Articles | Volume 21, issue 20
https://doi.org/10.5194/acp-21-15699-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-15699-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimation of the terms acting on local 1 h surface temperature variations in Paris region: the specific contribution of clouds
Oscar Javier Rojas Muñoz
CORRESPONDING AUTHOR
LATMOS/IPSL, UVSQ, Université Paris-Saclay, Sorbonne
Université, CNRS, 78280, Guyancourt, France
Marjolaine Chiriaco
LATMOS/IPSL, UVSQ, Université Paris-Saclay, Sorbonne
Université, CNRS, 78280, Guyancourt, France
Sophie Bastin
LATMOS/IPSL, UVSQ, Université Paris-Saclay, Sorbonne
Université, CNRS, 78280, Guyancourt, France
Justine Ringard
LATMOS/IPSL, UVSQ, Université Paris-Saclay, Sorbonne
Université, CNRS, 78280, Guyancourt, France
deceased, 21 May 2021
Related authors
No articles found.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary
Short summary
This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem Feofilov, Marie-Laure Roussel, Benjamin Witschas, and Sophie Bastin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2065, https://doi.org/10.5194/egusphere-2025-2065, 2025
Short summary
Short summary
Aeolus spaceborne Doppler Wind Lidar observes perfectly co-located vertical profiles of clouds and vertical profiles of horizontal wind that can be used to study cloud-wind interactions. At regional scale, we show that over the Indian Ocean, high cloud fractions increase when the Tropical Easterly Jet is active. At a smaller scale, we observe for the first time from space differences in the wind profiles within the cloud and its surrounding clear sky, that can be imputed to convective motions.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
Short summary
Short summary
We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
Short summary
Short summary
Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Cited articles
Al-Hinti, I., Al-Muhtady, A., and Al-Kouz, W.: Measurement and modelling of
the ground temperature profile in Zarqa, Jordan for geothermal heat pump
applications, Appl. Therm. Eng., 123, 131–137,
https://doi.org/10.1016/j.applthermaleng.2017.05.107, 2017.
Allan, R. P.: Combining satellite data and models to estimate cloud
radiative effect at the surface and in the atmosphere, Meteorol. Appl., 18, 324–333,
https://doi.org/10.1002/met.285, 2011.
Allan, R. P., Liu, C., Loeb, N. G., Palmer, M. D., Roberts, M., Smith, D.,
and Vidale, P.-L.: Changes in global net radiative imbalance 1985–2012,
Geophys. Res. Lett., 41, 5588–5597, https://doi.org/10.1002/2014GL060962,
2014.
Archer, K. J. and Kimes, R. V.: Empirical characterization of random forest
variable importance measures, Comput. Stat. Data An.,
52, 2249–2260, https://doi.org/10.1016/j.csda.2007.08.015, 2008.
Arkin, P. A. and Meisner, B. N.: The Relationship between Large-Scale
Convective Rainfall and Cold Cloud over the Western Hemisphere during
1982–84, Mon. Weather Rev., 115, 51–74,
https://doi.org/10.1175/1520-0493(1987)115<0051:TRBLSC>2.0.CO;2, 1987.
Arnold, N. S., Willis, I. C., Sharp, M. J., Richards, K. S., and Lawson, W.
J.: A distributed surface energy-balance model for a small valley glacier.
I. Development and testing for Haut Glacier d'Arolla, Valais, Switzerland,
42, 77–89, https://doi.org/10.3189/S0022143000030549, 1996.
Bartolini, E., Claps, P., and D'Odorico, P.: Interannual variability of winter precipitation in the European Alps: relations with the North Atlantic Oscillation., Hydrol. Earth Syst. Sci., 13, 17–25, https://doi.org/10.5194/hess-13-17-2009, 2009.
Bastin, S., Chiriaco, M., and Drobinski, P.: Control of radiation and
evaporation on temperature variability in a WRF regional climate simulation:
comparison with colocated long term ground based observations near Paris,
Clim. Dynam,, 51, 985–1003, https://doi.org/10.1007/s00382-016-2974-1, 2018.
Bennartz, R., Shupe, M. D., Turner, D. D., Walden, V. P., Steffen, K., Cox,
C. J., Kulie, M. S., Miller, N. B., and Pettersen, C.: July 2012 Greenland
melt extent enhanced by low-level liquid clouds, Nature, 496, 83–86,
https://doi.org/10.1038/nature12002, 2013.
Broeke, M. V. D., Reijmer, C., As, D. V., and Boot, W.: Daily cycle of the
surface energy balance in Antarctica and the influence of clouds, Int. J. Climatol. 26,
1587–1605, https://doi.org/10.1002/joc.1323, 2006.
Brownlee, J.: Bagging and Random Forest Ensemble Algorithms for Machine
Learning, Machine Learning Mastery, available at: https://machinelearningmastery.com/bagging-and-random-forest-ensemble-algorithms-for-machine-learning/ (last access: 18 October 2021), 2016.
Chakroun, M., Bastin, S., Chiriaco, M., and Chepfer, H.: Characterization of
vertical cloud variability over Europe using spatial lidar observations and
regional simulation, Clim. Dynam., 51, 813–835,
https://doi.org/10.1007/s00382-016-3037-3, 2018.
Chen, T., Rossow, W. B., and Zhang, Y.: Radiative Effects of Cloud-Type
Variations, J. Climate, 13, 264–286,
https://doi.org/10.1175/1520-0442(2000)013<0264:REOCTV>2.0.CO;2, 2000.
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P.,
Stubenrauch, C. J., and Zeng, S.: The GCM-Oriented CALIPSO Cloud Product
(CALIPSO-GOCCP), 115, D00H16, https://doi.org/10.1029/2009JD012251, 2010.
Cheruy, F., Campoy, A., Dupont, J.-C., Ducharne, A., Hourdin, F., Haeffelin,
M., Chiriaco, M., and Idelkadi, A.: Combined influence of atmospheric
physics and soil hydrology on the simulated meteorology at the SIRTA
atmospheric observatory, Clim. Dynam., 40, 2251–2269,
https://doi.org/10.1007/s00382-012-1469-y, 2013.
Cherviakov, M.: Variability of Earth's radiation budget components during
2009–2015 from radiometer IKOR-M data,
Geophys. Res. Abstr.,
EGU2016-9611, EGU General Assembly 2016, Vienna, Austria, 2016.
Chiriaco, M., Bastin, S., Yiou, P., Haeffelin, M., Dupont, J.-C., and
Stéfanon, M.: European heatwave in July 2006: Observations and modeling
showing how local processes amplify conducive large-scale conditions, Geophys. Res. Lett., 41,
5644–5652, https://doi.org/10.1002/2014GL060205, 2014.
Chiriaco, M., Dupont, J.-C., Bastin, S., Badosa, J., Lopez, J., Haeffelin, M., Chepfer, H., and Guzman, R.: ReOBS: a new approach to synthesize long-term multi-variable dataset and application to the SIRTA supersite, Earth Syst. Sci. Data, 10, 919–940, https://doi.org/10.5194/essd-10-919-2018, 2018.
SIRTA: The SIRTA-ReOBS Dataset, SIRTA [data set], https://doi.org/10.14768/4F63BAD4-E6AF-4101-AD5A-61D4A34620DE, last access: 10 April 2021.
Conangla, L., Cuxart, J., Jiménez, M. A., Martínez-Villagrasa, D.,
Miró, J. R., Tabarelli, D., and Zardi, D.: Cold-air pool evolution in a
wide Pyrenean valley, Int. J. Climatol., 38, 2852–2865, https://doi.org/10.1002/joc.5467,
2018.
Copernicus Climate Change Service: ERA5-Land hourly data from 2001 to
present, Climate Data Store [data set], https://doi.org/10.24381/CDS.E2161BAC, 2019.
ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Climate Data Store [data set], available at:
https://cds.climate.copernicus.eu/cdsapp#!/home, last access: 3
February 2020.
Dai, A., Trenberth, K. E., and Karl, T. R.: Effects of Clouds, Soil
Moisture, Precipitation, and Water Vapor on Diurnal Temperature Range, 12,
23, 2451–2473, https://doi.org/10.1175/1520-0442(1999)012<2451:EOCSMP>2.0.CO;2, 1999.
Dewitte, S. and Clerbaux, N.: Measurement of the Earth Radiation Budget at
the Top of the Atmosphere – A Review, Remote Sens., 9, 1143,
https://doi.org/10.3390/rs9111143, 2017.
Dione, C., Lohou, F., Chiriaco, M., Lothon, M., Bastin, S., Baray, J.-L.,
Yiou, P., and Colomb, A.: The Influence of Synoptic Circulations and Local
Processes on Temperature Anomalies at Three French Observatories, J. Appl.
Meteorol. Clim., 56, 141–158, https://doi.org/10.1175/JAMC-D-16-0113.1,
2017.
Dufresne, J.-L., Gautier, C., Ricchiazzi, P., and Fouquart, Y.: Longwave
Scattering Effects of Mineral Aerosols, J. Atmos. Sci., 59, 1959–1966,
https://doi.org/10.1175/1520-0469(2002)059<1959:LSEOMA>2.0.CO;2, 2002.
Efthymiadis, D., Goodess, C. M., and Jones, P. D.: Trends in Mediterranean gridded temperature extremes and large-scale circulation influences, Nat. Hazards Earth Syst. Sci., 11, 2199–2214, https://doi.org/10.5194/nhess-11-2199-2011, 2011.
Gaevskaya, G. N., Kondrati'ev, K. Y., and Yakushevskaya, K. E.: Radiative
heat flux divergence and heat regime in the lowest layer of the atmosphere,
Arch. Met. Geoph. Biokl. B., 12, 95–108,
https://doi.org/10.1007/BF02317955, 1962.
Genuer, R., Poggi, J.-M., and Tuleau-Malot, C.: Variable selection using
Random Forests,
31, 2225–2236, https://doi.org/10.1016/j.patrec.2010.03.014, 2012.
Grömping, U.: Estimators of Relative Importance in Linear Regression
Based on Variance Decomposition, Am. Stat., 61, 139–147,
https://doi.org/10.1198/000313007X188252, 2007.
Haeffelin, M., Barthès, L., Bock, O., Boitel, C., Bony, S., Bouniol, D., Chepfer, H., Chiriaco, M., Cuesta, J., Delanoë, J., Drobinski, P., Dufresne, J.-L., Flamant, C., Grall, M., Hodzic, A., Hourdin, F., Lapouge, F., Lemaître, Y., Mathieu, A., Morille, Y., Naud, C., Noël, V., O'Hirok, W., Pelon, J., Pietras, C., Protat, A., Romand, B., Scialom, G., and Vautard, R.: SIRTA, a ground-based atmospheric observatory for cloud and aerosol research, Ann. Geophys., 23, 253–275, https://doi.org/10.5194/angeo-23-253-2005, 2005.
Hakuba, M. Z., Folini, D., Sanchez-Lorenzo, A., and Wild, M.: Spatial
representativeness of ground-based solar radiation measurements, J. Geophys. Res.-Atmos., 118,
8585–8597, https://doi.org/10.1002/jgrd.50673, 2013.
Hartmann, D. L.: Chapter 6 Radiative Effects of Clouds on Earth's Climate,
in: International Geophysics, Vol. 54, Elsevier, 151–173,
https://doi.org/10.1016/S0074-6142(08)60215-6, 1993.
Hartmann, D. L., Ockert-Bell, M. E., and Michelsen, M. L.: The Effect of
Cloud Type on Earth's Energy Balance: Global Analysis, J. Climate,
5, 1281–1304, https://doi.org/10.1175/1520-0442(1992)005<1281:TEOCTO>2.0.CO;2, 1992.
Ionita, M., Lohmann, G., Rimbu, N., and Scholz, P.: Dominant modes of
Diurnal Temperature Range variability over Europe and their relationships
with large-scale atmospheric circulation and sea surface temperature anomaly
patterns: VARIABILITY OF THE SEASONAL DTR, J. Geophys. Res., 117, D15111,
https://doi.org/10.1029/2011JD016669, 2012.
Ionita, M., Boronean, C., and Chelcea, S.: Seasonal modes of dryness and
wetness variability over Europe and their connections with large scale
atmospheric circulation and global sea surface temperature, Clim. Dynam., 45,
2803–2829, https://doi.org/10.1007/s00382-015-2508-2, 2015.
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An Introduction to
Statistical Learning, Springer New York, New York, NY,
https://doi.org/10.1007/978-1-4614-7138-7, 2013.
Kato, S., Loeb, N. G., Rutan, D. A., Rose, F. G., Sun-Mack, S., Miller, W.
F., and Chen, Y.: Uncertainty Estimate of Surface Irradiances Computed with
MODIS-, CALIPSO-, and CloudSat-Derived Cloud and Aerosol Properties, Surv.
Geophys., 33, 395–412, https://doi.org/10.1007/s10712-012-9179-x, 2012.
Kauppinen, J., Heinonen, J., and Malmi, P.: Influence of Relative Humidity
and Clouds on the Global Mean Surface Temperature, Energ. Environ.,
25, 389–399, https://doi.org/10.1260/0958-305X.25.2.389, 2014.
Kukla, G. and Karl, T. R.: Nighttime warming and the greenhouse effect,
Environ. Sci. Technol., 27, 1468–1474, https://doi.org/10.1021/es00045a001,
1993.
Kushta, J., Kallos, G., Astitha, M., Solomos, S., Spyrou, C., Mitsakou, C.,
and Lelieveld, J.: Impact of natural aerosols on atmospheric radiation and
consequent feedbacks with the meteorological and photochemical state of the
atmosphere, J. Geophys. Res.-Atmos., 119, 1463–1491, https://doi.org/10.1002/2013JD020714, 2014.
Li, Z. and Leighton, H. G.: Global climatologies of solar radiation budgets
at the surface and in the atmosphere from 5 years of ERBE data, J. Geophys. Res.-Atmos., 98,
4919–4930, https://doi.org/10.1029/93JD00003, 1993.
Llasat, M. C. and Puigcerver, M.: Cold air pools over Europe, Meteorol.
Atmos. Phys., 42, 171–177, https://doi.org/10.1007/BF01314823, 1990.
Loh, W.-Y.: Regression Trees With Unbiased Variable Selection and
Interaction Detection, Stat. Sinica, 12, 361–386, 2002.
Malardel, S.: Fondamentaux de Météorologie, 2nd Edn.,
Cépaduès-éditions, Toulouse, 2009.
Manish: Tree Based Algorithms: A Complete Tutorial from Scratch (in R &
Python), Analytics Vidhya, available at: https://www.analyticsvidhya.com/blog/2016/04/tree-based-algorithms-complete-tutorial-scratch-in-python/ (last access: 18 October 2021), 2016.
Mariotti, A., Pan, Y., Zeng, N., and Alessandri, A.: Long-term climate
change in the Mediterranean region in the midst of decadal variability, Clim.
Dynam., 44, 1437–1456, https://doi.org/10.1007/s00382-015-2487-3, 2015.
McNider, R. T., Steeneveld, G. J., Holtslag, A. A. M., Pielke Sr., R. A., Mackaro, S., Pour-Biazar, A., Walters, J., Nair, U., and Christy, J.: Response and sensitivity of the nocturnal boundary layer over land to added longwave radiative forcing, J. Geophys. Res.-Atmos., 117, D14106, https://doi.org/10.1029/2012JD017578, 2012.
Miao, S., Dou, J., Chen, F., Li, J., and Li, A.: Analysis of observations on
the urban surface energy balance in Beijing, Sci. China Earth Sci., 55,
1881–1890, https://doi.org/10.1007/s11430-012-4411-6, 2012.
Miller, N. B., Shupe, M. D., Cox, C. J., Noone, D., Persson, P. O. G., and Steffen, K.: Surface energy budget responses to radiative forcing at Summit, Greenland, The Cryosphere, 11, 497–516, https://doi.org/10.5194/tc-11-497-2017, 2017.
Pal, S. and Haeffelin, M.: Forcing mechanisms governing diurnal, seasonal,
and interannual variability in the boundary layer depths: Five years of
continuous lidar observations over a suburban site near Paris, 120,
11936–11956, https://doi.org/10.1002/2015JD023268, 2015.
Parding, K., Olseth, J. A., Dagestad, K. F., and Liepert, B. G.: Decadal
variability of clouds, solar radiation and temperature at a high-latitude
coastal site in Norway, Tellus B, 66, 25897,
https://doi.org/10.3402/tellusb.v66.25897, 2014.
Pinardi, N. and Masetti, E.: Variability of the large scale general
circulation of the Mediterranean Sea from observations and modelling: a
review, Palaeogeogr. Palaeocl., 158, 153–173,
https://doi.org/10.1016/S0031-0182(00)00048-1, 2000.
Popiel, C. O. and Wojtkowiak, J.: Temperature distributions of ground in the
urban region of Poznan City, Exp. Therm. Fluid Sci., 51,
135–148, https://doi.org/10.1016/j.expthermflusci.2013.07.009, 2013.
Raschke, E., Ohmura, A., Rossow, W. B., Carlson, B. E., Zhang, Y.-C.,
Stubenrauch, C., Kottek, M., and Wild, M.: Cloud effects on the radiation
budget based on ISCCP data (1991 to 1995), Int. J. Climatol., 25, 1103–1125,
https://doi.org/10.1002/joc.1157, 2005.
Rebetez, M., Dupont, O., and Giroud, M.: An analysis of the July 2006
heatwave extent in Europe compared to the record year of 2003, Theor. Appl.
Climatol., 95, 1–7, https://doi.org/10.1007/s00704-007-0370-9, 2009.
Satheesh, S. K. and Krishna Moorthy, K.: Radiative effects of natural
aerosols: A review, Atmos. Environ., 39, 2089–2110,
https://doi.org/10.1016/j.atmosenv.2004.12.029, 2005.
Shi, X., McNider, R. T., Singh, M. P., England, D. E., Friedman, M. J.,
Lapenta, W. M., and Norris, W. B.: On the Behavior of the Stable Boundary
Layer and the Role of Initial Conditions, Pure Appl. Geophys., 162,
1811–1829, https://doi.org/10.1007/s00024-005-2694-7, 2005.
Stapf, J., Ehrlich, A., Jäkel, E., Lüpkes, C., and Wendisch, M.: Reassessment of shortwave surface cloud radiative forcing in the Arctic: consideration of surface-albedo–cloud interactions, Atmos. Chem. Phys., 20, 9895–9914, https://doi.org/10.5194/acp-20-9895-2020, 2020.
Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T.: Bias in random
forest variable importance measures: Illustrations, sources and a solution,
BMC Bioinformatics, 8, 25, https://doi.org/10.1186/1471-2105-8-25, 2007.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Springer
Netherlands, https://doi.org/10.1007/978-94-009-3027-8, 1988.
Trigo, R. M., Osborn, T. J., and Corte-Real, J. M.: The North Atlantic
Oscillation influence on Europe: climate impacts and associated physical
mechanisms, Clim. Res., 20, 9–17, 2002.
Vautard, R. and Yiou, P.: Control of recent European surface climate change
by atmospheric flow, Geophys. Res. Lett., 36, L22702, https://doi.org/10.1029/2009GL040480, 2009.
Wallace, J. and Hobbs, P.: Atmospheric Science – An Introduction Survey, 2nd
Edn., Academic Press, Burlington, MA, 504 pp., 2006.
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.
Wang, K. and Dickinson, R. E.: Contribution of solar radiation to decadal
temperature variability over land, P. Natl. Acad. Sci. USA, 110, 14877–14882,
https://doi.org/10.1073/pnas.1311433110, 2013.
Wang, Y., Xie, S.-P., Wang, B., and Xu, H.: Large-Scale Atmospheric Forcing
by Southeast Pacific Boundary Layer Clouds: A Regional Model Study, J. Climate, 18,
934–951, https://doi.org/10.1175/JCLI3302.1, 2005.
Wendisch, M., Macke, A., Ehrlich, A., Lüpkes, C., Mech, M., Chechin, D.,
Dethloff, K., Velasco, C. B., Bozem, H., Brückner, M., Clemen, H.-C.,
Crewell, S., Donth, T., Dupuy, R., Ebell, K., Egerer, U., Engelmann, R.,
Engler, C., Eppers, O., Gehrmann, M., Gong, X., Gottschalk, M., Gourbeyre,
C., Griesche, H., Hartmann, J., Hartmann, M., Heinold, B., Herber, A.,
Herrmann, H., Heygster, G., Hoor, P., Jafariserajehlou, S., Jäkel, E.,
Järvinen, E., Jourdan, O., Kästner, U., Kecorius, S., Knudsen, E.
M., Köllner, F., Kretzschmar, J., Lelli, L., Leroy, D., Maturilli, M.,
Mei, L., Mertes, S., Mioche, G., Neuber, R., Nicolaus, M., Nomokonova, T.,
Notholt, J., Palm, M., Pinxteren, M. van, Quaas, J., Richter, P.,
Ruiz-Donoso, E., Schäfer, M., Schmieder, K., Schnaiter, M., Schneider,
J., Schwarzenböck, A., Seifert, P., Shupe, M. D., Siebert, H., Spreen,
G., Stapf, J., Stratmann, F., Vogl, T., Welti, A., Wex, H., Wiedensohler,
A., Zanatta, M., and Zeppenfeld, S.: The Arctic Cloud Puzzle: Using
ACLOUD/PASCAL Multiplatform Observations to Unravel the Role of Clouds and
Aerosol Particles in Arctic Amplification, B. Am. Meteorol. Soc., 100, 841–871,
https://doi.org/10.1175/BAMS-D-18-0072.1, 2019.
Wild, M.: Towards Global Estimates of the Surface Energy Budget, 3, 87–97,
https://doi.org/10.1007/s40641-017-0058-x, 2017.
Wild, M., Folini, D., Hakuba, M. Z., Schär, C., Seneviratne, S. I.,
Kato, S., Rutan, D., Ammann, C., Wood, E. F., and König-Langlo, G.: The
energy balance over land and oceans: an assessment based on direct
observations and CMIP5 climate models, Clim. Dynam., 44, 3393–3429,
https://doi.org/10.1007/s00382-014-2430-z, 2015.
Willson, R. C., Gulkis, S., Janssen, M., Hudson, H. S., and Chapman, G. A.:
Observations of Solar Irradiance Variability, Science, 211, 700–702,
https://doi.org/10.1126/science.211.4483.700, 1981.
Wulf, H., Mulder, T., Schaepman, M. E., Keller, A., Jörg, P. C., and
Schaepman, M. E.: Remote sensing of soils, Research Report, University of Zurich,
https://doi.org/10.5167/uzh-109992, 2015.
Xoplaki, E., González-Rouco, J., Gyalistras, D., Luterbacher, J.,
Rickli, R., and Wanner, H.: Interannual summer air temperature variability
over Greece and its connection to the large-scale atmospheric circulation
and Mediterranean SSTs 1950–1999, Clim. Dynam., 20, 537–554,
https://doi.org/10.1007/s00382-002-0291-3, 2003.
Xoplaki, E., González-Rouco, J. F., Luterbacher, J., and Wanner, H.: Wet
season Mediterranean precipitation variability: influence of large-scale
dynamics and trends, Clim. Dynam., 23, 63–78,
https://doi.org/10.1007/s00382-004-0422-0, 2004.
Short summary
A method is developed and evaluated to quantify each process that affects hourly 2 m temperature variations on a local scale, based almost exclusively on observations retrieved from an observatory near the Paris region. Each term involved in surface temperature variations is estimated, and its contribution and importance are also assessed. It is found that clouds are the main modulator on hourly temperature variations for most hours of the day, and thus their characterization is addressed.
A method is developed and evaluated to quantify each process that affects hourly 2 m temperature...
Altmetrics
Final-revised paper
Preprint