
Genthon, C., Town, M. S., Six, D., Favier, V., Argentini, S., and Pellegrini,
A.: Meteorological atmospheric boundary layer measurements and ECMWF
analyses during summer at Dome C, Antarctica, J. Geophys. Res.-Atmos., 115, D5,
https://doi.org/10.1029/2009JD012741, 2010. a
Genthon, C., Six, D., Gallée, H., Grigioni, P., and Pellegrini, A.: Two
years of atmospheric boundary layer observations on a 45 m tower at Dome C on
the Antarctic plateau, J. Geophys. Res.-Atmos., 118, 3218–3232,
https://doi.org/10.1002/jgrd.50128, 2013. a
Gerbig, C., Körner, S., and Lin, J. C.: Vertical mixing in atmospheric tracer transport models: error characterization and propagation, Atmos. Chem. Phys., 8, 591–602, https://doi.org/10.5194/acp-8-591-2008, 2008. a
Grachev, A. A., Fairall, C. W., Persson, P. O. G., Andreas, E. L., and Guest,
P. S.: Stable Boundary-Layer Scaling Regimes: The SHEBA Data, Bound.-Lay.
Meteorol., 116, 201–235, https://doi.org/10.1007/s10546-004-2729-0, 2005. a
Grachev, A. A., Andreas, E. L., Fairall, C. W., Guest, P. S., and Persson, P.
O. G.: The Critical Richardson Number and Limits of Applicability of Local
Similarity Theory in the Stable Boundary Layer, Bound.-Lay. Meteorol., 147,
51–82, https://doi.org/10.1007/s10546-012-9771-0, 2013. a
Gryning, S.-E., Floors, R., Peña, A., Batchvarova, E., and Brümmer,
B.: Weibull Wind-Speed Distribution Parameters Derived from a Combination of
Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites,
Bound.-Lay. Meteorol., 159, 329–348, https://doi.org/10.1007/s10546-015-0113-x, 2016. a
He, Y., Monahan, A. H., Jones, C. G., Dai, A., Biner, S., Caya, D., and Winger,
K.: Probability distributions of land surface wind speeds over North
America, J. Geophys. Res.-Atmos., 115, D04103, https://doi.org/10.1029/2008JD010708,
2010. a
He, Y., McFarlane, N. A., and Monahan, A. H.: A New TKE-Based Parameterization
of Atmospheric Turbulence in the Canadian Global and Regional Climate
Models, J. Adv. Model. Earth Sy., 11, 1153–1188,
https://doi.org/10.1029/2018MS001532, 2019. a
Holdsworth, A. M., Rees, T., and Monahan, A. H.: Parameterization Sensitivity
and Instability Characteristics of the Maximum Sustainable Heat Flux
Framework for Predicting Turbulent Collapse, J. Atmos. Sci., 73, 3527–3540,
https://doi.org/10.1175/JAS-D-16-0057.1, 2016. a
Holtslag, A. A. M., Svensson, G., Baas, P., Basu, S., Beare, B., Beljaars, A.
C. M., Bosveld, F. C., Cuxart, J., Lindvall, J., Steeneveld, G. J.,
Tjernström, M., and Wiel, B. J. H. V. D.: Stable Atmospheric Boundary
Layers and Diurnal Cycles: Challenges for Weather and Climate Models, B.
Am. Meteor. Soc., 94, 1691–1706, https://doi.org/10.1175/BAMS-D-11-00187.1, 2013. a, b, c, d
Horenko, I.: On the Identification of Nonstationary Factor Models and Their
Application to Atmospheric Data Analysis, J. Atmos.
Sci., 67, 1559–1574, https://doi.org/10.1175/2010JAS3271.1, 2010. a
Hughes, J. P., Guttorp, P., and Charles, S. P.: A non-homogeneous hidden Markov
model for precipitation occurrence, J. Roy. Stat. Soc.
C, 48, 15–30, https://doi.org/10.1111/1467-9876.00136,
1999. a
Kaimal, J. C. and Gaynor, J. E.: The Boulder Atmospheric Observatory, J.
Appl. Meteorol. Climatol., 22, 863–880,
https://doi.org/10.1175/1520-0450(1983)022<0863:TBAO>2.0.CO;2, 1983. a
Kalthoff, N. and Vogel, B.: Counter-current and channelling effect under
stable stratification in the area of Karlsruhe, Theor. Appl. Climatol., 45,
113–126, https://doi.org/10.1007/BF00866400, 1992. a
Kyselý, J. and Plavcová, E.: Biases in the diurnal temperature range
in Central Europe in an ensemble of regional climate models and their
possible causes, Clim. Dynam., 39, 1275–1286,
https://doi.org/10.1007/s00382-011-1200-4, 2012. a
Lang, F., Belušić, D., and Siems, S.: Observations of
Wind-Direction Variability in the Nocturnal Boundary Layer, Bound.-Lay.
Meteorol., 166, 51–68, https://doi.org/10.1007/s10546-017-0296-4, 2018. a
Mahrt, L.: Nocturnal Boundary-Layer Regimes, Bound.-Lay. Meteorol., 88,
255–278, https://doi.org/10.1023/A:1001171313493, 1998a. a, b
Mahrt, L.: Stratified atmospheric boundary layers and breakdown of models,
Theor. Comput. Fluid Phys., 11, 263–279, https://doi.org/10.1007/s001620050093,
1998b. a, b
Mahrt, L.: Common microfronts and other solitary events in the nocturnal
boundary layer, Q. J. Roy. Meteorol. Soc., 136, 1712–1722,
https://doi.org/10.1002/qj.694, 2010. a
Mahrt, L.: The Near-Calm Stable Boundary Layer, Bound.-Lay. Meteorol., 140,
343–360, https://doi.org/10.1007/s10546-011-9616-2, 2011. a
Maroneze, R., Acevedo, O. C., Puhales, F. S., Demarco, G., and Mortarini, L.:
The Nocturnal Boundary Layer Transition from Weakly to Very Stable, Part 2:
Numerical Simulation with a Second Order Model, Q. J. Roy. Meteorol. Soc.,
https://doi.org/10.1002/qj.3643, 2019. a, b
Mauritsen, T. and Svensson, G.: Observations of Stably Stratified Shear-Driven
Atmospheric Turbulence at Low and High Richardson Numbers, J. Atmos. Sci.,
64, 645–655, https://doi.org/10.1175/JAS3856.1, 2007. a
Medeiros, B., Deser, C., Tomas, R. A., and Kay, J. E.: Arctic inversion
strength in climate models, J. Climate, 24, 4733–4740,
https://doi.org/10.1175/2011JCLI3968.1, 2011. a
Moene, A. F., Van de Wiel, B. J. H., and Jonker, H. J. J.: Local similarity
profiles from direct numerical simulation, in: Preprints, 19th Symp. on
Boundary Layers and Turbulence, Keystone, CO, Amer. Meteor. Soc. A, vol. 3,
2010. a
Monahan, A. H., He, Y., McFarlane, N., and Dai, A.: The Probability
Distribution of Land Surface Wind Speeds, J. Climate, 24, 3892–3909,
https://doi.org/10.1175/2011JCLI4106.1, 2011. a
Monahan, A. H., Rees, T., He, Y., and McFarlane, N.: Multiple Regimes of Wind,
Stratification, and Turbulence in the Stable Boundary Layer, J. Atmos. Sci.,
72, 3178–3198, https://doi.org/10.1175/JAS-D-14-0311.1, 2015. a, b, c
Monin, A. and Obukhov, A.: Basic laws of turbulent mixing in the surface layer
of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR, 151, 163–187,
1954. a
Nappo, C., Sun, J., Mahrt, L., and Belušić, D.: Determining Wave–Turbulence
Interactions in the Stable Boundary Layer, B. Am. Meteor. Soc., 95,
ES11–ES13, https://doi.org/10.1175/BAMS-D-12-00235.1, 2014. a
Nappo, C. J.: Sporadic breakdowns of stability in the PBL over simple and
complex terrain, Bound.-Lay. Meteorol., 54, 69–87,
https://doi.org/10.1007/BF00119413, 1991. a
Newsom, R. K. and Banta, R. M.: Shear-Flow Instability in the Stable Nocturnal
Boundary Layer as Observed by Doppler Lidar during CASES-99, J. Atmos. Sci.,
60, 16–33, https://doi.org/10.1175/1520-0469(2003)060<0016:SFIITS>2.0.CO;2, 2003. a
O'Brien, T. A., Collins, D. W., Rauscher, A. S., and Ringler, T. D.: Reducing the
computational cost of the ECF using a nuFFT: A fast and objective probability
density estimation method, Comput. Stat. Data Anal., 79, 222–234,
https://doi.org/10.1016/j.csda.2014.06.002, 2014. a
O'Brien, T. A., Kashinath, K., Cavanaugh, N. R., Collins, D. W., and O'Brien,
J. P.: A fast and objective multidimensional kernel density estimation
method: fastKDE, Comput. Stat. Data Anal., 101, 148–160,
https://doi.org/10.1016/j.csda.2016.02.014, 2016. a
O'Kane, T. J., Risbey, J. S., Franzke, C., Horenko, I., and Monselesan, D. P.:
Changes in the Metastability of the Midlatitude Southern Hemisphere
Circulation and the Utility of Nonstationary Cluster Analysis and Split-Flow
Blocking Indices as Diagnostic Tools, J. Atmos. Sci.,
70, 824–842, https://doi.org/10.1175/JAS-D-12-028.1, 2013. a
Optis, M., Monahan, A., and Bosveld, F. C.: Limitations and breakdown of
Monin–Obukhov similarity theory for wind profile extrapolation under stable
stratification, Wind Energy, 19, 1053–1072, https://doi.org/10.1002/we.1883, 2015. a
Pahlow, M., Parlange, M. B., and Porté-Agel, F.: On Monin–Obukhov
Similarity In The Stable Atmospheric Boundary Layer, Bound.-Lay. Meteorol.,
99, 225–248, https://doi.org/10.1023/A:1018909000098, 2001. a
Prabha, T., Hoogenboom, G., and Smirnova, T.: Role of land surface
parameterizations on modeling cold-pooling events and low-level jets, Atmos.
Res., 99, 147–161, https://doi.org/10.1016/j.atmosres.2010.09.017, 2011. a
Rabiner, L. R.: A tutorial on hidden Markov models and selected applications
in speech recognition, Proc. IEEEE, 77, 257–286, https://doi.org/10.1109/5.18626,
1989. a, b
Rees, J. M. and Mobbs, S. D.: Studies of internal gravity waves at Halley
Base, Antarctica, using wind observations, Q. J. Roy. Meteorol. Soc., 114,
939–966, https://doi.org/10.1002/qj.49711448206, 1988. a
Rishel, J., Johnson, S., and Holt, D.: Meteorological monitoring at Los
Alamos. Los Alamos National Progress Report LA-UR-03-9097, Tech. rep., Los
Alamos National Laboratory, available at:
https://envweb.lanl.gov/weathermachine/downloads/LA-UR-03-8097_webcopy.pdf (last access: 12 November 2019), 2003. a
Salmond, J. A. and McKendry, I. G.: A review of turbulence in the very stable
nocturnal boundary layer and its implications for air quality, Prog.
Phys. Geogr., 29, 171–188, https://doi.org/10.1191/0309133305pp442ra, 2005. a
Sorbjan, Z.: On similarity in the atmospheric boundary layer, Bound.-Lay.
Meteorol., 34, 377–397, https://doi.org/10.1007/BF00120989, 1986. a
Staley, D. and Jurica, G.: Effective atmospheric emissivity under clear skies,
J. Appl. Meteorol., 11, 349–356,
https://doi.org/10.1175/1520-0450(1972)011<0349:EAEUCS>2.0.CO;2, 1972. a
Sterk, H. A. M., Steeneveld, G. J., and Holtslag, A. A. M.: The role of
snow-surface coupling, radiation, and turbulent mixing in modeling a stable
boundary layer over Arctic sea ice, J. Geophys. Res.-Atmos., 118,
1199–1217, https://doi.org/10.1002/jgrd.50158, 2013. a
Sterk, H. A. M., Steeneveld, G. J., Vihma, T., Anderson, P. S., Bosveld, F. C.,
and Holtslag, A. A. M.: Clear-sky stable boundary layers with low winds over
snow-covered surfaces, Part 1: WRF model evaluation, Q. J. Roy. Meteorol. Soc.,
141, 2165–2184, https://doi.org/10.1002/qj.2513, 2015. a
Storm, B. and Basu, S.: The WRF Model Forecast-Derived Low-Level Wind Shear
Climatology over the United States Great Plains, Energies, 3, 258–276,
https://doi.org/10.3390/en3020258, 2010. a
Sun, J., Burns, S. P., Lenschow, D. H., Banta, R., Newsom, R., Coulter, R.,
Frasier, S., Ince, T., Nappo, C., Cuxart, J., Blumen, W., Lee, X., and Hu,
X.-Z.: Intermittent Turbulence Associated with a Density Current Passage in
the Stable Boundary Layer, Bound.-Lay. Meteorol., 105, 199–219,
https://doi.org/10.1023/A:1019969131774, 2002. a
Sun, J., Lenschow, D. H., Burns, S. P., Banta, R. M., Newsom, R. K., Coulter,
R., Frasier, S., Ince, T., Nappo, C., Balsley, B. B., Jensen, M., Mahrt, L.,
Miller, D., and Skelly, B.: Atmospheric Disturbances that Generate
Intermittent Turbulence in Nocturnal Boundary Layers, Bound.-Lay.
Meteorol., 110, 255–279, https://doi.org/10.1023/A:1026097926169, 2004. a
Sun, J., Mahrt, L., Banta, R. M., and Pichugina, Y. L.: Turbulence Regimes and
Turbulence Intermittency in the Stable Boundary Layer during CASES-99, J.
Atmos. Sci., 69, 338–351, https://doi.org/10.1175/JAS-D-11-082.1, 2012. a, b
Sun, J., Mahrt, L., Nappo, C., and Lenschow, D. H.: Wind and Temperature
Oscillations Generated by Wave–Turbulence Interactions in the Stably
Stratified Boundary Layer, J. Atmos. Sci., 72, 1484–1503,
https://doi.org/10.1175/JAS-D-14-0129.1, 2015. a
Svensson, G. and Holtslag, A. A. M.: Analysis of model results for the turning
of the wind and related momentum fluxes in the stable boundary layer,
Bound.-Lay. Meteorol., 132, 261–277, https://doi.org/10.1007/s10546-009-9395-1, 2009. a
Tastula, E.-M., Vihma, T., and Andreas, E. L.: Evaluation of Polar WRF from
modeling the atmospheric boundary layer over antarctic sea ice in autumn and
winter, Mon. Weather Rev., 140, 3919–3935, https://doi.org/10.1175/MWR-D-12-00016.1,
2012. a
Tomas, J. M., Pourquie, M. J. B. M., and Jonker, H. J. J.: Stable
Stratification Effects on Flow and Pollutant Dispersion in Boundary Layers
Entering a Generic Urban Environment, Bound.-Lay. Meteorol., 159, 159–221,
https://doi.org/10.1007/s10546-015-0124-7, 2016. a
Ulden, A. P. V. and Wieringa, J.: Atmospheric boundary layer research at
Cabauw, Bound.-Lay. Meteorol., 78, 39–69, https://doi.org/10.1007/BF00122486, 1996. a
van de Wiel, B. J. H., Moene, A. F., Steeneveld, G. J., Hartogensis, O. K., and
Holtslag, A. A. M.: Predicting the Collapse of Turbulence in Stably
Stratified Boundary Layers, Flow Turbul. Combust., 79, 251–274,
https://doi.org/10.1007/s10494-007-9094-2, 2007. a
van de Wiel, B. J. H., Moene, A. F., and Jonker, H. J. J.: The Cessation of
Continuous Turbulence as Precursor of the Very Stable Nocturnal Boundary
Layer, J. Atmos. Sci., 69, 3097–3127, https://doi.org/10.1175/JAS-D-12-064.1, 2012. a
van de Wiel, B. J. H., Vignon, E., Baas, P., van Hooijdonk, I. G. S., van der
Linden, S. J. A., van Hooft, J. A., Bosveld, F. C., de Roode, S. R., Moene,
A. F., and Genthonc, C.: Regime Transitions in Near-Surface Temperature
Inversions: A Conceptual Model, J. Atmos. Sci., 74, 1057–1073,
https://doi.org/10.1175/JAS-D-16-0180.1, 2017. a, b, c, d
Van Driest, E. R.: On turbulent flow near a wall, J. Aeronaut.
Sci., 18, 145–160, 1951. a
van Hooijdonk, I., Moene, A., Scheffer, M., Clercx, H., and van de Wiel, B.:
Early Warning Signals for Regime Transition in the Stable Boundary Layer: A
Model Study, Bound.-Lay. Meteorol., 162, 283–306,
https://doi.org/10.1007/s10546-016-0199-9, 2017. a, b, c
van Hooijdonk, I. G. S., Donda, J. M. M., Bosveld, H. J. H. C. F. C., and
van de Wiel, B. J. H.: Shear Capacity as Prognostic for Nocturnal Boundary
Layer Regimes, J. Atmos. Sci., 72, 1518–1532,
https://doi.org/10.1175/JAS-D-14-0140.1, 2015. a
Vercauteren, N. and Klein, R.: A Clustering Method to Characterize
Intermittent Bursts of Turbulence and Interaction with Submesomotions in the
Stable Boundary Layer, J. Atmos. Sci., 72, 1504–1517,
https://doi.org/10.1175/JAS-D-14-0115.1, 2015. a, b, c, d
Vignon, E., Genthon, C., Barral, H., Amory, C., Picard, G., Gallée, H.,
Casasanta, G., and Argentini, S.: Momentum- and Heat-Flux Parametrization at
Dome C, Antarctica: A Sensitivity Study, Bound.-Lay. Meteorol., 162,
341–367, https://doi.org/10.1007/s10546-016-0192-3, 2017a.
a
Vignon, E., van de Wiel, B. J. H., van Hooijdonk, I. G. S., Genthon, C.,
van der Linden, S. J. A., van Hooft, J. A., Baas, P., Maurel, W.,
Traullé, O., and Casasanta, G.: Stable boundary-layer regimes at Dome C,
Antarctica: observation and analysis, Q. J. Roy. Meteorol. Soc., 143,
1241–1253, https://doi.org/10.1002/qj.2998, 2017b. a, b
Walsh, J. E., Chapman, W. L., Romanovsky, V., Christensen, J. H., and Stendel,
M.: Global Climate Model Performance over Alaska and Greenland, J. Climate,
21, 6156–6174, https://doi.org/10.1175/2008JCLI2163.1, 2008. a
Walters, J. T., McNider, R. T., Shi, X., Norris, W. B., and Christy, J. R.:
Positive surface temperature feedback in the stable nocturnal boundary layer,
Geophys. Res. Lett., 34, L12709, https://doi.org/10.1029/2007GL029505, 2007. a
Wenzel, A., Kalthoff, N., and Horlacher, V.: On the profiles of wind velocity
in the roughness sublayer above a coniferous forest, Bound.-Lay. Meteorol.,
84, 219–230, https://doi.org/10.1023/A:1000444911103, 1997. a
Westerhellweg, A. and Neumann, T.: FINO1 Mast Correction, DEWI Magazin, 40,
60–66, 2012. a
Williams, A. G., Chambers, S., and Griffiths, A.: Bulk Mixing and Decoupling
of the Nocturnal Stable Boundary Layer Characterized Using a Ubiquitous
Natural Tracer, Bound.-Lay. Meteorol., 149, 381–402,
https://doi.org/10.1007/s10546-013-9849-3, 2013. a, b
Zhou, B. and Chow, F. K.: Turbulence Modeling for the Stable Atmospheric
Boundary Layer and Implications for Wind Energy, Flow Turbul.
Combust., 88, 255–277, https://doi.org/10.1007/s10494-011-9359-7, 2012. a
Zilitinkevich, S. S., Elperin, T., Kleeorin, N., Rogachevskii, I., Esau, I.,
Mauritsen, T., and Miles, M. W.: Turbulence energetics in stably stratified
geophysical flows: Strong and weak mixing regimes, Q. J. Roy. Meteorol. Soc.,
134, 793–799, https://doi.org/10.1002/qj.264, 2008. a
Short summary
Atmospheric stably stratified boundary layers display transitions between regimes of sustained and intermittent turbulence. These transitions are not well represented in numerical weather prediction and climate models. A prototype explicitly stochastic turbulence parameterization simulating regime dynamics is presented and tested in an idealized model. Results demonstrate that the approach can improve the regime representation in models.
Atmospheric stably stratified boundary layers display transitions between regimes of sustained...
