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Different soil study tools to better understand the dynamics of carbon in soils at different spatial scales, from a single soil profile to the global scale Marwa Tifafi

To cite this version: Marwa Tifafi. Different soil study tools to better understand the dynamics of carbon in soils at different spatial scales, from a single soil profile to the global scale. Climatologie. Université Paris-Saclay, 2018. Français. .

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Different soil study tools to better understand the dynamics of carbon in soils at different spatial scales, from a single soil profile to the global scale Thèse de doctorat de l'Université Paris-Saclay Préparée à l’Université de Versailles-Saint-Quentin-en-Yvelines École doctorale n°129 sciences de l'environnement d’Ile-de-France (SEIF) Spécialité de doctorat: Météorologie, Océanographie, Physique de l'Environnement Thèse présentée et soutenue à Orme des Merisiers, le 05 Avril 2018, par

Marwa Tifafi Composition du Jury : Mr. Matthieu Roy Barman Professeur, UVSQ (UMR8212)

Président

Mr. Ivan Janssens Professeur, Université d’Anvers

Rapporteur

Mr. Sylvain Pellerin Directeur de Recherche, INRA (UMR1391)

Rapporteur

Mme. Sophie Cornu Directrice de Recherche, INRA (UMR34)

Examinatrice

Mme. Patricia Garnier Directrice de Recherche, INRA (UMR1402)

Examinatrice

Mr. Bruno Ringeval Chargé de Recherche, INRA (UMR1391)

Examinateur

Mme. Christine Hatté Chercheuse, CEA (UMR8212)

Directrice de thèse

Mr. Bertrand Guenet Chargé de Recherche, CNRS (UMR8212)

Co-Directeur de thèse

Mr. Jérôme Balesdent Directeur de Recherche, INRA (UMR34)

Invité

Titre : Différents outils d'étude du sol pour mieux comprendre la dynamique du carbone dans les sols à différentes échelles spatiales, depuis l’échelle d’un profil à l'échelle globale Mots clés : sol, carbone organique, modélisation, mesures expérimentales, bases de données Résumé : Les sols sont la principale composante de l’écosystème terrestre et le plus grand réservoir de carbone organique sur Terre, étant très réactifs aux perturbations humaines et aux changements climatiques. Malgré leur importance dans les réservoirs de carbone, la dynamique du carbone des sols est une source importante d'incertitudes pour les prévisions climatiques futures. Le but de la thèse était d'explorer différents aspects d’études du carbone des sols (mesures expérimentales, modélisation et évaluation de bases de données) à différentes échelles spatiales (de l'échelle d'un profil à l'échelle globale). Nous avons souligné que l'estimation des stocks globaux de carbone du sol est encore assez incertaine. Par conséquent le rôle du carbone des sols dans

la dynamique du climat devient l'une des principales incertitudes dans les modèles du système terrestre utilisés pour prédire les changements climatiques futurs. La deuxième partie de la thèse porte sur la présentation d'une nouvelle version du modèle IPSL-Land Surface appelé ORCHIDEE-SOM, intégrant la dynamique du 14C dans le sol. Plusieurs tests effectués supposent que les améliorations du modèle devraient se focaliser davantage sur une paramétrisation dépendante de la profondeur, principalement pour la diffusion, afin d'améliorer la représentation du cycle global du carbone dans les modèles de surface terrestre, contribuant ainsi à contraindre les prédictions futures du réchauffement climatique.

Title : Different soil study tools to better understand the dynamics of carbon in soils at different spatial scales, from a single soil profile to the global scale Keywords : soil, organic carbon stock, modeling, experiments, databases Abstract : Soils are the major components of the terrestrial ecosystems and the largest organic carbon reservoir on Earth, being very reactive to human disturbance and climate change. Despite its importance within the carbon reservoirs, soil carbon dynamics is an important source of uncertainties for future climate predictions. The aim of the thesis was to explore different aspects of soil carbon studies (Experimental measurements, modeling, and database evaluation) at different spatial scales (from the scale of a profile to the global scale). We highlighted that the estimation of the global soil carbon stocks is still quite uncertain. Consequently, the role of soil carbon in the

climate dynamics becomes one of the major uncertainties in the Earth system models (ESMs) used to predict future climate change. The second part of thesis deals with the presentation of a new version of the IPSL-Land Surface Model called ORCHIDEE-SOM, incorporating the 14C dynamics in the soil. Several tests done assume that model improvements should focus more on a depth dependent parameterization, mainly for the diffusion, in order to improve the representation of the global carbon cycle in Land Surface Models, thus helping to constrain the predictions of the future soil organic carbon response to global warming.

Université Paris-Saclay Espace Technologique / Immeuble Discovery Route de l’Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France

ACKNOWLEDGMENTS I am starting the thesis manuscript with these few words to thank all the wise people who surrounded me and to the endless interest and support that I received along the way. I think it was not an easy trip, but a very exciting and satisfying one, a very nice adventure. Firstly, I would like to thank the “Ecole Doctoral des Sciences de l’Environnement d’Ile de France (ED129)” and the “University of Versailles Saint Quentin en Yvelines” who financially supported me to carry out my research. This PhD, financed by the University of Versailles Saint Quentin en Yvelines, is also within the scope of the ANR-14-CE01-0004 DeDyCAS project enabling me to attend various enriching conferences. I did my research work at the “Laboratoire des Sciences du Climat et de l’Evironnement (LSCE)” at the ‘Orme des Merisiers’ and the “Gif sur Yvette” sites. I express my gratitude to all the people I met at LSCE who helped me a lot for my professional and personal integration. I also want to mention my gratitude to the numerous people I met during these 3 years that were willing to help: all the technicians, PhDs, post-docs and permanents from the LSCE. A very special thanks to the persons who helped me with the experimental part, Evelyne, Caroline and Claudé, very nice women who were very patient with a beginner student in isotopic measurements. You all have a very special place in my heart. My short stays in Gif were really great thanks to you. I really do not know from where to start, there are so many people that I would like to thank… Christine, Bertrand, I think I had a lot of luck to have had you as supervisors. When I applied for your thesis proposal, we did not know each other at all, so it was a bit scary for me at first to get involved in a three years project with you but I did not regret my choice one day. Thank you for your uncontrollable passion for science and your willingness to share it with me. Thank you for your numerous comments and continuous training of a new student that had no idea about isotopic measurements and Land Surface Models. Beyond the scientific enrichment that you had both brought to me, I want to thank you for your usual good mood and for your limitless kindness to me. I want to thank you especially Christine, we did not see each other often, but even receiving an email from you is always a pleasure, your encouragement (and your smile every time we see each other) have lit up my days. I am also very grateful to Marta. You were there along this long way too, supporting me and helping me with a lot of ORCHIDEE bugs. Thanks to you, I was able to progress much faster and finalize the modeling part on time. I honestly have to say that it was a pleasure working with you. I would also like to thank Jerome and Sebastien, who were kind enough to follow me during the three years of my thesis and to follow the progress of my work by being part of my thesis committees. Thanks to your many comments and remarks, we have been able to advance even more.

Of course, I thank all the jury members for agreeing to be part of this work and to read it despite their professional responsibilities. I also managed to finish this PhD thanks to all the happy time I had during these three years. I was very lucky to find colleagues and other non-science related persons that became real friends with who I shared many “apéros”, “soirées raclette”, parties, laughs and much much more. Finally, a big and special thanks to my little nephew who was born and started growing at the same time I decided to wrap up this thesis. Your coming to the world is the most wonderful thing that has happened. You already helped me a lot to finalize with your hugs, your crying sometimes, your laughs... You gave me a new and unknown great sensation that filled my days with hope and love.

TABLE OF CONTENTS

LIST OF PUBLICATIONS ..................................................................................................... 6 EXTENDED SUMMARY ....................................................................................................... 7 INTRODUCTION .................................................................................................................... 9 CHAPTER 1. Large Differences In Global And Regional Total Soil Carbon Stock Estimates Based On SoilGrids, HWSD And NCSCD: Intercomparison And Evaluation Based On Field Data From USA, England, Wales And France......................................................................... 18 CHAPTER 2. Impact of Cultivation on Soil Carbon Dynamics in Ultisols of The Northeast of Argentina .................................................................................................................................. 34 CHAPTER 3. The Use Of Radiocarbon 14C To Constrain Soil Carbon Dynamics In The Land Surface Model ORCHIDEE ..................................................................................................... 55 GENERAL CONCLUSION .................................................................................................. 86 REFERENCES ....................................................................................................................... 92 APPENDICES ...................................................................................................................... 106 Appendix 1. Sieving Sizes Effect On Total Soil Organic Carbon, Total Nitrogen, 13C And 14 C Measurements And 14C Measurements Given By Two Different Analyzers (ECHoMICADAS and ARTEMIS) .................................................................................... 106 Appendix 2. Detailed Description of the Land Surface Model ORCHIDEE-SOM .......... 115 Appendix 3. ORCHIDEE-MICT (V8.4.1), A Land Surface Model For The High latitudes: Model Description And Validation .................................................................................... 127 Appendix 4. How Lysimetric Monitoring Of Technosols Can Contribute To Understand the Temporal Dynamics Of The Soil Porosity ......................................................................... 171 Appendix 5. Résumé Étendu en Français .......................................................................... 181

LIST OF PUBLICATIONS

M. Tifafi, R. Bouzouidja, S. Leguedois, S. Ouvrard, G. Séré (2017). How lysimetric monitoring of Technosols can contribute to understand the temporal dynamics of the soil porosity GEODERMA, 296, 2017, 60-68. https://doi.org/10.1016/j.geoderma.2017.02.0277

M. Tifafi, Guenet, B., & Hatté, C. (2018). Large differences in global and regional total soil carbon stock estimates based on SoilGrids, HWSD, and NCSCD: Intercomparison and evaluation based on field data from USA, England, Wales, and France. Global Biogeochemical Cycles, 32. https://doi.org/10.1002/2017GB005678

M. Tifafi, J. Balesdent, S.Cornu, H. Morra s, L. Moretti, B. Guenet and C. Hatte. Impact of cultivation on soil carbon dynamics in Argentinean Pampa – insight of 13C and 14C. In preparation.

M. Tifafi, M. Camino-Serrano, S.Cornu, H. Morra s, L. Moretti, B. Guenet and C. Hatte. The use of radiocarbon 14C to constrain soil carbon dynamics in the continental biosphere model ORCHIDEE. Submitted in Geoscientific Model Development journal.

Guimberteau, M., Zhu, D., Maignan, F., Huang, Y., Yue, C., Dantec-Nédélec, S., Ottlé, C., Jornet-Puig, A., Bastos, A., Laurent, P., Goll, D., Bowring, S., Chang, J., Guenet, B., Tifafi, M., Peng, S., Krinner, G., Ducharne, A., Wang, F., Wang, T., Wang, X., Wang, Y., Yin, Z., Lauerwald, R., Joetzjer, E., Qiu, C., Kim, H., and Ciais, P. (2018): ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation, Geoscientific Model Development journal, 11, 121-163. https://doi.org/10.5194/gmd-11-121-2018

B. Guenet, M. Camino-Serrano, P. Ciais, M. Tifafi, F. Maignan, J.L. Soong, and I.A. Janssens (2017). Impact of priming on global carbon soil carbon stocks. Accepted in Global Change Biology.

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SUMMARY The growing interest in the study of carbon stored in soils is due to two main reasons. First of all, soils are the major components of the terrestrial ecosystems and the largest organic carbon reservoir on Earth, being very reactive to human disturbance and climate change. Second, despite its importance within the carbon reservoirs, soil carbon dynamics is an important source of uncertainties for future climate predictions. The aim of the thesis was to explore different aspects of soil carbon studies (Experimental measurements, modeling, and database evaluation) at different spatial scales (from the scale of a profile to the global scale). In fact, we first estimated global and regional soil carbon stocks at 1m depth given by three existing databases (SoilGrids, the Harmonized World Soil Database (HWSD), and the Northern Circumpolar Soil Carbon Database (NCSCD)). We observed that total stocks predicted by each product greatly differ: it’s estimated to be around 3400 Pg by SoilGrids and is about 2500 Pg according to HWSD. It is therefore possible that previous estimates of the total organic carbon stock have seriously underestimated organic carbon stock in northern latitudes, peatlands and wetlands. Thus, estimates of the total soil organic carbon stock for global land mask are still quite diverse. This difference is marked in particular for boreal regions where differences can be related to high disparities in soil organic carbon concentration. Differences in other regions are more limited and may be related to differences in bulk density estimates. Finally, evaluation of the three datasets vs measurements shows that there is a significant difference in spatial patterns between ground truth data and compared datasets. The estimation of the global soil carbon stocks is therefore still quite uncertain. Consequently, the role of soil carbon in the climate dynamics becomes one of the major uncertainties in the Earth system models (ESMs) used to predict future climate change. In ESMs, soil, and thus soil carbon dynamics, is considered in Land Surface Model, which manages the carbon cycle, energy and water on land and simulates the carbon uptake by plants between the atmosphere and the land. Regarding the relatively simple representation of the soil organic matter dynamic in such model, the comparison with data is very complex. One of the promising approaches emerging from literature is the use of carbon isotopes data (13C and 14C) that can be considered as integrators of the soil carbon dynamics for different time scales (year to century). Therefore, isotopic measurements were performed in the laboratory on a soil latter used to evaluate a new version of ORCHIDEE. This was done to, first, better understand the usefulness of isotopes for the characterization of soil carbon dynamics at the profile scale, then to highlight the effect of the land cover change on the ability of a soil to sequester carbon. Indeed, the soil studied is an Argentinean soil under two different land covers. For that, we selected two Ultisol profiles, close together to undergo the same climate, the first under the native forest vegetation and the second, under a conversion into elephant grass plantation. Therefore, total organic carbon content, δ13C and 14C activity were measured. We observed that the carbon content decreases significantly up to 30 cm deep after the establishment of the new plantation which is correlated with smaller inputs compared to those under forest through both 7

weaker root inputs and aboveground export and a higher mineralization of soil organic matter induced by new cultivation. Then, we highlight a loss of 3% of carbon by planting elephant grass, moreover, most of this lost carbon comes from the native forest. δ13C data of both top layers clearly exhibits input of new carbon that derived from Elephant Grass into the upper layers of Elephant Grass profile. Finally, the lower 14C activity of top layer of the Elephant Grass profile highlights a loss of new carbon that was under Native Forest. This reveals once again that deforestation implies a decrease in the ability of the soil to sequester carbon. The last part of thesis deals then with the presentation of a new version of the IPSL-Land Surface Model called ORCHIDEE-SOM, incorporating the 14C dynamics in the soil. ORCHIDEE-SOM, first, simulates carbon dynamics in the soil column down to 2 m-depth, partitioned in 11 layers. Second, the concentration of dissolved organic carbon in each layer and transport between layers are modeled. Finally, soil organic carbon decomposition is considered taking into account the amount of fresh organic matter as a way of accounting for the priming effect. After implementing the 14C in the model, we evaluated model outputs against observations of soil organic carbon and modern fraction from four sites with different vegetation covers. The model managed to reproduce the soil organic carbon stocks and the modern fraction along the vertical profiles at the four sites. However, an overestimation of the total carbon stock throughout the profile was noted, but was mostly marked on the surface. Then, thanks to the introduction of 14C, it has been possible to highlight an underestimation of the age of carbon in the soil. Thereafter, two different tests on this new version have been performed. The first was to increase carbon residence time of the passive pool and decrease the flux from the slow pool to the passive pool. The second was to establish an equation of diffusion, initially constant throughout the profile, as an exponential function of depth. The first modifications did not improve the capacity of the model to reproduce observations whereas the second test showed a diminution of the soil carbon stock overestimation, especially at the surface and an improvement of the estimates of the carbon age. This assumes that model improvements should focus more on a depth dependent parameterization, mainly for the diffusion, in order to improve the representation of the global carbon cycle in LSMs, thus helping to constrain the predictions of the future soil organic carbon response to global warming.

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INTRODUCTION

INTRODUCTION For millennia, the Earth's climate varies according to the space and time [Zachos et al., 2001]. The changes are typically observed over long periods of time that diminish human perception at some point. However, in recent decades, climate change appears to have accelerated [Karl and Trenberth, 2003; IPCC, 2013] which leads to wonder about the existence of these changes, their causes, their fate and, more importantly, their immediate and distant consequences. In fact, nowadays, climate change becomes a major issue for human societies. Many of us can already perceive its effects: the heatwave in summer, a winter without snow, floods in one corner of Earth while another undergoes terrible droughts. For example, the heatwave affecting Texas is significantly more likely than 40 years ago and the likelihood of very high temperatures in November in the UK is increasing since 1960s [Peterson et al., 2012]. Then, there is a decrease of the extent of Antarctic sea ice in 2014 and an increase of the strength and likelihood of high sea surface temperatures in both the Atlantic and Pacific Oceans [Herring et al., 2015]. Even if it is very difficult to know if such isolated events are linked to climate change or simply to a "normal" variability, it’s certain that the probability of the occurrence of such extreme events increases with Climate change [IPCC, 2013]. Otherwise, during the twentieth century, the global mean surface air temperature has risen about 0.5°C [IPCC, 1995] and the average soil temperatures has increased by 0.6 °C [Jones et al., 1999]. The main warming occurred during two periods, the first from 1910 to 1945 and then after 1976 [Zwiers and Weaver, 2000; Walther et al., 2002]. The 1990-1999 decade was the hottest in the twentieth century and finally, since 2000, we have been breaking records of heat almost every year (https://climate.nasa.gov/vital-signs/global-temperature/). Beyond climatic mechanisms, characterization of climate impacts is at least as complex. The warming of the ocean water and the melting of the ice make rise the level of the seas, which endangers many archipelagos and lowlands [Nelson et al., 2001; Mazhitova et al., 2004]. These climatic changes disrupt fauna and flora [Walther et al., 2002; Root et al., 2003] and affect the agriculture, the health and the economy [Rosenzweig et al., 2001; Tol, 2002; Hayhoe et al., 2004; Haines et al., 2006; Morton, 2007; Ciscar et al., 2011]. The main cause of climate changes is the anthropogenic greenhouse gases (GHGs) emissions [IPCC, 2013]). These GHGs absorb the infrared radiation emitted by the Earth's surface and contribute to the greenhouse effect. This phenomenon is indeed natural and helps to maintain life on Earth in a viable conditions: the transparency of the atmosphere allows solar radiation to reach the ground. The energy thus brought in is transformed into heat. Like any hot body, the surface of the Earth radiates its heat. But GHGs and clouds are opaque to the infrared rays emitted by the Earth. By absorbing this radiation, they trap thermal energy near the surface of the globe, where it warms the low atmosphere. So, the greenhouse natural effect brings the mean temperature of the Earth's surface from -18 °C (which it would be in its absence) to +15 °C [Halmann and Steinberg, 1999]. Thus, the amount of GHGs in the atmosphere is positively correlated with global temperatures [Barnola et al., 1987; Chappellaz et al., 1990; Long, 1991]. 10

The concentrations of GHGs in the Earth's atmosphere have been increasing since the 19th century mainly because of anthropogenic emissions [IPCC, 2013]. This rise in GHGs contributes to a global warming which is causing climatic disturbances. The increase in the main GHGs is mainly due to certain human activities, including the massive use of fossil fuels, deforestation, food production, soils artificialisation (urbanization)… Among the various greenhouse gases, CO2 is the second most important one in the atmosphere (after water vapor) contributing at about 26% to this phenomenon [Kiehl and Trenberth, 1997]. Its natural equilibrium is being perturbed due to anthropogenic activities (fossil fuel burning, deforestation, etc.). Moreover, as the atmospheric CO2 increases, the global temperature increases and this put more water vapor into the atmosphere [Halmann and Steinberg, 1999]. Water vapor is also a very effective GHG which tends to increase the Earth’s temperature even further. Carbon dioxide comes from exchange with ocean, from autotrophic and heterotrophic respiration of ecosystems, from use of fossil fuels and from the combustion of vegetal. Since the beginning of the industrial revolution in the 18th century, the use of fossil fuel has increased in addition to the large-scale deforestation [FAO, 2006]. These activities and other human interventions have resulted in an increase in CO2 concentration in the atmosphere since the beginning of industrialization, from ∼280 parts per million by volume (ppmv) [Monnin et al., 2001] to its present value of ∼400 ppmv [Monastersky, 2013]. So, a good understanding of the evolution of carbon concentration and the sources of carbon emissions may help us to take better (political and environmental) decisions in order to slow down the global warming. For instance, the interest in the study of carbon cycle is very significant and this for many reasons, not only because of the continued increase of its concentration in the atmosphere. First, the study of the carbon cycle has always been of great scientific interest. Indeed, these studies make it possible, for example, to evaluate the availability of carbon in order to optimize local production sensitive to this element [Sharkey, 2015]. Then, the carbon cycle is very important for the biosphere [Muller et al., 2016], since carbon is one of the most important component for the development of living beings on Earth. Finally, the study of this cycle has recently taken a special place in the context of this issue of global warming especially to evaluate feedbacks in both directions [Friedlingstein et al., 2006, 2009]. More broadly than climate issues, the study of the carbon cycle will determine the effects of the release of carbon stored in the form of fossil fuels by human activities [Haszeldine, 2009]. It is therefore very important to have a thorough knowledge of the carbon cycle, to explore its different reservoirs, as well as the interactions and exchanges between them.

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Figure 1. Simplified schematic of the global carbon cycle, showing the reservoir sizes in Pg C and the main annual fluxes in Pg C yr–1: pre-industrial fluxes in black and anthropogenic fluxes in red [Ciais et al., 2013]

We can stand the carbon cycle (Figure 1) in the form of interconnected reservoirs: the atmosphere, the terrestrial biosphere, the oceans and the sediments. The Earth’s oceans contain about 38000 Pg C, most of which is in the form of dissolved inorganic carbon stored at great depths (Figure 1). A much smaller amount of carbon, approximately 900 Pg C, is located near the ocean surface. This carbon is exchanged rapidly with the atmosphere through both physical processes, such as CO2 dissolving into the water, and biological processes, such as the growth, death and decay of plankton [Falkowski et al., 1998]. The atmosphere contains approximately 800 Pg C, most of which is in the form of CO2. The relatively small size of the atmospheric C pool makes it more sensitive to disruptions caused by an increase in sources or sinks of C from the Earth’s other pools. 12

Terrestrial ecosystems contain carbon in the form of plants, animals, soils, microorganisms (bacteria and fungi), carbonate, oxalate … Most of the carbon in terrestrial ecosystems exists in organic forms, compounds that were produced by living beings, including leaf, wood, root, dead plant material... Plants exchange carbon with the atmosphere through photosynthesis, in which CO2 is absorbed, and respiration, where some fraction of the previously captured CO2 is released back to the atmosphere as a product of metabolism. At the death of the organism, such exchanges with atmosphere cease but CO2 emission continues through decomposition of dead organic matter by bacteria and fungi. Thus, the carbon exchanges among reservoirs are the result of different chemical, physical and biological processes. Some stocks and flows among these reservoirs are relatively well quantified. For instance, about 120 Pg C of the atmospheric CO2 is fixed by the terrestrial biomass via photosynthesis, per year [Janzen, 2004] and, in parallel, the flux of CO2 to the atmosphere from land use change is about 1.6 (0.5 to 2.7) Pg C yr–1 for the 1990s [Denman et al., 2007]. However, there is still a great debate about the carbon stored in terrestrial ecosystems [Van der Werf et al., 2009; Harris et al., 2012] and in particular in soils [Köchy et al., 2015]. Nonetheless, whilst ocean contains the largest active pool of carbon, the soils are a major component of the terrestrial ecosystem and the largest organic carbon reservoir on Earth. The global mass of soil organic carbon is greater than the combined mass of carbon contained in the atmosphere and in the living biomass [Ciais et al., 2013]. Soils contain 3.3 times the size of the atmospheric carbon pool and 4.5 times the size of the biotic carbon pool [Lal, 2004]. Then, soil is a non-renewable natural resource and is quite reactive to human disturbance and climate change. Even minor change of the soil organic carbon mass may have pronounced effects on the concentration of CO2 in the atmosphere and thus on climate change [Jones et al., 2005; Schuur et al., 2008]. Unfortunately, despite its importance, the global mass of soil organic carbon (SOC) and its distribution in space is not well known [Jandl et al., 2014; Scharlemann et al., 2014a; Tifafi et al., 2017]. Overall, the mean soil organic carbon stock is estimated to be about 1500 Pg C, but this value follows several uncertainties. Scharlemann et al. [2014] estimated that, according to a metaanalysis using 27 studies estimating the global SOC stocks, the median value of the SOC estimated is about 1460.5 Pg and vary from 504 to 3000 Pg. This can be partly explained by the fact that existing data do not fully cover carbon stocks throughout the depth, which partly explains the underestimation of SOC stocks in some regions of the world. In fact, most SOC estimates and maps currently provide data up to 1 m deep only. This may be sufficient for some soils for which the organic carbon content decreases with depth, but it’s not for those which are much deeper and which have higher SOC concentrations. In this context, several carbon-related products in soils already exist. They provide information at regional (NSCSD, RMQS, NSI) and/or global scales (HWSD, SoilGrids, ISCN). In addition, some products only gather field data and present soil data on well-defined profiles. Others take these field data to extrapolate them and estimate the spatial distribution of the carbon stocks. However, the stocks predicted by each product largely differ. These products exhibit in fact both similarities and differences in the methods and collected field data. Indeed, while based 13

on the same regional data sources, they use different methods of stock estimation (pedotransfer functions). So, uncertainties on global soil carbon stock might arise not only from sampling of soil-profile data but also from differences in approaches to stock calculations and estimations. Therefore, the estimation of the global soil carbon stocks is still quite uncertain. Consequently, the role of soil carbon in the climate dynamics becomes one of the major uncertainties in the Earth system models used to predict future climate change [Luo et al., 2016]. Important uncertainties are still linked to the carbon stock in soils from the scale of a site to the global scale. This is further confirmed by the simulations of coupled climate and carbon cycle models which show that there is still considerable uncertainty in future estimates of carbon storage in vegetation and soils [IPCC, 2001; Houghton, 2003; Schaphoff et al., 2006; Sitch et al., 2008; Friend et al., 2014]. Indeed, the major mechanisms are rather well known, however, equations and associated parameter values are still quite uncertain. In fact, modeling the temporal and spatial variation of soil processes is difficult because soil is a complex reservoir consisting of a broad range of types of organo-mineral particles and aggregates and contains numerous organisms exhibiting different physiological processes [Fang and Moncrieff, 1999]. Furthermore, soil properties vary temporally and spatially, both horizontally and vertically [Davidson and Trumbore, 1995]. For instance, microbial respiration has been reported to be influenced by many environmental factors, such as the quantity and type of live and dead biomass in the soil, soil temperature and moisture content [Bridge and Rixon, 1976; Chapman, 1979; Bridge et al., 1983; Rajvanshi and Gupta, 1986; Qi et al., 1994]. Then, microbial activity of the soil is controlled by complex factors which are in constant interactions. These factors are biological (physiology of microorganisms, ecology of populations and communities) physical (diffusion of solutes and gases in the soil) and chemical (composition of organic materials that serve as substrate to microorganisms). Then, microbial community in soils is diverse, abundant, and only partly understood [Gans et al., 2005]. It is often described as a “black box” [Tiedje et al., 1999]. Some researchers estimate that as much as 90–99% of the soil microbial community is uncultivable by current technologies [Hill et al., 2000]. Therefore, we are limited in our ability to confidently and absolutely ascribe soil processes to specific microorganisms [Jastrow et al., 2007]. Yet, the complexity of the mechanisms involved in controlling soil microbial activity and therefore the carbon flux from the soil to the atmosphere makes predicting the response of these systems to climate change extremely complex and these issues need to be addressed at different scales. Thus our ability to predict future changes in carbon stocks in soils using global climate models of the processes governing storage and destocking at variable time and space scales is currently heavily criticized. So, a better understanding of SOC stocks and flows is essential for better carbon management and climate change mitigation options, as well as to help parameterize Earth system models used to guide climate policy. Indeed, these models are increasingly used today in order to predict the future evolution of the climate. They have become an indispensable tool to predict the risks of continued environmental changes for future climate, ecosystem services and sustainable land management. For instance, a set of Earth System Models (ESMs) are used within the Intergovernmental Panel on Climate Change (IPCC) [Taylor et al., 2012] for assessment of the 14

impacts of climate change and design of mitigation strategies. Hence, their predictions need to be as accurate as possible. These models represent the physical, chemical and biological processes within and between the atmosphere, ocean and terrestrial biosphere. They allow us to follow and understand, on the one hand, the effect of the climate on carbon and vegetation and, on the other hand, the effect of the vegetation and carbon on the climate [Prentice and Cowling, 2013]. This means that they integrate well the feedbacks from the biosphere to the atmosphere. One of the components of the ESMs is the land surface model (LSM). This component primarily manages the carbon cycle, energy and water on land and simulate the carbon uptake by plants between the atmosphere and the land, namely the net primary production1 (NPP) and the heterotrophic soil respiration and therefore the net ecosystem exchange2 (NEE) with atmosphere. Unfortunately, despite the importance of soil as a large component of the global carbon storage, the soil compartments are not well represented in ESMs [Todd-Brown et al., 2013]. Indeed, soil carbon dynamics described in ESMs are based on the Century model [Parton et al., 1987] or the ROTH-C model [Coleman et al., 1997] where soil carbon is represented as several pools, with different turnover rates for each pool. Carbon is decomposed in each pool, one part is then transferred from one pool to another and the other part is lost through heterotrophic respiration. Depicting soil soil organic matter as not homogeneous but showing different fates (expressed as different mean residence times) was a very important step forward that allows to address important agronomical issues and provides a rough description of soil organic matter dynamics, pretty easy to describe as mathematic equations. Anyway, there arises the time of more precise challenges and the impossibility to fit observations with this simple view, which illustrate the need of a finer representation of soil organic matter in LSMs. In addition, soils are generally represented as a single-layer box in ESMs that do not take into account the evolution and variation of soil organic processes and therefore soil organic carbon (SOC) as a function of depth. Soil organic matter representation evolves and its mathematical depiction in LSMs also. Some newly evidenced process such as priming effect [Guenet et al., 2016], exchanges with dissolved phase also [Camino-Serrano et al., 2017], are now included in ESMs. But the core structure of soil organic matter dynamics remains based on RothC or Century representation. If it is true that soil organic compounds do not show homogeneous fate, it is also sure that they do not be classed to a fixed number of reservoirs, homogenous either by the chemical structure of the compounds, by the size or density of aggregates, or any physical or chemical specificities. So comparison between model output and observation is not easy and can only be done as the scale of global soil organic matter within one sample. There is no way to compare model outputs to observation at the reservoir scale, whatever it is. So that, to be more pertinent in evaluating the parameters and the equations of the newly implemented processes, it is of interest of implementing carbon isotopes (13C and 14C)'s fates in the model itself, this to facilitate the 1

NPP represents the net production of organic carbon by plants in an ecosystem usually measured over a period of a year or more. It’s Gross Primary Production minus the amount of carbon respired by plants themselves in autotrophic respiration. 2 NEE refers to the net primary production minus carbon losses in heterotrophic respiration. 15

comparison between model outputs and available observations, but also, thanks to more constraints, to better precise the model's structure and parameters. Indeed, 13C and 14C acquired on soil organic matter, provide complementary information on the dynamics (temporal dimension) of soil organic matter. These tracers have the major advantage of being "integrators" of carbon dynamics on short time scales (a few weeks to a few years for 13 C) but also on long time scales (a few decades to several centuries for 14C). They are therefore very powerful tools for constraining conceptual schemes that are far from the variables measured in the field. On one hand, the stable carbon composition (13C) of soil organic matter contains information regarding the occurrence of C3 (some grasses, all trees) and C4 (only grasses) plant species in past plant communities. Indeed, whereas δ13C of C3 plants is about -26‰, the δ13C of C4 plants is higher, close to -12‰ [O’Leary, 1981]. This large difference allows to clearly decipher between these two types of plants. Observing a pair of profiles, close together, that underwent the same climate and the same management history, except during the last years when the vegetation (C3 or C4) of one of theme was replaced by another type of vegetation (C4 or C3 respectively) allows, by measuring soil δ13C, to quantify the contribution of the new vegetation in the carbon stock. Knowing the conversion year, it furthermore allows to define the dynamics of the more recently introduced carbons within a heterogeneous pool of carbons aged from some years to several millennia. This approach was introduced by Balesdent in the 1980's [Cerri et al., 1985; Balesdent et al., 1987] and is now commonly used in soil science (e.g. [Trumbore, 1993; Trumbore et al., 1995]. Likewise besides the natural chronometer, 14C isotope act also as tracer thanks to the bomb peak of the 1960s [Delibrias et al., 1964; Hua et al., 2013]. Atmospheric bomb testing in the late 1950's and early 1960's yielded for the abrupt increase of atmospheric 14C concentration that doubles in 2-3 years. By exchange with ocean and terrestrial reservoirs, it decreases since but still remains above the natural background (F14C=1). As any other carbon isotopes, this 14C was metabolized by the vegetation and transferred to soil. By measuring 14C activity of soil sample and looking at the high values, it is possible to evaluate the amount of carbon introduced into the soil since the 1960s. We here have another temporal constraint on soil organic dynamics. The idea arose very soon after the evidence of the bomb peak [Scharpenseel and Schiffmann, 1977; Balesdent and Guillet, 1982].

The methodology used in this thesis is based on three main tasks: 1) to compare existing products that provide data on carbon stock in soils on a global and regional scale 2) to measure carbon isotopes on two profiles of the same soil under two different land covers 3) to constrain the LSM ORCHIDEE using the radiocarbon 14C. The main goal of the thesis was to look at different soil study tools (experimentation, databases and modeling) in order to better understand the mechanisms of carbon in soils at different spatial scales, and then to introduce a key tracer, radiocarbon (14C), to constrain carbon dynamics in soils in the ORCHIDEE continental biosphere model.

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The specific objectives of each chapter of this thesis are: Chapter 1. To investigate different products available that provide carbon stock in soils at different, global and regional scales and try to find possible reasons for the uncertainties associated with estimating the total mass of carbon in soils. Chapter 2. To highlight changes in a soil under two different land cover and their relations with past climatic changes. Radiocarbon dating was used to estimate soil organic matter chronology and 13C was used as an indicator of the vegetation types in the local environment. Chapter 3. To constrain carbon dynamics in soils by introducing radiocarbon 14C into the ORCHIDEE continental biosphere model. Thanks to this tracer, we will evaluate the SOC residence times, in particular by looking at the 14C peak produced by atmospheric weapons testing and observed in the soils of different sites.

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CHAPTER 1: Large Differences In Global And Regional Total Soil Carbon Stock Estimates Based On SoilGrids, HWSD And NCSCD: Intercomparison And Evaluation Based On Field Data From USA, England, Wales And France

PUBLICATIONS Global Biogeochemical Cycles RESEARCH ARTICLE 10.1002/2017GB005678 Key Points: • Estimates of the total soil organic carbon stock for global land mask are still quite diverse. Uncertainties associated with soil bulk density are higher and apply to all regions of the globe, whereas those of the carbon concentration are mostly marked at high latitudes • Whatever the region considered, the SoilGrids-, HWSD-, and NCSCD-derived carbon stocks are not in agreement with the field data: Using field data gathered over USA, databases underestimate the stocks by 40% for SoilGrids and by 80–90% for HWSD. Using regional inventories over France and England and Wales, SoilGrids overestimates the carbon stock by 30% in the first case and by more than 150% in the second one • It is possible that previous estimates of the total organic carbon stock have seriously underestimated organic carbon stock in northern latitudes, peatlands, and wetlands

Correspondence to: M. Tifafi, marwa.tifafi@lsce.ipsl.fr

Citation: Tifafi, M., Guenet, B., & Hatté, C. (2018). Large differences in global and regional total soil carbon stock estimates based on SoilGrids, HWSD, and NCSCD: Intercomparison and evaluation based on field data from USA, England, Wales, and France. Global Biogeochemical Cycles, 32. https://doi.org/10.1002/ 2017GB005678 Received 27 MAR 2017 Accepted 12 DEC 2017 Accepted article online 18 DEC 2017

©2017. American Geophysical Union. All Rights Reserved.

TIFAFI ET AL.

Large Differences in Global and Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and NCSCD: Intercomparison and Evaluation Based on Field Data From USA, England, Wales, and France Marwa Tifafi1

, Bertrand Guenet1

, and Christine Hatté1

1 Laboratoire des Sciences du Climat et de l’Environnement/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

Abstract

Soils are the major component of the terrestrial ecosystem and the largest organic carbon reservoir on Earth. However, they are a nonrenewable natural resource and especially reactive to human disturbance and climate change. Despite its importance, soil carbon dynamics is an important source of uncertainty for future climate predictions and there is a growing need for more precise information to better understand the mechanisms controlling soil carbon dynamics and better constrain Earth system models. The aim of our work is to compare soil organic carbon stocks given by different global and regional databases that already exist. We calculated global and regional soil carbon stocks at 1 m depth given by three existing databases (SoilGrids, the Harmonized World Soil Database, and the Northern Circumpolar Soil Carbon Database). We observed that total stocks predicted by each product differ greatly: it is estimated to be around 3,400 Pg by SoilGrids and is about 2,500 Pg according to Harmonized World Soil Database. This difference is marked in particular for boreal regions where differences can be related to high disparities in soil organic carbon concentration. Differences in other regions are more limited and may be related to differences in bulk density estimates. Finally, evaluation of the three data sets versus ground truth data shows that (i) there is a significant difference in spatial patterns between ground truth data and compared data sets and that (ii) data sets underestimate by more than 40% the soil organic carbon stock compared to field data.

1. Introduction Climate change is “unprecedented with respect to scale, severity and complexity” (Bäckstrand & Lövbrand, 2015). There has been a drastic increase in the atmospheric concentration of carbon dioxide (and of other greenhouse gases) since the industrial revolution. This increase in atmospheric CO2 concentration is estimated to be about 31% since 1750 (Lal, 2004b), from the combustion of fossil fuel (405 ± 20 Pg; Le Quéré et al., 2015) and land use changes (190 ± 65 Pg; Le Quéré et al., 2015). Thus, there is an urgent need to understand the major role of carbon with respect to climate change and therefore the short- and long-term behavior of its various compartments. Within this framework, the global carbon cycle is typically composed by three large reservoirs interconnected through interchange pathways: the atmosphere, the terrestrial biosphere, and the ocean. The carbon exchanges among reservoirs are the result of different chemical, physical, and biological processes. Some stocks and flows among these reservoirs are relatively well quantified. For instance, about 120 Pg C yr1 of atmospheric CO2 is fixed by terrestrial biomass via photosynthesis (Janzen, 2004). In parallel, the flux of CO2 to the atmosphere from land use change was about 1.6 (0.5 to 2.7) Pg C yr1 for the 1990s (Denman et al., 2007). However, there is still much debate about the carbon stored in terrestrial ecosystems (Harris et al., 2012; Sanderman et al., 2017; Van der Werf et al., 2009) and in particular in soils (Köchy et al., 2015). While the ocean constitutes the largest active carbon pool (including organic and inorganic carbon), soils are a major component of the terrestrial ecosystem and the largest organic carbon reservoir on Earth. However, large knowledge gaps with regard to the functioning of this reservoir induce uncertainties in predicting its reaction to global change (Luo et al., 2016; Schmidt et al., 2011). The global mass of soil organic carbon is greater than the combined mass of carbon contained in the atmosphere and in living biomass (Ciais et al., 2013). Soils contain 3.3 times the size of the atmospheric carbon pool and 4.5 times the size of the biotic carbon pool (Lal, 2004a). In addition, soil is a nonrenewable natural resource and is quite reactive to human disturbance and climate change. Even minor changes in global

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soil organic carbon (SOC) mass may have pronounced effects on atmospheric CO2 concentrations and thus on climate change (Jones et al., 2005; Schuur et al., 2008). Therefore, a better understanding of soil organic carbon stock and flows is essential for better carbon management and climate change mitigation policies, and also to help parameterize global circulation models used to guide climate policy. Unfortunately, despite its importance, the global mass of SOC and its distribution in space are not well known (Jandl et al., 2014; Scharlemann et al., 2014). Although many estimates of global (Amundson, 2001; Stockmann et al., 2015) and regional (De Wit et al., 2006; Tarnocai et al., 2009) SOC stocks have been published and the overall average value is generally estimated to be around 1,500 Pg (Batjes, 1996; Köchy et al., 2015), this value tends to vary considerably. A meta-analysis using 27 studies estimating global SOC mass by Scharlemann et al. (2014) asserted that the median value of the estimated SOC mass is about 1,460.5 Pg and varies from 504 to 3,000 Pg. This implies that despite the large quantity of carbon stored as soil organic carbon and despite a great deal of research, there remains substantial uncertainty on the size of global SOC stocks and their spatial distribution (Scharlemann et al., 2014). This may be explained by the numerous factors controlling SOC dynamics and all the associated uncertainties as well as all the difficulties inherent in measuring and estimating carbon concentrations and bulk density (Köchy et al., 2015). Besides meta-analyses of field data, several global land information systems already exist. They are of paramount importance for land system models that fail to properly represent carbon stocks in soils (ToddBrown et al., 2014) thereby inducing strong uncertainties for future stock estimation (Nishina et al., 2014) and climate predictions (Arora et al., 2013; Arora & Boer, 2014). To our knowledge, however, these have never been compared. These products exhibit both similarities and differences in the methods and collected field data. Indeed, while based on the same regional data sources, they use different methods of stock estimation (pedotransfer functions), so uncertainties on global soil carbon stock may arise not only from sampling of soilprofile data but also from differing approaches to stock calculations and estimations. Objectives of our work are in line with the growing need for global and specific information on the carbon stock in soils so more accurate predictions can be made. Our aim is therefore to compare the total numbers of organic carbon stock as well as the spatial distribution of organic carbon and to assess important factors contributing to differences in estimations of soil carbon stocks.

2. Materials and Methods The following are two types of data used: 1. Databases estimating soil properties that draw on field data, yet use different gap filling and calculation/mapping approaches in order to estimate soil properties on a more generalized scale. These include SoilGrids (Hengl et al., 2017) and the Harmonized World Soil Database (HWSD) (Batjes, 2016) for global maps, as well as the Northern Circumpolar Soil Carbon Database (NCSCD) (Hugelius, Bockheim et al., 2013, Hugelius, Tarnocai, et al., 2013) for high-latitude regions. 2. Field data that consist of point measurements of carbon stocks on selected profiles available within the International Soil Carbon Network (ISCN) (International Soil Carbon Network (ISCN), 2012), the National Survey Inventory of England and Wales (NSI) (Bellamy et al., 2005), and the Réseau de Mesures de la Qualité des Sols of France (RMQS) (Arrouays et al., 2003; Jolivet et al., 2006) . In all cases, stocks are measured from the top of the mineral soil. For the data sets, the depth of 1 m was chosen in order to compare the products with each other (Table 1). Thereafter, to compare the databases with the measurements, the stocks are compared on the same minimum available depth. For instance, in the case of France, since the stock is supplied at a depth of 50 cm, the comparison with the SoilGrids database was made taking into account the stock at 50 cm depth only. 2.1. Data Sets Estimating Carbon Stocks in Soil 2.1.1. SoilGrids SoilGrids is a global soil information system containing spatial predictions for several soil properties (clay, silt and sand content, pH index, cation-exchange capacity …), at seven standard depths: 0 cm, 5 cm, 15 cm, 30 cm, 60 cm, 100 cm, and 200 cm (Hengl et al., 2014, 2017). Altogether approximately 110,000 world soil profiles are used to generate this data set. The data set (April 2017 version) can be downloaded here: ftp://ftp. soilgrids.org/data/. A selection of the SoilGrids data is available as zipped data sets with a growing selection

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Table 1 Summary of the Main Characteristics of the Data Used

SoilGrids HWSD NCSCD ISCN RMQS NSI

Scale

Initial grid resolution

Resolution after aggregation

Maximum depth

Number of layers

Data type

Global Global High latitudes Global France England and Wales

5 km 1 km 0.5° Not regular 2 16 km 2 5 km

0.5° 0.5° -

2m 1m 3m 1m 0.50 m 0.95 m

6 2 4 1 1 1

Estimated Estimated Estimated Measured Measured Measured

Note. The first three are databases using field measured data as the basis for the calculations, yet using different gap filling and calculation/mapping approaches, in order to estimate soil properties and cover maps at the global scale (SoilGrids and HWSD) and in high latitudes (NCSCD). The SoilGrids and HWSD data were aggregated to 0.5° (using a linear interpolation), the classical resolution used for land surface models running at global scale. The latter are the field data that consist of point measurements of carbon stocks based on stratified sampling available within the ISCN (global scale), the NSI (England and Wales), and the RMQS (France).

from 250 m to 10 km resolution. In this study, we used the 5 km resolution files (layers from 0 to 1 m depth), which we aggregated to 0.5° resolution using a linear interpolation (SoilGrids in tables and figures). For SoilGrids, we first calculate the carbon content (OCC (wt %)), the bulk density (BD (kg m3)), and the gravel content (G (vol %)) for each standard layer (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, and 60–100 cm) as below, according to Hengl et al. (2017): 1 1 X OCCab ¼ ðb  aÞðOCCa þ OCCb Þ (1) 2 ba X 1 1 ðb  aÞðBDa þ BDb Þ (2) BDab ¼ 2 ba 1 1 X ðb  aÞðGa þ Gb Þ (3) Gab ¼ 2 ba where a and b are the lower and upper limits, respectively, of the standard depths. Then, the soil organic carbon stock (SOC (kg m2)) was calculated, for each layer, using the following equation: ðSOCÞab ¼ ðOCCÞab ðBDÞab ð1  Gab ÞDab

(4)

where D is the layer thickness (m). Finally, the total SOC was then summed on the full soil depth (1 m). 2.1.2. Harmonized World Soil Database (HWSD) HWSD contains a collection of geographic information on soil physical and chemical properties from regional and national inventories all over the world (Batjes, 2016). The HWSD is organized in mapping units, each consisting of particular combinations of different soils (Food and Agriculture Organization/International Institute for Applied Systems Analysis/ International Soil Reference and Information Centre/Institute of Soil Science, Chinese Academy of Sciences/Joint Research Centre of the European Commission, 2012). HWSD database provides soil properties for the topsoil (0 cm to 30 cm) and the subsoil (from 30 cm to 100 cm depth) layers only. Soil organic carbon stock (SOC (kg m2)) was calculated, for each layer, using the following equation: ðSOCÞ030 cm ¼ ðOCCÞ030 cm ðBDÞ030 cm ð1  G030 cm ÞD030 cm

(5)

ðSOCÞ30100 cm ¼ ðOCCÞ30100 cm ðBDÞ30100 cm ð1  G30100 cm ÞD30100 cm

(6)

where OCC (wt %) is the organic carbon content, BD (kg m3) is the bulk density, G (vol %) is the gravel content or the coarse fragments and/or segregated ice content, and D is the layer thickness (m). The total SOC was then summed on the full soil depth (1 m). One particularity of this database is that there are two different ways to estimate soil bulk density from soil properties: (i) the SOTWIS bulk density values estimated by soil type and depth (HWSD_SOTWIS in tables and figures) and (ii) the Saxton bulk densities, calculated from equations developed by Saxton et al. (1986) (HWSD_SAXTON in tables and figures). This equation relates the bulk density to the texture of the soil only. TIFAFI ET AL.

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Thanks to these different methods of bulk density calculations, we were able to calculate two carbon stock values from the same data set. The data are available in the form of a 30 arcsec raster database and can be downloaded at http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/. As mentioned in Table 1, we aggregated the data to 0.5° resolution for this study too using a linear interpolation. 2.1.3. Northern Circumpolar Soil Carbon Database (NCSCD) NCSCD is a spatial data set, which quantifies storage of organic carbon in soils of the northern circumpolar permafrost region (Hugelius, Bockheim et al., 2013, Hugelius, Tarnocai, et al., 2013), from 45°N to 90°N. The spatial data covers permafrost-affected areas in Alaska, Canada, the contiguous U.S., Europe, Greenland, Iceland, Kazakhstan, Mongolia, Russia, and Svalbard. The NCSCD contains many thousands of polygons (>78,000) and information on soil organic carbon (kg m2) between 0 cm and 300 cm depth and at different spatial resolutions. For this database, only the total carbon stock values are directly available in kg C m2. The data set can be downloaded at http://bolin.su.se/data/ncscd/. The data are stored as multiple netCDFfiles and at different spatial resolutions: 1°, 0.5°, 0.25°, 0.1°, 0.05°, and 0.012°. In this study we used the 0.5° resolution data. 2.2. Field Data for Soil Carbon Stocks 2.2.1. International Soil Carbon Network (ISCN) The ISCN database (ISCN, 2012) currently includes data for over 257,000 individual soil layers collected from over 41,000 profiles all around the world but mainly located in North America. The 1 m soil stock provided (30,691 points over the world and mainly in North of America) is the sum of its component layers, which may have been sampled by horizon or depth (Boby et al., 2010; Bockheim et al., 1998, 1999, 2004, 2001, 2003; Cleve et al., 1993; Harden et al., 2006; Johnson et al., 2003; Jorgenson, 2000; Jorgenson et al., 2008, 2009; Kane et al., 2005; Kane & Vogel, 2009; Kristen et al., 2004; Michaelson et al., 1996; Myers-Smith et al., 2007; Neff et al., 2005; O’Donnell et al., 2011; Ping et al., 2005; Ping & Michaelson, 2010; Schuur et al., 2007; Vogel et al., 2008; Yarie & Lter, 2014). The data set can be downloaded at http://iscn.fluxdata.org/. 2.2.2. Network of Soil Quality Measurements (RMQS) The RMQS (Arrouays et al., 2003; Jolivet et al., 2006) database is based on 2,200 monitoring sites distributed uniformly over the French territory, according to a mesh 16 km × 16 km squares. It provides total carbon stock at the upper 50 cm of soil. Multiple added information is available on each site, e.g., vegetation description, environment description, profile sample collection history, and laboratory analyses. 2.2.3. National Soil Inventory of England and Wales (NSI) The NSI database covers England and Wales on a 5 km grid. Sites (5,662) were sampled for soil (Bellamy et al., 2005) and the total soil organic carbon was provided on 95 cm depth. NSI provides a very detailed soil description including stone abundance, root descriptions, and boundary information. 2.3. Soil Properties, Net Primary Productivity, and Climate Data In order to highlight the potential reasons for differences in the results given by the databases and also to determine relationships between soil properties and carbon stocks, different data provided by the databases (SoilGrids and HWSD) were used: organic carbon concentration (OC), bulk density (BD), clay, sand and silt content (soil texture), soil pH, and cation-exchange capacity (CEC). After the soil properties, external properties such as climate data (precipitation and temperature) from the Global Precipitation Climatology Center and the total net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) product were also used for the same purpose mentioned above. The MODIS-NPP data product was obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center, U.S. Geological Survey/Earth Resources Observation and Science Center, Sioux Falls, South Dakota (https:// lpdaac.usgs.gov/data_access), and the 0.5° global climate forcing product (1901–2007) was developed for the third phase of GSWP3 (http://hydro.iis.u-tokyo.ac.jp/GSWP3/), based on the 20th Century Reanalysis version 2 performed with the National Centers for Environmental Prediction land-atmosphere model (Compo et al., 2011). Table 1 summarizes the main features of the different databases. Table 2 gathers the total carbon stock calculated from the three databases/products. To enable comparison between the different databases, we

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Table 2 Total Organic Carbon Stock for the (0, 1 m) Upper Soil Layer at the Global and Regional Scales

Carbon stock (Pg C)

Global Boreal (60°N–90°N) North Temperate (30°N–60°N) Tropical (30°S–30°N)

SoilGrids

HWSD_SOTWIS

HWSD_SAXTON

3,421 1,161(34%) 1,376(40%) 865(25%)

2,439 390(16%) 890(37%) 1,061(44%)

2,798 807(29%) 1,227(44%) 696.6(25%)

NCSCD 290

Note. Values are for the SoilGrids, HWSD, and NCSCD databases. The bold numbers (percentage) are for the regional distribution of the global carbon stock.

decided to make the calculations on the same upper (0–1 m) layer and at the same spatial resolution (0.5°, a classical resolution used for land surface models running at global scale).

3. Results 3.1. Latitudinal Distribution of the Global Soil Carbon Stock on 1 m Depth The distribution of carbon stocks along a latitudinal gradient (Figures 1 and 2) for both global databases (SoilGrids and HWSD) presents a similar pattern with increasing values from the equator to the North Pole. This increase is more pronounced in the case of the SoilGrids database. Regarding the HWSD data set (Figures 1b and 1c), the carbon stock values are higher when calculations were made with SAXTON bulk density, particularly at the high latitudes. The major difference between the databases is observed at high latitudes (Figure 2); between 60°N and 90°N, the latitudinal profile shows a significant peak for SoilGrids and a slightly less marked peak for HWSD_SAXTON. Yet this peak is less important in the case of HWSD_SOTWIS and even lower with NCSCD. Between 50°N and 50°S, we note that the curve of SoilGrids and that of HWSD are closer. Finally, SoilGrids again shows an important peak, followed in this case by HWSD_SOTWIS.

Figure 1. Global distribution of carbon stock on the (0, 1 m) upper layer in the global scale (kg C m databases.

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) and for the different

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3.2. The Stocks Estimated by Each Product on 1 m Depth First, we note that the total carbon stock values (Table 2) greatly vary from one database to another and also within the same database (HWSD) according to the method of bulk density estimation (2,439 and 2,798 Pg C at global scale for SOWTIS and SAXTON, respectively). Furthermore, the value provided by SoilGrids (3,421 Pg C) is at least 1.4 higher than those provided by HWSD.

Figure 2. The total carbon stock (kg C m tude and for the different databases.

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Second, the regional distributions of carbon stocks over the three large latitudinal bands of the three databases (Table 2) are also not in agreement. The highest percentage (37 to 44%) of carbon is recorded in temperate regions, except for in HWSD_SOTWIS where the highest value is recorded in the tropics (44%). In boreal regions, the percentage is 34% for SoilGrids and 29% for HWSD_SAXTON. It is, however, lower (16%) when the SOTWIS bulk density is used. Finally, in the tropics, SoilGrids and HWSD_SAXTON have the lowest value (25%) and HWSD_SOTWIS has the highest (44%).

) on the (0, 1 m) upper layer per lati-

For the HWSD data set, higher SOC stocks are recorded in the boreal zone when using SAXTON bulk density estimations, while higher values are recorded in the tropics with SOTWIS bulk density values. Overall, in the boreal and temperate regions, SoilGrids recorded the highest values (up to four times the values of NCSCD for the boreal zone). For the tropics, values provided by the two data sets are almost close to each other. For the boreal zone, we note that the carbon stock estimated in all three cases is higher than the NCSCD estimations.

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Figure 3. The carbon profiles (kg C m corresponding layer.

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) on the global and regional scales; the symbol is located at the lower depth of the

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3.3. The SOC Distribution Within Depth (up to 1 m Deep) At the global and regional scales, the lowermost values of the HWSD carbon content profiles are lower than those of SoilGrids (Figure 3). However, both databases yield a similar general trend: high carbon content in the first 30 centimeters that then decreases with depth. The dissimilarity between the products/databases is almost the same for surface soil and for deep soil. At the surface and in deep soil, we recorded the highest value of SOC stock in the boreal region, and the lowest one in the tropics (Figures 3b–3d). In the boreal region, the difference between SoilGrids and HWSD and NCSCD is very pronounced. This difference is less significant between HWSD_SOTIWS and NCSCD. Overall, the lowest values are recorded with NCSCD. Figure 4. Global distribution of organic carbon concentration (kg/100 kg): Difference between SoilGrids and HWSD on the (0, 1 m) upper layer.

3.4. Identifying Sources of Discrepancies

SOC stocks are the result of equation (1), which multiplies carbon concentration by bulk density. Both represent a source of discrepancy between products. To estimate whether products differ as a result of carbon concentration, bulk density, or both, we compared each factor for products with available information (HWSD and SoilGrids) at 1 m depth. The difference in organic carbon concentration (OC %) distribution for both global databases, illustrated in Figure 4 (OC_SoilGrids-OC_HWSD), confirms once again that the main difference is located at high latitudes. Indeed, above 50°N, SoilGrids provides carbon concentration values much higher than those of HWSD, with

Figure 5. Global distribution of bulk density (kg m

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): Difference between SoilGrids and HWSD on the (0, 1 m) upper layer.

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Figure 6. Data set comparison: For every location that corresponds to an analyzed soil profile recorded in the field databases, the corresponding carbon stock was estimated from the databases and plotted against the observed stock.

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Table 3 2 Possible Correlation Between Soil Organic Carbon Stock (kg m ) of a Given Prodcut and Soil Properties, for the (0, 1 m) Upper Soil Layer of the Same Product Clay content (%)

SoilGrids HWSD_SOTWIS HWSD_SAXTON

Sand content (%) 2

Silt content (%) 2

Slope

Intercept

R

Slope

Intercept

R

0.20 0.75 0.67

34 45 41