Articles | Volume 18, issue 2
https://doi.org/10.5194/bg-18-573-2021
© 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/bg-18-573-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jan 2021
Research article |
| 26 Jan 2021
| 26 Jan 2021
Climatic traits on daily clearness and cloudiness indices
Departamento de Geociencias y Medio Ambiente, Universidad Nacional de Colombia, Medellín, Colombia
Andrés Ochoa
Departamento de Geociencias y Medio Ambiente, Universidad Nacional de Colombia, Medellín, Colombia
Related authors
Carlos A. Sierra and Estefanía Muñoz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1640, https://doi.org/10.5194/egusphere-2025-1640, 2025
Short summary
Short summary
We propose an approach to obtain weights for calculating averages of variables from Earth system models (ESM) based on concepts from information theory. It quantifies a relative distance between model output and reality, even though it is impossible to know the absolute distance from model predictions to reality. The relative ranking among models is based on concepts of model selection and multi-model averages previously developed for simple statistical models, but adapted here for ESMs.
Estefanía Muñoz, Andrés Ochoa, and Germán Poveda
EGUsphere, https://doi.org/10.5194/egusphere-2022-119, https://doi.org/10.5194/egusphere-2022-119, 2022
Preprint archived
Short summary
Short summary
Rodriguez-Iturbe et al. in 1999 presented a model to analytically describe soil moisture dynamics for water-limited ecosystems. In this paper, we extend the model to energy-limited ecosystems by introducing the dependence of maximum evapotranspiration on radiation, and we model this relationship through a negative exponential equation. We illustrate the extended model with two study cases and evaluate the sensibility of soil moisture and the long-term water balance to available energy.
Carlos A. Sierra and Estefanía Muñoz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1640, https://doi.org/10.5194/egusphere-2025-1640, 2025
Short summary
Short summary
We propose an approach to obtain weights for calculating averages of variables from Earth system models (ESM) based on concepts from information theory. It quantifies a relative distance between model output and reality, even though it is impossible to know the absolute distance from model predictions to reality. The relative ranking among models is based on concepts of model selection and multi-model averages previously developed for simple statistical models, but adapted here for ESMs.
Estefanía Muñoz, Andrés Ochoa, and Germán Poveda
EGUsphere, https://doi.org/10.5194/egusphere-2022-119, https://doi.org/10.5194/egusphere-2022-119, 2022
Preprint archived
Short summary
Short summary
Rodriguez-Iturbe et al. in 1999 presented a model to analytically describe soil moisture dynamics for water-limited ecosystems. In this paper, we extend the model to energy-limited ecosystems by introducing the dependence of maximum evapotranspiration on radiation, and we model this relationship through a negative exponential equation. We illustrate the extended model with two study cases and evaluate the sensibility of soil moisture and the long-term water balance to available energy.
Cited articles
Allen, R. G., Trezza, R., and Tasumi, M.: Analytical integrated functions for
daily solar radiation on slopes, Agr. Forest Meteorol., 139,
55–73, https://doi.org/10.1016/j.agrformet.2006.05.012, 2006. a
Anthoni, P., Knohl, A., Rebmann, C., Freibauer, A., Mund, M., Ziegler, W.,
Kolle, O., and Schulze, E.: Forest and agricultural land-use-dependent CO2
exchange in Thuringia, Germany, Glob. Change Biol., 10, 2005–2019,
https://doi.org/10.1111/j.1365-2486.2004.00863.x, 2004a. a
Anthoni, P. M., Freibauer, A., Kolle, O., and Schulze, E.-D.: Winter wheat
carbon exchange in Thuringia, Germany, Agr. Forest Meteorol.,
121, 55–67, https://doi.org/10.1016/S0168-1923(03)00162-X,
2004b. a
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A.,
Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W.,
Paw, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala,
T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the Temporal and
Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and
Energy Flux Densities, B. Am. Meteorol. Soc., 82,
2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001. a
Bendt, P., Collares-Pereira, M., and Rabl, A.: The frequency distribution of
daily insolation values, Sol. Energy, 27, 1–5,
https://doi.org/10.1016/0038-092X(81)90013-X, 1981. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh,
S., Sherwood, S., Stevens, B., and Zhang, X.: Clouds and Aerosols, in:
Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Stocker, T., Qin, D., Plattner, G.-K., Tignor, M.,
Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., 571–657, Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA, 2013. a
Cañada, J., Pedros, G., and Bosca, J.: Relationships between UV
(0.290–0.385 µm) and broad band solar radiation hourly values in
Valencia and Córdoba, Spain, Energy, 28, 199–217, 2003. a
Cermak, J., Eastman, R. M., Bendix, J., and Warren, S. G.: European
climatology of fog and low stratus based on geostationary satellite
observations, Q. J. Roy. Meteor. Soc., 135,
2125–2130, https://doi.org/10.1002/qj.503, 2009. a
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. a
Daly, E., Porporato, A., and Rodríguez-Iturbe, I.: Coupled Dynamics of
Photosynthesis, Transpiration, and Soil Water Balance. Part I: Upscaling from
Hourly to Daily Level, J. Hydrometeorol., 5, 546–558,
https://doi.org/10.1175/1525-7541(2004)005<0546:CDOPTA>2.0.CO;2,
2004a. a
Daly, E., Porporato, A., and Rodríguez-Iturbe, I.: Coupled Dynamics of
Photosynthesis, Transpiration, and Soil Water Balance. Part II: Stochastic
Analysis and Ecohydrological Significance, J. Hydrometeorol., 5,
559–566, https://doi.org/10.1175/1525-7541(2004)005<0559:CDOPTA>2.0.CO;2,
2004b. a
Dodge, Y.: The Concise Encyclopedia of Statistics, Springer New York, New
York, NY, https://doi.org/10.1007/978-0-387-32833-1, 2008. a
D'Odorico, P., Ridolfi, L., Porporato, A., and Rodríguez-Iturbe, I.:
Preferential states of seasonal soil moisture: The impact of climate
fluctuations, Water Resour. Res., 36, 2209–2219,
https://doi.org/10.1029/2000WR900103, 2000. a
Egli, S., Thies, B., Drönner, J., Cermak, J., and Bendix, J.: A 10 year
fog and low stratus climatology for Europe based on Meteosat Second
Generation data, Q. J. Roy. Meteor. Soc., 143,
530–541, https://doi.org/10.1002/qj.2941, 2017. a
Engmann, S. and Cousineau, D.: Comparing distributions: the two-sample
Anderson–Darling test as an alternative to the Kolmogorov–Smirnov test,
Journal of Applied Quantitative Methods, 6, 1–17,
2011. a
Gordon, J. M. and Hochman, M.: On the random nature of solar radiation, Sol.
Energy, 32, 337–342, https://doi.org/10.1016/0038-092X(84)90276-7, 1984. a
Haberreiter, M., Schöll, M., Dudok de Wit, T., Kretzschmar, M., Misios,
S., Tourpali, K., and Schmutz, W.: A new observational solar irradiance
composite, J. Geophys. Res.-Space, 122, 5910–5930,
https://doi.org/10.1002/2016JA023492, 2017. a
Hansen, J. W.: Stochastic daily solar irradiance for biological modeling
applications, Agr. Forest Meteorol., 94, 53–63,
https://doi.org/10.1016/S0168-1923(99)00003-9, 1999. a
Harrouni, S.: Fractal Classification of Typical Meteorological Days from
Global Solar Irradiance: Application to Five Sites of Different Climates,
in: Modeling Solar Radiation at the Earth's Surface, edited by: Badescu, V.,
chap. 2, 29–55, Springer-Verlag Berlin Heidelberg, 2008. a
Holdridge, L. R.: Determination of World Plant Formations From Simple Climatic
Data, Science, 105, 367–368, https://doi.org/10.1126/science.105.2727.367, 1947. a
Holdridge, L. R.: Life zone ecology, Tropical Science Center, San Jose, Costa
Rica, 1967. a
Hollands, K. G. T. and Suehrcke, H.: A three-state model for the probability
distribution of instantaneous solar radiation, with applications, Sol.
Energy, 96, 103–112, https://doi.org/10.1016/j.solener.2013.07.007, 2013. a
Ianetz, A. and Kudish, A.: A method for determining the solar global and
defining the diffuse and beam irradiation on a clear day, in: Modeling Solar
Radiation at the Earth's Surface: Recent Advances, edited by: Badescu, V.,
chap. 4, 93–113, Springer-Verlag Berlin Heidelberg,
https://doi.org/10.1007/978-3-540-77455-6_4, 2008. a, b
Kasten, F. and Young, A. T.: Revised optical air mass tables and approximation
formula, Appl. Optics, 28, 4735, https://doi.org/10.1364/AO.28.004735, 1989. a
Knohl, A., Schulze, E. D., Kolle, O., and Buchmann, N.: Large carbon uptake by
an unmanaged 250-year-old deciduous forest in Central Germany, Agr. Forest Meteorol., 118, 151–167, https://doi.org/10.1016/S0168-1923(03)00115-1,
2003. a
Laio, F., Tamea, S., Ridolfi, L., D'Odorico, P., and Rodríguez-Iturbe,
I.: Ecohydrology of groundwater-dependent ecosystems: 1. Stochastic water
table dynamics, Water Resour. Res., 45, 1–13,
https://doi.org/10.1029/2008WR007292, 2009. a, b
Leckner, B.: The spectral distribution of solar radiation at the earth's
surface–elements of a model, Sol. Energy, 20, 143–150,
https://doi.org/10.1016/0038-092X(78)90187-1, 1978. a, b
Leemans, R.: Global Holdridge Life Zone Classifications, Digital Raster Data,
in: Global Ecosystems Database Version 2.0, NOAA National Geophysical Data
Center, Boulder, USA, 1992. a
Li, Z. and Trishchenko, A. P.: Quantifying Uncertainties in Determining SW
Cloud Radiative Forcing and Cloud Absorption due to Variability in
Atmospheric Conditions, J. Atmos. Sci., 58, 376–389,
https://doi.org/10.1175/1520-0469(2001)058<0376:QUIDSC>2.0.CO;2,
2001. a
Liu, B. Y. and Jordan, R. C.: The interrelationship and characteristic
distribution of direct, diffuse and total solar radiation, Sol. Energy, 4,
1–19, https://doi.org/10.1016/0038-092X(60)90062-1, 1960. a
Manzoni, S., Porporato, A., D'Odorico, P., Laio, F., and Rodriguez-Iturbe, I.: Soil nutrient cycles as a nonlinear dynamical system, Nonlin. Processes Geophys., 11, 589–598, https://doi.org/10.5194/npg-11-589-2004, 2004. a, b
Martinez-Lozano, J. A., Tena, F., and Utrillas, M. P.: Ratio of UV to global
broad band irradiation in Valencia, Spain, Int. J. Climatol., 19, 903–911, 1999. a
Mercado, L. M., Bellouin, N., Sitch, S., Boucher, O., Huntingford, C., Wild,
M., and Cox, P. M.: Impact of changes in diffuse radiation on the global
land carbon sink, Nature, 458, 1014–1017, https://doi.org/10.1038/nature07949, 2009. a
Muñoz, E.: Soil moisture dynamics in water- and energy-limited
ecosystems. Application to slope stability, Phd, Universidad Nacional de
Colombia,
available at: http://bdigital.unal.edu.co/73824/6/1128281996.2019.pdf (last access: 23 October 2020),
2019. a
Muñoz, E. and Ochoa, A.: Radiation, clearness and clear-day indices at 37 FLUXNET sites, HydroShare, https://doi.org/10.4211/hs.7962c298ce544953be8d1dd6dab63bb0, 2021. a
Muñoz, E., Ochoa, A., Poveda, G., and Rodríguez-Iturbe, I.:
Probabilistic soil moisture dynamics of water- and energy-limited
ecosystems, EarthArXiv, https://doi.org/10.31223/osf.io/au4tb, 2020. a
NASA: U.S. Standard Atmosphere, 1976, Tech. rep., NOAA, Washington, D.C.,
1976. a
Nordbotten, J. M., Rodriguez-Iturbe, I., and Celia, M. A.: Stochastic coupling
of rainfall and biomass dynamics, Water Resour. Res., 43, 1–7,
https://doi.org/10.1029/2006WR005068, 2007. a, b
Nyamsi, W. W., Blanc, P., Augustine, J. A., Arola, A., and Wald, L.: A new clear-sky method for assessing photosynthetically active radiation at the surface level, Atmosphere, 10, 219, https://doi.org/10.3390/ATMOS10040219, 2019. a
Oliphant, A., Susan, C., Grimmond, B., Schmid, H. P., and Wayson, C. A.:
Local-scale heterogeneity of photosynthetically active radiation (PAR),
absorbed PAR and net radiation as a function of topography, sky conditions
and leaf area index, Remote Sens. Environ., 103, 324–337,
https://doi.org/10.1016/j.rse.2005.09.021, 2006. a
Olseth, J. A. and Skartveit, A.: A probability density function for daily
insolation within the temperate storm belts, Sol. Energy, 33, 533–542,
https://doi.org/10.1016/0038-092X(84)90008-2, 1984. a
Olson, R., Holladay, S., Cook, R., Falge, E., Baldocchi, D., and Gu, L.:
FLUXNET. Database of fluxes, site characteristics, and flux-community
information, Tech. rep., Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, United States, https://doi.org/10.2172/1184413, 2004. a, b
Pearson, K.: X. On the criterion that a given system of deviations from the
probable in the case of a correlated system of variables is such that it can
be reasonably supposed to have arisen from random sampling, The London,
Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50,
157–175, https://doi.org/10.1080/14786440009463897, 1900. a
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007. a
Platt, U., Pfeilsticker, K., and Vollmer, M.: Radiation and Optics in the
Atmosphere, in: Springer Handbook of Lasers and Optics, edited by:
Träger, F., 1475–1517, Springer Berlin Heidelberg, Berlin,
Heidelberg, https://doi.org/10.1007/978-3-642-19409-2_23,
2012. a
Polo, J., Zarzalejo, L., and Ramírez, L.: Solar Radiation Derived from
Satellite Images, in: Modeling Solar Radiation at the Earth's Surface,
edited by: Badescu, V., chap. 18, 449–461, Springer-Verlag Berlin
Heidelberg, 2008. a
Porporato, A., D'Odorico, P., Laio, F., and Rodriguez-Iturbe, I.: Hydrologic
controls on soil carbon and nitrogen cycles. I. Modeling scheme, Adv. Water Resour., 26, 45–58, https://doi.org/10.1016/S0309-1708(02)00094-5, 2003. a, b
Ridolfi, L., D'Odorico, P., Laio, F., Tamea, S., and Rodriguez-Iturbe, I.:
Coupled stochastic dynamics of water table and soil moisture in bare soil
conditions, Water Resour. Res., 44, 1–11, https://doi.org/10.1029/2007WR006707,
2008. a
Robinson, N.: Solar Radiation, Elsevier, Amsterdam, 1966. a
Rösner, B., Egli, S., Thies, B., Beyer, T., Callies, D., Pauscher, L.,
and Bendix, J.: Fog and Low Stratus Obstruction of Wind Lidar Observations
in Germany – A Remote Sensing-Based Data Set for Wind Energy Planning,
Energies, 13, 3859, https://doi.org/10.3390/en13153859, 2020. a
Sager, J. C. and McFarlane, J. C.: Radiation, in: Growth Chamber Handbook,
edited by: Langhans, R. and Tibbitts, T., 1–30, NC-101 Committee on
Controlled Environment Technology and Use, Iowa State University, Ames, IA, USA, 1997. a
Schaffer, B. E., Nordbotten, J. M., and Rodriguez-Iturbe, I.: Plant biomass
and soil moisture dynamics: analytical results, P. R. Soc. A, 471, 20150179,
https://doi.org/10.1098/rspa.2015.0179,
2015. a
Schöll, M., Dudok de Wit, T., Kretzschmar, M., and Haberreiter, M.:
Making of a solar spectral irradiance dataset I: observations,
uncertainties, and methods, J. Space Weather Spac., 6, A14, https://doi.org/10.1051/swsc/2016007, 2016. a
Scholz, F. and Stephens, M.: K-Sample Anderson-Darling Tests, J. Am. Stat. Assoc., 82, 918–924, https://doi.org/10.2307/2288805, 1987. a
Skartveit, A. and Olseth, J. A.: The probability density and autocorrelation
of short-term global and beam irradiance, Sol. Energy, 49, 477–487,
https://doi.org/10.1016/0038-092X(92)90155-4, 1992.
a
Tamea, S., Laio, F., Ridolfi, L., and Rodriguez-Iturbe, I.: Crossing
properties for geophysical systems forced by Poisson noise, Geophys. Res. Lett., 38, 1–5, https://doi.org/10.1029/2011GL049074, 2011. a, b
Tran, V. L.: Stochastic models of solar radiation processes, Ph.D. thesis,
Université d'Orléans, Orléans, France, 2013. a
Utrillas, M. P., Marín, M. J., Esteve, A. R., Salazar, G., Suárez,
H., Gandía, S., and Martínez-Lozano, J. A.: Relationship between
erythemal UV and broadband solar irradiation at high altitude in Northwestern
Argentina, Energy, 162, 136–147, 2018. a
Vigroux, E.: Contribution à l'étude expérimentale de
l'absorption de l'ozone, Ann. Phys., 12, 709–762,
https://doi.org/10.1051/anphys/195312080709, 1953. a
Wallace, J. M. and Hobbs, P. V.: Atmospheric Science. An Introductory Survey,
Academic Press, 2 edn., San Diego, CA, USA, 2006. a
Wu, A., Song, Y., van Oosterom, E. J., and Hammer, G. L.: Connecting
Biochemical Photosynthesis Models with Crop Models to Support Crop
Improvement, Front. Plant Sci, 7, 1518, https://doi.org/10.3389/fpls.2016.01518,
2016. a
Short summary
We inspect for climatic traits in the shape of the PDF of the clear-day (c) and the clearness (k) indices at 37 FLUXNET sites for the SW and the PAR spectral bands. We identified three types of PDF, unimodal with low dispersion, unimodal with high dispersion and bimodal, with no difference in the PDF type between c and k at each site. We found that latitude, global climate zone and Köppen climate type have a weak relation and the Holdridge life zone a stronger relation with c and k PDF types.
We inspect for climatic traits in the shape of the PDF of the clear-day (c) and the clearness...
Altmetrics
Final-revised paper
Preprint