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Nature communications
Jan 27, 2020;11 (1): 522.

Soil structure is an important omission in Earth System Models.

Simone Fatichi, Dani Or, Robert Walko, Harry Vereecken, Michael H Young, Teamrat A Ghezzehei, Tomislav Hengl, Stefan Kollet, Nurit Agam, Roni Avissar
Author Information
  1. Simone Fatichi: Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland. simone.fatichi@ifu.baug.ethz.ch. ORCID
  2. Dani Or: Department of Environmental Science, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zurich, Switzerland. ORCID
  3. Robert Walko: Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA.
  4. Harry Vereecken: Agrosphere, Jülich Research Center, Kreis Düren, Rheinland, Germany.
  5. Michael H Young: Bureau of Economic Geology, The University of Texas at Austin, Austin, Texas, USA. ORCID
  6. Teamrat A Ghezzehei: Life and Environmental Sciences, University of California, Merced, Merced, California, USA. ORCID
  7. Tomislav Hengl: OpenGeoHub foundation, Wageningen, The Netherlands. ORCID
  8. Stefan Kollet: Agrosphere, Jülich Research Center, Kreis Düren, Rheinland, Germany.
  9. Nurit Agam: Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Beersheba, Israel. ORCID
  10. Roni Avissar: Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA.
PMID: 31988306 DOI: 10.1038/s41467-020-14411-z

Abstract

Most soil hydraulic information used in Earth System Models (ESMs) is derived from pedo-transfer functions that use easy-to-measure soil attributes to estimate hydraulic parameters. This parameterization relies heavily on soil texture, but overlooks the critical role of soil structure originated by soil biophysical activity. Soil structure omission is pervasive also in sampling and measurement methods used to train pedotransfer functions. Here we show how systematic inclusion of salient soil structural features of biophysical origin affect local and global hydrologic and climatic responses. Locally, including soil structure in models significantly alters infiltration-runoff partitioning and recharge in wet and vegetated regions. Globally, the coarse spatial resolution of ESMs and their inability to simulate intense and short rainfall events mask effects of soil structure on surface fluxes and climate. Results suggest that although soil structure affects local hydrologic response, its implications on global-scale climate remains elusive in current ESMs.

References

  1. Amundson, R., Guo, Y. & Gong, P. Soil diversity and land use in the United States. Ecosystems 6, 470–482 (2013). [DOI: 10.1007/s10021-002-0160-2]
  2. Schimel, J. P. Microbes and global carbon. Nat. Clim. Change 3, 867–868 (2013). [DOI: 10.1038/nclimate2015]
  3. Paustian, K. et al. Climate-smart soils. Nature 532, 49–57 (2016). [PMID: 27078564]
  4. Katul, G. G., Oren, R., Manzoni, S., Higgins, C. & Parlange, M. B. Evapotranspiration: a process driving mass transport and energy exchange in the soil-plant -atmosphere- climate system. Rev. Geophys. 50, RG3002 (2012). [DOI: 10.1029/2011RG000366]
  5. Fatichi, S., Pappas, C. & Ivanov, V. Y. Modelling plant-water interactions: an ecohydrological overview from the cell to the global scale. WIREs Water 3, 327–368 (2016). [DOI: 10.1002/wat2.1125]
  6. Raich, J. W. & Nadelhoffer, K. J. Belowground carbon allocation in forest ecosystems: global trends. Ecology 70, 1346–1354 (1989). [DOI: 10.2307/1938194]
  7. Cleveland, C. C. et al. Patterns of new versus recycled primary production in the terrestrial biosphere. Proc. Natl Acad. Sci. USA 110, 12733–12737 (2013). [PMID: 23861492]
  8. Avissar, R. & Pielke, R. A. A parameterization of heterogeneous land-surface for atmospheric numerical models and its impact on regional meteorology. Mon. Weather Rev. 117, 2113–2136 (1989). [DOI: 10.1175/1520-0493(1989)117<2113]
  9. Avissar, R. & Werth, D. Global hydroclimatological teleconnections resulting from tropical deforestation. J. Hydromet. 6, 134–145 (2005). [DOI: 10.1175/JHM406.1]
  10. Lobell, D. B., Bala, G. & Duffy, P. B. Biogeophysical impacts of cropland management changes on climate. Geophys. Res. Lett. 33, L06708, https://doi.org/10.1029/2005GL025492 (2006). [DOI: 10.1029/2005GL025492]
  11. Betts, A. K., Desjardins, R., Worth, D. & Cerkowniak, D. Impact of land-use change on the diurnal cycle climate of the Canadian Prairies. J. Geophys. Res. Atmos. 118, 11,996–12,011 (2013). [DOI: 10.1002/2013JD020717]
  12. Vick, E. S. K., Stoy, P. C., Tang, A. C. I. & Gerken, T. The surface-atmosphere exchange of carbon dioxide, water, and sensible heat across a dryland wheat-fallow rotation. Agr. Ecosyst. Environ. 232, 129–140 (2016). [DOI: 10.1016/j.agee.2016.07.018]
  13. Burakowski, E. et al. The role of surface roughness, albedo, and Bowen ratio on ecosystem energy balance in the Eastern United States. Agric. For. Meteorol. 249, 367–376 (2018). [DOI: 10.1016/j.agrformet.2017.11.030]
  14. Davin, E. L., Seneviratne, S. I., Ciais, P., Olioso, A. & Wang, T. Preferential cooling of hot extremes from cropland albedo management. Proc. Natl Acad. Sci. USA 111, 9757–9761 (2014). [PMID: 24958872]
  15. Leomordant, L., Gentine, P., Stéfanon, M., Drobinski, P. & Fatichi, S. Modification of land-atmosphere interactions by CO effects: implications for summer dryness and heatwave amplitude. Geophys. Res. Lett. 43, 10240–10248 (2016). [DOI: 10.1002/2016GL069896]
  16. Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO physiological forcing. Nat. Commun. 9, 1094 (2018). [PMID: 29545570]
  17. Liu, Y. & Avissar, R. A study of persistence in the land-atmosphere system using a General Circulation Model and observations. J. Clim. 12, 2139–2153 (1999). [DOI: 10.1175/1520-0442(1999)012<2139]
  18. Koster, R. D. et al. On the nature of soil moisture in land surface models. J. Clim. 22, 4322–4335 (2009). [DOI: 10.1175/2009JCLI2832.1]
  19. Lehmann, P., Merlin, O., Gentine, P. & Or, D. Soil texture effects on surface resistance to bare soil evaporation. Geophys. Res. Lett. 45, 10,398–10,405 (2018). [DOI: 10.1029/2018GL078803]
  20. Or, D. & Lehmann, P. Surface evaporative capacitance: How soil type and rainfall characteristics affect global‐scale surface evaporation. Water Resour. Res. 55, 519–539 (2019). [DOI: 10.1029/2018WR024050]
  21. Seneviratne, S. I. et al. Investigating soil moisture-climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010). [DOI: 10.1016/j.earscirev.2010.02.004]
  22. Thiery, W. et al. Present-day irrigation mitigates heat extremes. J. Geophys. Res. Atmospheres 122, 1403–1422 (2017). [DOI: 10.1002/2016JD025740]
  23. Maxwell, R. M. & Kollet, S. J. Interdependence of groundwater dynamics and land-energy feedbacks under climate change. Nat. Geosci. 1, 665–669 (2008). [DOI: 10.1038/ngeo315]
  24. Taylor, R. G. et al. Ground water and climate change. Nat. Clim. Chang. 3, 322–329 (2012). [DOI: 10.1038/nclimate1744]
  25. Keune, J. et al. Studying the influence of groundwater representations on land surface-atmosphere feedbacks during the European heat wave in 2003. J. Geophys. Res. Atmos. 121, 13,301–13,325 (2016). [DOI: 10.1002/2016JD025426]
  26. Vereecken, H. et al. Modeling soil processes: review, key challenges, and new perspectives. Vadose Zone J. 15, vzj2015 (2016). [DOI: 10.2136/vzj2015.outstanding]
  27. Hartemink, A. E. & Minasny, B. Towards digital soil morphometrics. Geoderma 230–231, 305–317 (2014). [DOI: 10.1016/j.geoderma.2014.03.008]
  28. Vereecken, H. et al. Infiltration from the pedon to global grid scales: An overview and outlook for land surface modeling. Vadose Zone J. 18, 180191 (2019). [DOI: 10.2136/vzj2018.10.0191]
  29. Brooks, R. H., & Corey, A. T. Hydraulic Properties of Porous Media. Hydrology Papers 3 (Colorado State University, Fort Collins, 1964).
  30. van Genuchten, M. Th A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980). [DOI: 10.2136/sssaj1980.03615995004400050002x]
  31. Gutmann, E. D. & Small, E. E. A comparison of land surface model soil hydraulic properties estimated by inverse modeling and pedotransfer functions. Water Resour. Res. 43, W05418 (2007). [DOI: 10.1029/2006WR005135]
  32. Smettem, K. R. J. & Kirkby, C. Measuring the hydraulic properties of a stable aggregated soil. J. Hydrol. 117, 1–13 (1990). [DOI: 10.1016/0022-1694(90)90084-B]
  33. Gerke, H. H. & van Genuchten, M. T. A dual-porosity model for simulating the preferential movement of water and solutes in structured porous media. Water Resour. Res. 29, 305–319 (1993). [DOI: 10.1029/92WR02339]
  34. Tuller, M. & Or, D. Unsaturated hydraulic conductivity of structured porous media: a review of liquid configuration-based models. Vadose Zone J. 1, 14–37 (2002). [DOI: 10.2136/vzj2002.1400]
  35. Coppola, A., Comegna, V., Basile, A., Lamaddalena, N. & Severino, G. Darcian preferential water flow and solute transport through bimodal porous systems: experiments and modelling. J. Contam. Hydrol. 104, 74–83 (2009a). [PMID: 19042056]
  36. Jarvis, N., Koestel, J., Messing, I., Moeys, J. & Lindahl, A. Influence of soil, land use and climatic factors on the hydraulic conductivity of soil. Hydrol. Earth Syst. Sci. 17, 5185–5195 (2013). [DOI: 10.5194/hess-17-5185-2013]
  37. Follett, R. Soil management concepts and carbon sequestration in cropland soils. Soil Tillage Res. 61, 77–92 (2001). [DOI: 10.1016/S0167-1987(01)00180-5]
  38. Arnold, C., Ghezzehei, T. A. & Berhe, A. A. Decomposition of distinct organic matter pools is regulated by moisture status in structured wetland soils. Soil Biol. Biochem. 81, 28–37 (2015). [DOI: 10.1016/j.soilbio.2014.10.029]
  39. Weynants, M., Vereecken, H. & Javaux, M. Revisiting Vereecken pedotransfer functions: introducing a closed-form hydraulic model. Vadose Zone J. 8, 86–95 (2009). [DOI: 10.2136/vzj2008.0062]
  40. Vereecken, H. et al. Using pedotransfer functions to estimate the van Genuchten–Mualem soil hydraulic properties: a review. Vadose Zone J. 9, 795–820 (2010). [DOI: 10.2136/vzj2010.0045]
  41. Wösten, J. H. M., Lilly, A., Nemes, A. & Le Bas, C. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169–185 (1999). [DOI: 10.1016/S0016-7061(98)00132-3]
  42. Schaap, M. G., Leij, F. J. & van Genuchten, M. T. Rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. J. Hydrol. 251, 163–176 (2001). [DOI: 10.1016/S0022-1694(01)00466-8]
  43. Tomasella, J., & Hodnett, M. G. In: Pachepsky, Y. & Rawls, W. J. (eds.) Development of Pedotransfer Functions in Soil Hydrology. Developments in Soil Science 30, pp. 415–429 (Elsevier, 2004).
  44. Tóth, B. et al. New generation of hydraulic pedotransfer functions for Europe. Eur. J. Soil Sci. 66, 226–238 (2015). [PMID: 25866465]
  45. Ahuja, L. R., Naney, J. W., Green, R. E. & Nielsen, D. R. Macroporosity to charcaterize spatial variability of hydraulic conductivity and effects of land manmagement. Soil Sci. Soc. Am. J. 48, 699–702 (1984). [DOI: 10.2136/sssaj1984.03615995004800040001x]
  46. Nemes, A., Schaap, M., Leij, F. & Wösten, J. Description of the unsaturated soil hydraulic database UNSODA version 2.0. J. Hydrol. 251, 151–162 (2001). [DOI: 10.1016/S0022-1694(01)00465-6]
  47. Børgesen, C. D., Jacobsen, O. H., Hansen, S. & Schaap, M. G. Soil hydraulic properties near saturation, an improved conductivity model. J. Hydrol. 324, 40–50 (2006). [DOI: 10.1016/j.jhydrol.2005.09.014]
  48. Saxton, K. E. & Rawls, W. J. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 70, 1569–1578 (2006). [DOI: 10.2136/sssaj2005.0117]
  49. Vereecken, H., Meas, J. & Feyen, J. Estimating unsaturated hydraulic conductivity from easily measured soil properties. Soil Sci. 149, 1–12 (1990). [DOI: 10.1097/00010694-199001000-00001]
  50. Koestel, J. & Jorda, H. What determines the strength of preferential transport in undisturbed soil under steady-state flow? Geoderma 217–218, 144–160 (2014). [DOI: 10.1016/j.geoderma.2013.11.009]
  51. van Dam, J. C. Simulation of field-scale water flow and bromide transport in a cracked clay soil. Hydrol. Process. 14, 1101–1117 (2000). [DOI: 10.1002/(SICI)1099-1085(20000430)14]
  52. Barto, E. K., Alt, F., Oelmann, Y., Wilcke, W. & Rillig, M. C. Contributions of biotic and abiotic factors to soil aggregation across a land use gradient. Soil Biol. Biochem. 42, 2316–2324 (2010). [DOI: 10.1016/j.soilbio.2010.09.008]
  53. Chamberlain, E. J. & Gow, A. J. Effect of freezing and thawing on the permeability and structure of soils. Engrg. Geol. 13, 73–92 (1979). [DOI: 10.1016/0013-7952(79)90022-X]
  54. Tuller, M. & Or, D. Hydraulic functions for swelling soils: pore scale considerations. J. Hydrol. 272, 50–71 (2003). [DOI: 10.1016/S0022-1694(02)00254-8]
  55. Passioura, J. B. Soil structure and plant growth. Aust. J. Soil Res. 29, 717–728 (1991). [DOI: 10.1071/SR9910717]
  56. Oades, J. M. The role of biology in formation, stabilization and degradation of soil structure. Geoderma 56, 377–400 (1993). [DOI: 10.1016/0016-7061(93)90123-3]
  57. Dunne, T., Zhang, W. Z. & Aubry, B. F. Effects of rainfall, vegetation, and microtopography on infiltration and runoff. Water Resour. Res. 27, 2271–2285 (1991). [DOI: 10.1029/91WR01585]
  58. Thompson, S. E., Harman, C. J., Heine, P. & Katul, G. G. Vegetation‐infiltration relationships across climatic and soil type gradients. J. Geophys. Res. Biogeosci. 115(G2), (2010).
  59. Archer, N. A. L. et al. Soil characteristics and landcover relationships on soil hydraulic conductivity at a hillslope scale: a view towards local flood management. J. Hydrol. 497, 208–222 (2013). [DOI: 10.1016/j.jhydrol.2013.05.043]
  60. Niemeyer, R., Fremier, A., Heinse, R., Chávez, W. & DeClerck, F. Woody vegetation increases saturated hydraulic conductivity in dry tropical Nicaragua. Vadose Zone J. 13, vzj2013.01 (2014). [DOI: 10.2136/vzj2013.01.0025]
  61. Fuentes, J. P., Flury, M. & Bezdicek, D. F. Hydraulic properties in a silt loam soil undernatural prairie, conventional till, and no-till. Soil Sci. Soc. Am. J. 68, 1679–1688 (2004). [DOI: 10.2136/sssaj2004.1679]
  62. Araya, S. N. & Ghezzehei, T. Using machine learning for prediction of saturated hydraulic conductivity and its sensitivity to soil structural perturbations. Water Resour. Res. 55, 5715–5737 (2019). [DOI: 10.1029/2018WR024357]
  63. Dexter, A. R. Advances in characterization of soil structure. Soil Tillage Res. 11, 199–238 (1988). [DOI: 10.1016/0167-1987(88)90002-5]
  64. Bronick, C. J. & Lal, R. Soil structure and management: a review. Geoderma 124, 3–22 (2005). [DOI: 10.1016/j.geoderma.2004.03.005]
  65. Fatichi, S. & Pappas, C. Constrained variability of modeled T:ET ratio across biomes. Geophys. Res. Lett. 44, 6795–6803 (2017). [DOI: 10.1002/2017GL074041]
  66. Hengl, T. et al. SoilGrids1km global soil information based on automated mapping. PLoS ONE 9, e105992 (2014). [PMID: 25171179]
  67. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017). [PMID: 28207752]
  68. Fatichi, S. et al. Partitioning direct and indirect effects reveals the response of water limited ecosystems to elevated CO. Proc. Natl Acad. Sci. USA 113, 12757–12762 (2016). [PMID: 27791074]
  69. Mastrotheodoros, T. et al. Linking plant functional trait plasticity and the large increase in forest water use efficiency. J. Geophys. Res. Biogeosci. 122, 2393–2408 (2017). [DOI: 10.1002/2017JG003890]
  70. Manoli, G., Ivanov, V. Y. & Fatichi, S. Dry season greening and water stress in Amazonia: the role of modeling leaf phenology. J. Geophys. Res. Biogeosci. 123, 1909–1926 (2018). [DOI: 10.1029/2017JG004282]
  71. Mascaro, G., Vivoni, E. R. & Méndez-Barroso, L. A. Hyperresolution hydrologic modeling in a regional watershed and its interpretation using empirical orthogonal functions. Adv. Water Resour. 83, 190–206 (2015). [DOI: 10.1016/j.advwatres.2015.05.023]
  72. Baroni, G., Zink, M., Kumar, R., Samaniego, L. & Attinger, S. Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales. Hydrol. Earth Syst. Sci. 21, 2301–2320 (2017). [DOI: 10.5194/hess-21-2301-2017]
  73. Walko, R. & Avissar, R. The Ocean-Land-Atmosphere Model (OLAM). Part I: shallow water tests. Mon. Weather Rev. 136, 4033–4044 (2008a). [DOI: 10.1175/2008MWR2522.1]
  74. Walko, R. & Avissar, R. The Ocean-Land-Atmosphere Model (OLAM). Part II: formulation and tests of the nonhydrostatic dynamic core. Mon. Weather Rev. 136, 4045–4062 (2008b). [DOI: 10.1175/2008MWR2523.1]
  75. Walko, R. & Avissar, R. A direct method for constructing refined regions in unstructured conforming triangular-hexagonal computational grids: Application to OLAM. Mon. Weather Rev. 139, 3923–3937 (2011). [DOI: 10.1175/MWR-D-11-00021.1]
  76. Gochis, D. J. et al. The WRF-Hydro modeling system technical description, (Version 5.0). NCAR Technical Note. pp. 107 (2018).
  77. Maxwell, R. M. et al. Development of a coupled groundwater–atmosphere model. Mon. Weather Rev. 139, 96–116 (2011). [DOI: 10.1175/2010MWR3392.1]
  78. Maxwell, R. M. & Condon, L. E. Connections between groundwater flow and transpiration partitioning. Science 353, 377–380 (2016). [PMID: 27463671]
  79. Mizukami, N. et al. Towards seamless large-domain parameter estimation for hydrologic models. Water Resour. Res. 53, 8020–8040 (2017). [DOI: 10.1002/2017WR020401]
  80. Gutmann, E. D. & Small, E. E. A method for the determination of the hydraulic properties of soil from MODIS surface temperature for use in land-surface models. Water Resour. Res. 46, W0652 (2010). [DOI: 10.1029/2009WR008203]
  81. Or, D. The tyranny of small scales – on representing soil processes in global land surface models. Water Resources Res. 55. https://doi.org/10.1029/2019WR024846 , (2019).
  82. Rabot, E., Wiesmeier, M., Schlüter, S. & Vogel, H.-J. Soil structure as an indicator of soil functions: a review. Geoderma 314, 122–137 (2018). [DOI: 10.1016/j.geoderma.2017.11.009]
  83. Hirmas, D. R. et al. Climate-induced changes in continental-scale soil macroporosity may intensify water cycle. Nature 561, 100–103 (2018). [PMID: 30185954]
  84. Assouline, S. Modeling the relationship between soil bulk density and the hydraulic conductivity function. Vadose Zone J. 5, 697–705 (2006). [DOI: 10.2136/vzj2005.0084]
  85. Ebrahimi, A. & Or, D. Microbial community dynamics in soil aggregates shape biogeochemical gas fluxes from soil profiles – upscaling an aggregate biophysical model. Glob. Change Biol. 22, 3141–3156 (2016). [DOI: 10.1111/gcb.13345]
  86. Green, K. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019). [PMID: 30675043]
  87. Saxton, K. E., Rawls, W. J., Romberger, J. S. & Papendick, R. I. Estimating generalized soil water characteristics from texture. Trans. ASAE 50, 1031–1035 (1986).
  88. Van Looy, K. et al. Pedotransfer functions in Earth system science: challenges and perspectives. Rev. Geophysics 55, 1199–1256 (2017). [DOI: 10.1002/2017RG000581]
  89. Assouline, S. & Or, D. Conceptual and parametric representation of soil hydraulic properties: a review. Vadose Zone J. 12, vzj2013.07 (2013).
  90. Mualem, Y. New model for predicting hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12, 513–522 (1976). [DOI: 10.1029/WR012i003p00513]
  91. Kutílek, M. & Jendele L., The structural porosity in soil hydraulic functions: a review. Soil Water Res. 3, S7–S20 (2008).
  92. Othmer, H., Diekkrüger, B. & Kutílek, M. Bimodal porosity and unsaturated hydraulic conductivity. Soil Sci. 152, 139–150 (1991). [DOI: 10.1097/00010694-199109000-00001]
  93. Zurmühl, T. & Durner, W. Modeling transient water and solute transport in a biporous soil. Water Resour. Res. 32, 819–829 (1996). [DOI: 10.1029/95WR01678]
  94. Coppola, A., Basile, A., Comegna, A. & Lamaddalena, N. Monte Carlo analysis of field water flow comparing uni- and bimodal effective hydraulic parameters for structured soil. J. Contam. Hydrol. 104, 153–165 (2009b). [PMID: 19027986]
  95. Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785–818 (2015). [DOI: 10.1002/2015RG000483]
  96. Cotton, W. R. et al. RAMS 2001: current status and future directions. Meteor. Atmos. Phys. 82, 5–29 (2003). [DOI: 10.1007/s00703-001-0584-9]
  97. Walko, R. L. et al. Coupled atmosphere-biophysics-hydrology models for environmental modeling. J. Appl. Meteor. 39, 931–944 (2000b). [DOI: 10.1175/1520-0450(2000)039<0931]
  98. Gleeson, T., Moosdorf, N., Hartmann, J. & van Beek, L. P. H. A glimpse beneath earth’s surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophys. Res. Lett. 41, 3891–3898 (2014). [DOI: 10.1002/2014GL059856]
  99. Fatichi, S., Manzoni, S., Or, D. & Paschalis, A. A mechanistic model of microbially mediated soil biogeochemical processes - a reality check. Glob. Biogeochem. Cycles 33, 620–648 (2019). [DOI: 10.1029/2018GB006077]
  100. Giorgi, F. & Francisco, R. Uncertainties in regional climate prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Clim. Dyn. 16, 169–182 (2000). [DOI: 10.1007/PL00013733]
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