Scholarly article on topic 'Global chemical weathering and associated P-release — The role of lithology, temperature and soil properties'

Global chemical weathering and associated P-release — The role of lithology, temperature and soil properties Academic research paper on "Earth and related environmental sciences"

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Abstract of research paper on Earth and related environmental sciences, author of scientific article — Jens Hartmann, Nils Moosdorf, Ronny Lauerwald, Matthias Hinderer, A. Joshua West

Abstract Because there remains a lack of knowledge about the spatially explicit distribution of chemical weathering rates at the global scale, a model that considers prominent first-order factors is compiled step by step and the implied spatial variability in weathering is explored. The goal is to fuel the discussion about the development of an “Earth System” weathering function. We use as a starting point an established model of the dependence of chemical weathering on lithology and runoff, calibrated for an island arc setting, which features very high chemical weathering rates and a strong dependence on lithology and runoff. The model is enhanced stepwise with further factors accounting for soil shielding and temperature, and the observed variation of fluxes is discussed in context of observed data from large rivers globally. Results suggest that the global soil shielding reduces chemical weathering (CW) fluxes by about 44%, compared to an Earth surface with no deeply weathered soils but relatively young rock surfaces (e.g. as in volcanic arc and other tectonically active areas). About 70% of the weathering fluxes globally derive from 10% of the land area, with Southeast Asia being a primary “hot spot” of chemical weathering. In contrast, only 50% of runoff is attributed to 10% of the land area; thus the global chemical weathering curve is to some extent disconnected from the global runoff curve due to the spatially heterogeneous climate as well as rock and soil properties. The analysis of carbonate dissolution reveals that about half of the flux is not delivered from labeled carbonate sedimentary rocks, but from trace carbonates in igneous rocks as well as from siliciclastic sediment areas containing matrix carbonate. In addition to total chemical weathering fluxes, the release of P, a nutrient that controls biological productivity at large spatial scales, is affected by the spatial correlation between runoff, lithology, temperature and soil properties. The areal abundance of deeply weathered soils in Earth's past may have influenced weathering fluxes and P-fuelled biological productivity significantly, specifically in the case of larger climate shifts when high runoff fields shift to areas with thinner soils or areas with more weatherable rocks and relatively increased P-content. This observation may be particularly important for spatially resolved Earth system models targeting geological time scales. The model is discussed against current process knowledge and geodata with focus on improving future global chemical weathering model attempts. Identified key processes and geodata demanding further research are a) the representation of flowpaths to distinguish surface runoff, interflow and baseflow contributions to CW-fluxes, b) freeze-thaw effects on chemical weathering, specifically for the northern latitudes, c) a more detailed analysis to identify to what extent the spatially heterogeneous distribution of Earth surface properties causes a decoupling of the Earth system rating functions between CW-fluxes and global runoff, as well as d) an improved understanding of where and to what extent trace or matrix carbonates in silicate-dominated rocks and sediments contribute to carbonate weathering. The latter demands e) an improved representation of carbonate content in lithological classes in the lithological representation of the Earth surface. Further improvement of the lithological database is needed for f) the age of rocks and g) the geochemistry of sediments with focus on unconsolidated sediments in the large basins. And clearly h) an improved global soil database is needed for future improvements with reliable soil depth, mineralogical composition as well as physical properties.

Academic research paper on topic "Global chemical weathering and associated P-release — The role of lithology, temperature and soil properties"


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Global chemical weathering and associated P-release — The role of lithology, temperature and soil properties


Jens Hartmann a'*, Nils Moosdorf3, Ronny Lauerwald a,b, Matthias Hindererc, A. Joshua West

a Institute for Geology, KlimaCampus, Universität Hamburg, Bundesstrasse 55, D-20146 Hamburg, Germany b Department of Earth & Environmental Sciences, Université Libre de Bruxelles, 50, av. F.D. Roosevelt, 1050 Bruxelles, Belgium c Institute for Applied Geosciences, Technische Universität Darmstadt, Schnittspahnstrasse 16,64287 Darmstadt, Germany d University of Southern California, Department of Earth Sciences, Zumberge Hall of Science, 3651 Trousdale Parkway, Los Angeles, CA 90089, USA


Article history:

Received 25 May 2013

Received in revised form 16 October 2013

Accepted 20 October 2013

Available online 31 October 2013

Editor: J. Fein



Chemical weathering Rock properties Earth system Global scale Phosphorus

Because there remains a lack of knowledge about the spatially explicit distribution of chemical weathering rates at the global scale, a model that considers prominent first-order factors is compiled step by step and the implied spatial variability in weathering is explored. The goal is to fuel the discussion about the development of an "Earth System" weathering function. We use as a starting point an established model of the dependence of chemical weathering on lithology and runoff, calibrated for an island arc setting, which features very high chemical weathering rates and a strong dependence on lithology and runoff. The model is enhanced stepwise with further factors accounting for soil shielding and temperature, and the observed variation of fluxes is discussed in context of observed data from large rivers globally.

Results suggest that the global soil shielding reduces chemical weathering (CW) fluxes by about 44%, compared to an Earth surface with no deeply weathered soils but relatively young rock surfaces (e.g. as in volcanic arc and other tectonically active areas). About 70% of the weathering fluxes globally derive from 10% of the land area, with Southeast Asia being a primary "hot spot" of chemical weathering. In contrast, only 50% of runoff is attributed to 10% of the land area; thus the global chemical weathering curve is to some extent disconnected from the global runoff curve due to the spatially heterogeneous climate as well as rock and soil properties. The analysis of carbonate dissolution reveals that about half of the flux is not delivered from labeled carbonate sedimentary rocks, but from trace carbonates in igneous rocks as well as from siliciclastic sediment areas containing matrix carbonate.

In addition to total chemical weathering fluxes, the release of P, a nutrient that controls biological productivity at large spatial scales, is affected by the spatial correlation between runoff, lithology, temperature and soil properties. The areal abundance of deeply weathered soils in Earth's past may have influenced weathering fluxes and P-fuelled biological productivity significantly, specifically in the case of larger climate shifts when high runoff fields shift to areas with thinner soils or areas with more weatherable rocks and relatively increased P-content. This observation may be particularly important for spatially resolved Earth system models targeting geological time scales. The model is discussed against current process knowledge and geodata with focus on improving future global chemical weathering model attempts.

Identified key processes and geodata demanding further research are a) the representation of flowpaths to distinguish surface runoff, interflow and baseflow contributions to CW-fluxes, b) freeze-thaw effects on chemical weathering, specifically for the northern latitudes, c) a more detailed analysis to identify to what extent the spatially heterogeneous distribution of Earth surface properties causes a decoupling of the Earth system rating functions between CW-fluxes and global runoff, as well as d) an improved understanding of where and to what extent trace or matrix carbonates in silicate-dominated rocks and sediments contribute to carbonate weathering. The latter demands e) an improved representation of carbonate content in lithological classes in the lithological representation of the Earth surface. Further improvement of the lithological database is needed for f) the age of rocks and g) the geochemistry of sediments with focus on unconsolidated sediments in the large basins. And clearly h) an improved global soil database is needed for future improvements with reliable soil depth, mineral-ogical composition as well as physical properties.

© 2013 The Authors. Published by Elsevier B.V. All rights reserved.

☆ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Corresponding author. E-mail addresses: (J. Hartmann), (AJ. West).

0009-2541/$ - see front matter © 2013 The Authors. Published by Elsevier B.V. All rights reserved. 016/j.chemgeo.2013.10.025

1. Introduction

Understanding the evolution of landscapes and biogeochemical cycles at the Earth's surface relies on knowledge about spatial and temporal variations in chemical weathering and their controls. Weathering influences biogeochemical cycles via CO2-consumption, mobilization of dissolved inorganic carbon, and release of phosphorus as well as other beneficial nutrients for ecosystems. The past decades have seen a range of studies exploring chemical weathering covering the total range of scales and using a range of approaches, including laboratory experiments, in-depth studies of weathering at specific sites, and global compilations of stream, river, and soil chemistry, as well as different modeling approaches to link these observations (Gaillardet et al., 1999; Anderson et al., 2004; Lerman et al., 2007; Navarre-Sitchler and Brantley, 2007; Godderis et al., 2009; Brantley et al., 2011; Maher, 2011). Complementary to this work have been efforts to assess the spatially explicit characteristics of weathering, in other words, efforts to develop spatial models that capture the most relevant variability in weathering rates across the Earth's surface (Godderis et al., 2009; Roelandt et al., 2010). So far there exists no globally, spatially explicit assessment of chemical weathering rates at a resolution that allows inclusion of the contribution of small islands or resolves arc areas. However, these small areas where postulated to contribute over-proportionally to the global chemical weathering flux (Hartmann and Moosdorf, 2011; Gaillardetet al., 2012).

Such spatially explicit approaches, while challenging to develop accurately, given the range of parameters that determine weathering fluxes, have significant potential for contributing valuable information to Earth system models (Ludwig et al., 1998, 1999; Donnadieu et al., 2006; Godderis et al., 2009; Roelandt et al., 2010). Drawdown of CO2 by weathering and its transformation to surface water alkalinity, as well as nutrient release, like P or Si, remains to be completely understood at the global scale.

Understanding the spatial variability in the release of P by weathering is vital for understanding Earth system interactions because this nutrient is often limiting ecosystem biomass production, specifically in humid tropical regions dominated by forests (Cleveland et al., 2011). Tropical forested regions contain about 25% of the total terrestrial biomass (Jobbagy and Jackson, 2000) and account for at least 33% of the global terrestrial NPP (Grace et al., 1995; Phillips et al., 1998; Beer et al.,

2010). Over long time scales, the release characteristics of P by chemical weathering are important for predicting ecosystem response to changing environmental conditions, and thus for identifying feedbacks in the global carbon cycle and the Earth system more broadly (Porder et al., 2007). Assuming that P release follows the rates of rock weathering, the relationship between P release and hydrology may vary considerably for different rock types (Hartmann and Moosdorf,

2011). This could have important global implications. Therefore, identifying the spatial distribution of P release at the Earth's surface today is a valuable baseline as a starting point for the analysis of release patterns due global change.

Many studies suggest that chemical weathering rates (CWR), and associated CO2 drawdown and release of nutrients, are in general a first-order function of several of the following factors: hydrology (runoff), lithology, rates of physical erosion, soil properties, and temperature (Kump et al., 2000; West et al., 2005; Navarre-Sitchler and Brantley, 2007; Godderis et al., 2009; Hartmann, 2009; Hartmann et al., 2009; Hartmann and Moosdorf, 2011). Quantifying the dependence on these controls is important for determining the spatial distribution of weathering globally. One approach to such quantification is to determine the basic parameters that describe weathering fluxes from river catchments in regions where there exist data on river chemistry across the main environmental gradients, e.g. across basins with different lithology, and runoff. These parametric relationships can then be applied at the global scale, including regions with similar properties but without river monitoring data (Amiotte-Suchet and Probst, 1993;

Hartmann and Moosdorf, 2011). The importance of additional factors can then be assessed by adding parameters to the model and exploring the match between predicted values and observed data, providing insights into each parameter's contribution to the variability of fluxes for certain environmental conditions.

This study takes as a starting point the parameterization of chemical weathering fluxes from Japan (Hartmann and Moosdorf, 2011), which has the advantage of covering a wide range of the weathering environments found globally, including many different lithology types as well as significant variability in runoff. Based on data from 381 mostly pristine catchments with relatively thin soils, a robust multi-lithological chemical weathering model was derived in previous work (Hartmann and Moosdorf, 2011), based solely on information about lithology and runoff. When compared to data from 39 large rivers from around the world, the results of the global application of this Japan parameterization (described in the following as the "island arc runoff-lithology model") provide a reasonable first-order description.

However, this approach on its own misses the effect of variability in two important aspects: (1) temperature, and (2) physical erosion. When physical erosion is low, the development of thick soils inhibits weathering rates — the "soil shielding" effect (c.f. Stallard, 1995). Neither this, nor the effect oftemperature, is captured in the parameterization of the island arc runoff-lithology model because the runoff variability in Japan dominates the weathering signal for given lithological classes. Here, we include temperature and soil shielding effects based on independent evidence oftheir quantitative importance. This enables us to: (i) quantify the importance of the soil-shielding effect at the global scale relative to the highly active weathering environment of an island arc with usually thin soils on average; (ii) determine the spatial distribution of chemical weathering as a function of area; and (iii) identify the relative importance of different lithological classes for present-day global weathering budgets.

The steps that we take in this study represent valuable progress towards developing a large scale approach using empirical methods to construct a global geodatabase superstructure (as suggested by Stallard, 1995) for describing weathering, associated CO2 consumption and P release. This structure provides the foundation for future work to include more detailed information, such as physical erosion (partly controlled by tectonic setting, land cover, land use and climate), hydrolog-ical flow path characteristics (e.g., groundwater versus surface flow versus interflow contributions to weathering fluxes), temporal variability of hydrological processes affecting soil moisture, groundwater table, or residence time, as well as secondary mineral reactions (Godderis et al., 2009), and ecosystem responses. Such approaches are valuable for the implementation in Earth system models, for which the computational resources for a purely "mechanistic" model are still not available to study for example long-term effects of variability of chemical weathering in the Earth system at high resolution. If enhanced carefully, this model structure could approach the complexity of purely mechanistic models, which are assembled based on processes at the microscale (Godderis et al., 2009). In contrast to the microscale approaches we start here with the large scale approach and try to narrow down to relevant drivers and pinpoint to missing knowledge to take the next steps. Combining both approaches would make it possible to assess more robustly the importance of dominant factors at various scales, in determining the spatial variability of chemical weathering at the global scale, and to analyze local and regional changes due to global change, with potential relevant influences on feedback mechanisms in the Earth system.

2. Methods

2.1. The island arc parameterization: runoff-lithology-based chemical weathering rates

The basic component of this analysis was the application of a chemical weathering model developed using data on river chemistry from

the highly active weathering area of the Japanese Archipelago, including 381 river catchments (Hartmann and Moosdorf, 2011). This model described weathering flux as a function of runoff, for individual lithological classes of the global lithological map database GLiM (Hartmann and Moosdorf, 2012). Chemical weathering rates of silicate dominated lithological classes were represented by a linear function of runoff for each class, applied to each grid cell (1 km2) of the resampled geodatabase:

FCW-Li-q,i = bi * q

with FCW-Li-q being the chemical weathering rate (t km-2 a-1), bi the factor for each lithological class i, and q the runoff (mm a-1; bi factors listed in Appendix 1). The chemical weathering rate (CWR) was defined as the specific fluvial export of total Ca + Mg + Na + K + SiO2, and carbonate-derived CO3, in t km-2 a-1. Note that Hartmann and Moosdorf (2011) defined CWR as Ca + Mg + Na + K + Si (not SiO2); the "O2" for SiO2 was included here for a better mass loss comparison with respect to CaCO3 and with respect to related literature. Carbonate dissolution can significantly contribute to CW-fluxes from silicate-dominated lithological classes (e.g. Mast et al., 1990; Hartmann and Moosdorf, 2011; Moosdorf et al., 2011b). Thus the proportion of CaCO3 on CW-fluxes was estimated based on the Ca-excess (Ca-fluxes not attributed to chemical weathering of silicate minerals), calculated for certain lithological classes and assuming that those Ca-fluxes would represent CaCO3-dissolution (Table Appendix 1). Thus bi could be represented by two parameters given the relative contribution from silicate and carbonate weathering: (Appendix 1):

'carbonate "" bsilicate

The calibration catchments in Japan did not provide data for "pure" carbonate or for evaporite lithologies. For these lithologies, the GEM-CO2-model equation for consumption of atmospheric CO2 (Amiotte-Suchet and Probst, 1993,1995) was adapted. The atmospheric CO2 consumption predicted by the GEM-CO2 model is assumed to equal the CO32- liberation from carbonate weathering. The CO32- liberation from carbonate rock was related to cation weathering via a stoichiometric factor considering the molar weight of carbon and the average carbonate rock composition (Hartmann et al., 2012). For carbonate dominated lithological units (SC), silicate weathering was neglected. Even if the rock composition of such a unit is evenly split between carbonate and siliciclastic sediments, the silicate weathering would only be responsible for a very small part of the element fluxes because of the much greater weatherability of carbonates (Meybeck, 1987; Gaillardet et al., 1999; Moosdorf et al., 2011b).

Although evaporites are known to dissolve quickly (Meybeck, 1987), no globally applicable equations for their weathering rates with runoff are available. Here the weathering rates from silicates and carbonates were considered solely, and the applied equation scheme represents the chemical weathering rates of rocks without contribution from evap-orites other than carbonates embedded in this lithological class. To acknowledge carbonate weathering in evaporite areas the equation for carbonate rocks is used, which would provide insights about the range of carbonate release from these areas.

The island arc runoff-lithology model, adapted to include equations for carbonate chemical weathering, was then applied to determine global chemical weathering fluxes using data on spatial variation in rock type from the global lithological map GLiM (Hartmann and Moosdorf, 2012) and runoff data of Fekete et al. (2002), resampled to 1 km x 1 km over the ice free areas.

2.2. Observed weathering fluxes from selected large catchments

The survey data from 49 sampling locations of large river catchments from different sources (Martins, 1982; Edmond et al., 1995; Edmond et al., 1996; Probst et al., 1992; Tardy et al., 2004; Cochonneau et al., 2006;

McClelland et al., 2008; Richey et al., 2008), were carefully revised and some locations obviously not representing seasonal variability were excluded (n = 10). To validate the modeled weathering rates and to calibrate a soil shielding factor, the chemical weathering rates within the 39 remaining large river catchments were used. Six of these catchments are located in the Northern high latitudes and drain to the Arctic Ocean; the remaining 33 catchments are located in the Tropics (Table Appendix 2). The chemical weathering rates were calculated from average concentrations of weathering derived river water concentrations of the major cations and silica, and the long-term average annual runoff after Fekete et al. (2002).

The concentrations of major ions and dissolved silica were weighted by reported instantaneous discharges, or, if these were unavailable, long-term averages of monthly runoff after Fekete et al. (2002). The weathering derived concentrations of major cations were calculated based on a precipitation correction. For this, it is assumed that solutes from wet deposition have molar ratios similar to that of sea water (Wilson, 1975; Keene et al., 1986). To correct for atmospheric inputs a sea salt composition with the molar ratios of Ca/Cl = 1.89E - 02, Mg/Cl = 9.67E - 02, Na/Cl = 8.59E - 01, K/Cl = 1.87E - 02, Si/Cl = 1.67E - 04 after Wilson (1975) was subtracted from the averaged concentrations of major ions until the concentration of chloride was zero.

2.3. Phosphorus mobilization by chemical weathering

It is assumed that release of P from chemical weathering to soils and ecosystems is proportional to the release of SiO2 and cations in the long-term based on the average geochemical composition of the lithological classes. P-release was thus calculated combining the basic equations describing chemical weathering fluxes with the geochemical data per lithological class presented in Hartmann et al. (2012) using the approach described in Hartmann and Moosdorf (2011), but modifying the CWR-equation by a temperature and a soil shielding term as explained below in detail (Eq. (3)). The P-contents in lithological classes relative to the SiO2 and the major cation content are given in Appendix 1 Table A1-2, last lines. The P-release FP is thus calculated as FP = bre]ative

P-content * Fsi + cations-

3. The effects of temperature and soil shielding

Weathering fluxes based on the straightforward runoff-lithology-model are shown in Fig. 1, comparing predicted and calculated fluxes for large tropical and arctic catchments. This simple, two-parameter island arc model (Eq. (1)) describes weathering fluxes at the global scale remarkably well, in agreement with the previous observations of first-order correlation between runoff and weathering flux (Gaillardet et al., 1999, 2012). However, there remains some scatter in the plot in Fig. 1, with some of the natural variability clearly not captured by the model. Moreover, there are areas of the Earth's surface where the island arc model over-estimates weathering rates, for example in the Amazon basin, where the model suggests that the lowland Amazon should have among the highest weathering rates (Fig. 2, Appendix 2) while data suggest that this area actually has one of the world's lowest considering hydrological conditions (Gaillardet et al., 1999).

Two important effects on weathering, identified in independent laboratory and field investigations, are the temperature and soil-shielding effects, and these may explain some of the inaccuracy. Here we incorporate these effects into the calculation of global weathering fluxes addressing in addition to Eq. (1) the temperature effect, using the Arrhenius term, and a term for the soil shielding:

FCW,i = FCW-Li-q,i(lithology, runoff) * FTi(Temp) * Fs(soil properties), (3)

where FCW-Li-q, i represents the chemical weathering flux in accordance to Eq. (1) as a function of runoff and lithology, the temperature effect is

C) Japan Arc Model (no soil shielding; corrected for temp. effect) 450 r

400 350

20 40 60 80

Observed flux cations + SiO2 (Mt a-1)

d) Japan Arc Model (corrected for soil shielding + temp. effect) 180 r

1 160 'a

3 120 el

"g 100

Observed flux cations + SiO2 (Mt a-1)

Fig. 1. Calculated versus modeled weathering fluxes of cations plus dissolved silica (SiO2) for the a) Japan island arc lithology-runoff model (top-left), b) the runoff-lithology-soil shielding model (top-right), c) the runoff-lithology-temperature model without soil shielding correction (bottom-left) and d) the runoff-lithology-temperature-shielding model (bottom-right). For the latter model the outflow of the Amazon and two upstream catchments is corrected (shown by arrows) for the large overestimation caused by the Rio Negro, which is largely overestimated, because it is a black water river and the applied soil shielding function as well as soil data do not match with field observations. The solid line represents the 1:1 line between calculated and modeled cation plus silica fluxes.

expressed as FT, j = exp(-Ea, ¡/R*(1/T — 1/T0)), with Ea, j being the activation energy for a certain lithological class at point of interest (Table 1), R the gas constant, and T the temperature in Kelvin. T0 is the average reference temperature of Japan (284.15° K = 11 °C) to adjust for the temperature effect relative to the reference area of the mobilization term FCW-Li-q, i. The soil shielding term FS represents effectively a reduction term for certain identified soil types causing a reduction in CWR of the underlying lithological class i. This term was estimated based on field data (Appendix 2).

3.1. Including the temperature effect on chemical weathering

Chemical weathering is known to be influenced by temperature (e.g. White et al., 1999). Previous studies based on field research suggest (Table 1) that this dependence can be described by an Arrhenius relationship with a certain range of "apparent" activation energies for felsic and mafic lithological classes. These activation energies are included in the temperature correction of the model Eq. (3) following an Arrhenius-type equation, normalized to the average temperature of the calibration catchments (11 °C; Fig. 2).

The temperature effect on chemical weathering has been studied in general only for igneous rocks or (metamorphic) sedimentary rock types close to them in geochemistry and mineralogy (Table 1). The terrestrial surface is characterized by about 2/3 sedimentary type lithologies,

and there is little data on temperature-dependence of chemical weathering for these specific rock types based on river chemical fluxes. However, the reported activation energies derived from felsic and mafic lithological classes largely converge on a similar range, independent of applied field data region. Given this, according to typical averages of literature data (see Table 1), a temperature correction has been applied to silicate weathering fluxes in the global calculation assigning one correction factor to each lithological type in Table 1 with the exception of carbonates. An activation energy of 60 kJ/mol was assumed for all "felsic"-type lithologies, including sedimentary rocks, while 50 kJ/mol was used for basic rock types (VB: Basic volcanic rocks; VI: Intermediate volcanic rocks; PB: Basic plutonic rocks). Pyroclastics (PY) are composed of a significant amount of glass and therefore a mixed activation energy was assumed (0.5 * 42 + 0.5 * 50 = 46) based on Table 1.

Field observations suggest that carbonate draining catchments are in general saturated with respect to calcite (Barth et al., 2003; Szramek and Walter, 2004; Szramek et al., 2007,2011). As the solubility of calcite is inversely correlated to temperature, this probably counteracts the increased dissolution rate based on the identified activation energy (Table 1) (Plummer and Busenberg, 1982). Thus, in the absence of globally representative and clear field data, and considering that in addition to saturated runoff water an uncertain proportion of unsaturated runoff would contribute to total runoff, carbonate weathering rates are not corrected for a temperature effect.

Fig. 2. Calculated chemical weathering rates applying the island-arc runoff-lithology functions, a) temperature corrected, but without soil correction (top), and b) the runoff-lithology-temperature model corrected by the shielding function, with a soil shielding factor of 90% (bottom).

Including the temperature effect significantly changes the the temperature effect on its own leads to significant overglobal distribution of weathering to very high rates in the warm, estimation of weathering fluxes in many settings, in the humid humid tropics (Fig. 1). When compared to the river data, adding tropics (Fig. 1).

Table 1

Typical activation energies for basalt, granite/felsic rock as well as calcite or volcanic glasses (the latter two are based on laboratory experiments).

Activation energy Reference Rock type Note


42 Dessert et al. (2001) Basalt

50 Navarre-Sitchler and Brantley (2007) Basalt For river data

59 Gislason et al. (2009) Basalt Average of catchments

50 Sak et al. (2004) Basalt

50 Average basalt

74 Westetal. (2005) Felsic

69 Rasmussen et al. (2011) Granite Regolith, humid conditions

49 Oliva et al. (2003) Granite Global data set, catchments > 1000 mm a-1 runoff

61 White and Blum (1995) Granite Global data set

52 Yadav and Chakrapani (2010) Multiple Granite, quartzite, diorite, and others

61 Average granite, felsic

24 Palandri and Kharaka (2004) Calcite

42 Wolff-Boenisch et al. (2004) Volcanic glass

Bold data means averages of the values above.

chemical weathering rate (CWR)

higher Japan humid tropics


i i i i temperature correction

i i periglacial processes j H soil shielding correction

_ -^L ? "


Fig. 3. Visualization of the effect of incorporating temperature and soil shielding into the island arc runoff-lithology model. For temperature correction the Arrhenius-term was used, with the average temperature of the Japanese catchments as reference and lithology-dependent activation energies (see Table 1). Soil shielding correction was applied with FS = 0.1 and assuming a binary code for soil shielding (yes or no) depending on the abundant soil type (Figs. 4 and 5).

in Fig. 1 from application of the runoff-lithology-temperature model show. However, sophisticated global datasets on soil depth and exact soil properties are missing. Thus, an average soil shielding factor was estimated for the following soil types from the FAO soil classification system (Fig. 4):

- Ferralsols (also called Laterites), Acrisols, Nitisols, and Lixisols are intensively weathered tropical soils. In case of Ferralsols, weathering profiles can reach down tens or even hundreds of meters.

- Histosols are peat soils, i.e. wetland soils, which are characterized by an upper organic layer of up to several meters in thickness which is rather "impermeable" and shields the underlying mineral substrate. The chemical composition of the runoff from peat lands is thus dominated by dissolved organic matter while the influence of rock weathering is often negligible.

- Gleysols represent a soil class characterized by a shallow ground water table and are also a good indicator for wetland areas with effective soil shielding.

32. Including a soil shielding factor

A significant effect that is not captured in the runoff-lithology-temperature model (Section 3.1) is the effect of soil shielding. This takes place when the principal hydrologic processes are isolated from active, weatherable minerals by thick, chemically depleted soils or other surface layers like wetlands (Edmond et al., 1995; Stallard, 1995; Boeglin and Probst, 1998; West et al., 2005). This effect was shown for humid tropical areas with thick, cation poor soils (Viers et al., 2000), and it has been suggested that stable shields are characterized by self-limiting weathering (Edmondetal., 1995; Goudie and Viles, 2012). In addition a shielding effect is probably also generally relevant for flat areas with high groundwater table, or e.g. in highlands that are not tectonically active (von Blanckenburg et al., 2004). In permafrost regions the observable soil shielding effect for large catchment areas may in contrary be weaker or absent because the cyclic thawing and freezing lead to an increased surface reaction area (as suggested, e.g., by Huh and Edmond, 1999), in addition to relatively high pore-water contents during thawing season.

When extrapolating a model from a region like the Japanese Archipelago to the world, this shielding effect should be considered, as results

An averaged soil shielding factor for identified soil classes possibly responsible for a relevant soil shielding effect was estimated by applying Eq. (3) to large rivers presented in Appendix 2 and comparing the differences to CWRs based on monitoring data, while the soil shielding factor FS was shifted from 0 to 1 in 0.1 steps for areas with identified soil types. For areas without those soil types the term FS was set to 1. This resulted in a best estimate averaged soil shielding factor of FS = 0.1 (Fig. 5), representing a reduction of 90% of the calculated fluxes for areas with soil shielding. In fact, model predictions remain slightly above the average observations for the largest tropical humid areas (see Fig. 1), suggesting that the soil shielding effect may be slightly stronger for these areas.

In assessing the fit of the model to the global river dataset, there is a trade-off between the strength of the soil shielding effect and the apparent global activation energy for weathering. In other words, for a lower activation energy, the implied soil shielding effect for those areas with shielded soils (Fig. 4) would be lower in humid, tropical areas. The values adopted here are based on the best estimate of global effective activation energies, but there is considerable uncertainty in these parameters. Attempts to parameterize the soil shielding effect for individual soil types failed due to the quality of geodata and heterogeneity of the large catchments considered in this study.

Fig. 4. Distribution of soil types assumed to cause a shielding effect. Source: Harmonized World Soil Database, Fao et al. (2009).

-0.85 -0.65 -0.45 -0.25 -0.05 0.15 0.35 0.55 0.75 log10 (modeled/calculated Cation+SiO2 flux rates)







Fig. 5. Ratio of predicted versus calculated fluxes after soil shielding correction (Fs = 0.1) of the temperature corrected island arc runoff-lithology model. The red line represents the theoretical normal distribution.


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4. Results

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4.1. General findings

Total global silicate and carbonate weathering fluxes from ice free areas amount to 851 Mta—1 and 588 Mt a-1, respectively (Fig. 3, Table 2). This, in sum, is about 20% below the global values reported by Gaillardet et al. (1999), who extrapolated the fluxes of the 60 rivers representing about half of the global discharge, using a different method than used here. Possible reasons for this difference are discussed below. The total carbonate contribution to calculated CW-fluxes from carbonate sedimentary rocks (SC), and also siliciclastic sedimentary rocks (SS), mixed sedimentary rocks (SM), acid plutonic rocks (PA) and meta-morphics (MT) amount to 41% of the total fluxes, which is lower than estimated by Gaillardet et al. (1999). If removing the soil shielding effect for carbonate fluxes the flux would increase by ~260 Mt a-1, which would result, still, in a lower flux than estimated by Gaillardet et al. (1999). Note that 44% of the carbonate weathering flux is attributed to silicate dominated lithological classes (Table 2) and that here, for the first time, these carbonate fluxes are allocated to distinct lithological classes in a spatially explicit manner.

The application ofthe chemical weathering model with a soil shielding factor of 0.1 reduces the global CWR by 44% compared to a scenario without soil shielding (Table 2). This implies that soil shielding presents a strong control on the global weathering fluxes. The soil shielding for the selected soil types reduces weathering fluxes by 47 to 66% for the following lithological classes: unconsolidated sediments (SU), siliciclastic sedimentary rocks (SS), basic plutonic rocks (PB), mixed sedimentary rocks (SM), metamorphics (MT), acid plutonic rocks (PA), and acid volcanic rocks (VA) (Table 2). Other lithological classes, like carbonate sedimentary rocks (SC), basic volcanic rocks (VB), intermediate volcanic rocks (VI) or pyroclastics (PY), are highly susceptible to weathering but are situated in areas less affected by the soil shielding in the present-day.

If normalized to their areal proportion, the lithological classes that comprise unconsolidated sediments (SU), as well as siliciclastic sedimentary rocks (SS), metamorphics (MT) and acid plutonic rocks (PA) contribute below average, and the classes that comprise carbonate sedimentary rocks (SC), basic volcanic rocks (VB), intermediate volcanic rocks (VI), pyroclastics (PY), intermediate plutonic rocks (PI) and basic plutonic rocks (PB) contribute above average to the global CW-fluxes (Table 2).

4.2. Phosphorous release

The spatial pattern of calculated P-release is similar to the CW-fluxes (Fig. 6, Table 3), suggesting that the spatial distribution of weathering



— ro

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tD tD O tN

tD O cn O cn tN

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Fig. 6. Estimated P-release by chemical weathering. Note that the high P-release-rates in the central part of the Amazon basin are partly due to missing information about the soil types being recognized to contribute to the soil shielding effect, the abundance of unconsolidated sediments being affected by several weathering cycles, and partly too high runoff estimates for this area in the global runoff dataset if compared to precipitation and evapotranspiration estimates. Addressing a soil shielding effect in those areas would result in a steeper area-flux rating curve for P release (Fig. 7).

rates has a larger impact on P-release by chemical weathering than the P-content of different rock types (based on Hartmann and Moosdorf, 2012). This is because the P-content varies less than runoff. However, P-content variations are within the same magnitude as the temperature effect, based on applied activation energies and the global distribution of temperature. High P-contents are found in rocks of some lithological classes with increased weathering susceptibility like volcanic rocks (Appendix 1). Particularly high P-release is associated with these rock

types when they are located in humid areas of relatively high temperature.

4.3. Spatial distribution of global chemical weathering fluxes

Weathering rates per grid cell were ordered by descending value to calculate a cumulative rating curve as a function of land area (Fig. 7). Areas of soil shielding are predominantly located in humid tropical

Table 3

P-release per lithological class. Colors imply high (green) or low (yellow) values in the last column.

Lithological classes (GLiM) Area Averaged P-release rate P-release total

106 km2 kg P km-2 a-1 106 kg P a-1 % total P release % Area Ratio (% total P-release) / (% total area)

Unconsolidated sediments (SU) 36.10 6.28 226.8 19.82% 26.9% 0.7

Siliciclastic sedimentary rocks (SS) 23.90 4.48 107.0 9.35% 17.8% 0.5

Mixed sedimentary rocks (SM) 21.14 6.56 138.7 12.12% 15.8% 0.8

Carbonate sedimentary rocks (SC) 10.89 14.46 157.5 13.77% 8.1% 1.7

Pyroclastics (PY) 0.94 84.71 79.5 6.95% 0.7% 9.9

Basic volcanic rocks (VB) 5.12 29.77 152.4 13.32% 3.8% 3.5

Intermediate volcanic rocks (VI) 2.52 37.54 94.8 8.28% 1.9% 4.4

Acid volcanic rocks (VA) 1.54 3.92 6.0 0.53% 1.1% 0.5

Basic plutonic rocks (PB) 0.95 37.17 35.4 3.10% 0.7% 4.4

Intermediate plutonic rocks (PI) 0.57 10.92 6.3 0.55% 0.4% 1.3

Acid plutonic rocks (PA) 8.48 6.63 56.3 4.92% 6.3% 0.8

Metamorphics (MT) 18.77 4.41 82.7 7.23% 14.0% 0.5

Evaporites (EV) 0.43 1.49 0.6 0.06% 0.3% 0.2

Ice and glaciers (IG) 1.51 1.1%

Water bodies (WB) 1.30 1.0%

No data (nd) 0.11 0.1%

Sum (or average) 134 8.53 1144

< 0% ' ■ ■ ■ '....................................

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Accumulated area

Fig. 7. Cumulative flux-area rating curves, ordered by value in a descending manner, showing the different distribution of weathering flux as well as P-release against the areal distribution of runoff.

Table 4

Chemical weathering rates and P-release for selected regions.

Region Area Chemical weathering Averaged Total flux rate P-release Averaged Total flux rate

106 km2 t km-2 a-1 106 t a-1 kg km-2 a-1 106 kg a-1

Carribbean Islands 0.212 24.9 5.3 23.6 5.0

Madagascar 0.594 17.8 10.6 14.8 8.8

New Zealand 0.268 37.6 10.0 27.9 7.5

South East Asian

Islands 2.940 72.1 212.0 66.3 195.0


Archipelago 0.373 29.3 10.9 34.1 12.7

Kamchatka 0.272 7.1 1.9 10.3 2.8

Sum/average 4.659 53.8 250.7 49.8 231.8

% of global 3.5% 502% 17.4% 584% 20.3%

Global 134.3 10.7 1,439 8.5 1,144

and subtropical regions of generally high weathering fluxes (Figs. 2 and 4). This means that areas with high weathering flux contributing most to the global CW-flux have greater global importance than they would in the absence of a soil shielding effect (compare the CW-flux rating curves in Fig. 7 that include or exclude soil shielding).

In total, 10% of the global land area contributes about 50% of the runoff fluxes to the ocean, but 70% of the CW-fluxes and 77% of the P-release (Fig. 7). Contributing areas in the rating curve differ between the scenarios, as flux-values per grid are ordered starting with the highest flux grids for each scenario (1 x 1 km basic resolution) and subsequently summed up using the next grid.

In general, islands with high mountains, arc areas, or areas of active volcanism are thought to contribute considerably above average to chemical weathering fluxes (Gaillardet et al., 1999; Hartmann and Moosdorf, 2011; Gaillardet et al., 2012). This is confirmed in our global analysis by highlighting CWRs of individual regions (Table 4, Fig. 8). Southeast Asia supplies a very large proportion of global chemical weathering fluxes considering its relatively small area and that a significant part of this area is covered by soil types affected by soil shielding (Fig. 4). It has a 6.7 higher chemical weathering rate than the world average. Despite its small land area (~1.9% of the terrestrial surface) the Southeast Asian islands contribute 14% of the total CW-fluxes and 16.8% of the global P-release. These high CWRs can be attributed both to this being an area of arc tectonics with accompanied volca-nism, as well as to the significant abundance of carbonates.

As can be seen from the spatial distribution of chemical weathering rates (Fig. 2) the northern latitudes do not contribute above the global average of 10.7 t km- 2 a- 1 in general. The six large northern catchments contribute 4% of the estimated total global chemical weathering fluxes, but these fluxes are probably underestimated by 53% (see Appendix A2, and discussion in Section 5.2.1).

While 38% of the global runoff into the oceans is intercepted by regional seas (Meybeck et al., 2007), about 36.6% of the released P is attributed to their tributary areas. However, these fluxes are globally unevenly distributed (Fig. 6). This observation may be of relevance for nutrient budgets of regional seas because eventually P released by rocks will be transferred to the coastal zones in dissolved or solid form affecting the coastal or regional sea's biogeochemical cycles. Note that P-release is considered here to be the flux from rocks and sediments to the soils and ecosystems and not to the rivers in the first place.

Fig. 8. Selected regions for detail analyses of weathering rates (Table 4). Madagascar was added for comparison.

5. Discussion

In this discussion we consider three main aspects of the results from this study:

(1) the aspects of the existing global model where uncertainty is largest and further work is needed to improve our spatially explicit understanding of weathering;

(2) the implications from the results of this work for chemical weathering in areas that have been identified as important loci of chemical weathering in previous work, namely volcanic arcs, high latitudes, and the foreland floodplains of orogenic systems;

(3) the relevance ofthe presented approach for understanding global present and past biogeochemical cycles.

5.1. Uncertainties and the need for better constraints on global chemical weathering models

The Japan-island arc model as the reference core for the presented model was developed based on catchment data focusing on runoff and lithology. The application ofthe model to the monitored Japanese catchments leads only to a small overall error of 5% overestimation for the total calibration dataset. While regional fluxes are well reflected, at the point scale the error could be larger of course. However, calculated and predicted CWRs show a strong correlation (r = 0.79), with a slight tendency to underestimate high CWRs. The residuals average at zero, which indicates that the parameter estimates are robust, as supported by the test statistics (Hartmann and Moosdorf, 2011). A larger uncertainty at the catchment scale is found when extrapolating to the global application here and this uncertainty is impossible to quantify where no monitoring data are available, or chemical weathering fluxes are difficult to distinguish from land use, e.g. due to liming of agricultural areas. In some cases the local to regional bias could be addressed and certain areas are discussed in detail in Section 5.2. The difference between calculated and modeled chemical weathering fluxes in tropical areas is significant for some areas like the Rio Negro (Fig. 1). This is to a large extent due to the limited information from the applied soil database (e.g., missing wetland representation). For most tropical rivers, published long-term monitoring data are missing. Many rivers are sampled only once, which introduces huge flux uncertainties and such data are less useful for model evaluations. In addition, there remains a lack of regionally calibrated chemical weathering models, which would allow a more detailed study of the "hot spot" regions and their contribution to the global fluxes. Differences in estimated fluxes comparing the parameterization for models from different regions have been clearly illustrated, e.g. for dissolved silica fluxes (Jansen et al., 2010) and alkalinity fluxes (Moosdorf et al., 2011b). Thus, the application of the island arc model based on Japanese data provides a reasonable initial global spatially explicit estimate, but there is wide scope for refinement.

There are a number of specific aspects of the weathering system where the current analysis highlights the significant shortcomings of general understanding:

5.1.1. Lithology

Using global averages for the geochemical composition of each lith-ological class introduced further uncertainty which is difficult to quantify. This affects the calculation in two ways: Firstly for the trans-ferability of the chemical weathering parameters used in Eq. (3), and secondly, and probably more relevant, for the calculation of carbonate contribution to CWRs based on the Ca-excess calculation using Ca to Na molar ratios of the lithologies (c.f. discussion in Hartmann and Moosdorf, 2011, with Moosdorf et al., 2011a,b). Indeed the global carbonate CWR seems low if compared to other global compilations based on observations from large rivers (e.g. Gaillardet et al., 1999; c.f. Section 4.1).

While for igneous rocks the average composition might be seen as relatively homogeneous, the geochemical composition of sediments, specifically of alluvial deposits, is highly heterogeneous. Their geochemical composition is variable, depending on sources and evolution of the grains. Consolidated sediments can incorporate a carbonate matrix, whose chemical weathering would result into significant contributions of excess Ca (the Ca proportion being released in addition to silicate-Ca-release) (Hartmann, 2009; Hartmann and Moosdorf, 2011). No global spatially resolved map is available describing the quantity of matrix carbonate (i.e. small amounts of carbonate disseminated within other lithologies). Thus, the fluxes of trace carbonates and further easily weatherable trace minerals, matrix carbonates from sediments, and carbonates from acid plutonics or metamorphic rocks all involve considerable uncertainty (Table 2). However, a study on North America (Moosdorf et al., 2011a,b) quantifying the Ca-excess ratios for comparable lithological classes as for the Japanese settings suggests that the values used here are reasonable; using a geochemical database of the USGS it could be shown that Ca/Na molar ratios vary locally in both sediments and igneous rocks.

Further, studies characterizing geochemical alteration of sediment fluxes in the foreland areas of the Himalaya and the Andes show that considering the spatial distribution of geochemical properties of sediments should improve global CW-flux estimates, specifically for unconsolidated sediments (Bouchez et al., 2012; Lupker et al., 2012). Alluvial deposits in Japan are characterized by significantly higher CWRs (considering comparable runoff conditions) than other unconsolidated sediments on the archipelago, suggesting that a combined influence of weathering age of grains and land use increase CWRs of those younger sediments if compared to older ones (Hartmann, 2009; Hartmann et al., 2010; Hartmann and Moosdorf, 2011). Since the potential land use effect on alluvial deposits and the relatively young age of grains due the small distance to the source rocks in the small catchments could not be ruled out, an application ofthis weathering function for alluvial deposits was not conducted at the global scale. However those examples show the relative importance of detailed knowledge of weathering material in highly active areas with small basins and steep relief, like South East Asia, if compared to the large basins like the Amazon. It could not be ruled out that in the former areas CWRs are underestimated, due the lithological composition and the lower weathering parameter for unconsolidated sediments, if compared to that of alluvial deposits (Hartmann and Moosdorf, 2011).

5.1.2. Hydrology

The present analysis uses a first-order classification oflithologies, and a relatively simple estimate of total runoff (Fekete et al., 2002). This ignores the important effects of hydrologic flowpaths on weathering reactions, for which a global representative database is missing. A more robust analysis would require attention to flowpath and to water-rock contact time (Hartmann et al., 2010; Maher, 2011). For example it was shown that on the Japanese Archipelago with increasing average gradient of slope of the landscape, dissolved silica fluxes decrease (Hartmann et al., 2010), most likely due to a dilution effect because with increasing gradient of slope the ratio of surface runoff to base flow runoff increases. Thus landscape relief metrics may serve as a valuable parameter, and it may be possible to develop simple model terms describing the influence of the ratio between surface runoff to interflow to groundwater flow on CW-fluxes based on relief and lithologic characteristics (Hartmann et al., 2010; Maher, 2011). However, this would probably require more detailed lithological classification (e.g. ascribing specific aquifer characteristics to each lithological class, like permeability; e.g. Gleeson et al., 2011), and understanding the hydrologic implications in detail, as well as a large enough hydrochemical monitoring database to derive a global parameterization to address this globally. Besides the negative non-linear correlation between dissolved silica and gradient of slope, a positive correlation between the ratio of non-silicate Ca-flux contribution to the total

CW-flux and gradient of slope for certain consolidated sediment and igneous lithological classes was observed using the data for the calibration of the Japan-island-arc model (Hartmann and Moosdorf, 2011). This effect counteracts the observed dissolved silica flux behaviors due to the higher dissolution rates of the affected Ca-bearing minerals. This would explain why a general relief parameter estimation representing hydrological conditions could not be identified for the Japan dataset for the aggregated weathering term "SiO2 + cations".

Further, plutonics or consolidated siliciclastics are expected to be dominated by fracture flow, while unconsolidated sediments are pore water aquifers — differences that influence water-mineral contact time and the available surface area for reaction.

Thus, while the broad classification in this analysis provides a firstorder picture of this variability, more detailed understanding of the lithology classes as well as detailed information on soil properties considering their regional/local hydrologic characteristics would be needed to implement a more mechanistic model for the influence of hydrologic flowpaths, e.g. distinguishing surface runoff, interflow and groundwater contribution as a first step. Such efforts demand the creation of several additional geodata layers, including a more sophisticated, globally calibrated and evaluated hydrological model. But such an effort would increase the potential for developing improved large scale weathering models.

5.1.3. Temperature

The activation energies applied in this analysis, derived from studies of the temperature-dependence of dissolution rates, yield global fluxes that are consistent with a reasonable extent of the soil shielding effect for humid tropical areas (c.f. data in Stallard and Edmond, 1983; Boeglin and Probst, 1998; Moquet et al., 2011). The calculated global values are sensitive to this term: e.g., assuming that the activation energies applied here would be altered by plus or minus 20 kJ/mol, the global weathering fluxes would increase by 23% or decrease by 14%, respectively. Thus, accurate knowledge of temperature dependence of weathering for each lithological class (or further subclasses considering regional settings) is important for more accurately quantifying global, spatially distributed chemical weathering. However, there are a number of complications to accurately represent the temperature-dependence of chemical weathering, including:

i. Apparent activation energies applied to global scale models and to certain lithological settings will always be just that - apparent activation energies - because they include not only the known (from laboratory experiment) activation energy for dissolution of a given mineral, but also effects like ecosystem activity (which is temperature dependent) or hydrology. This means they will always have an associated large uncertainty, unless all other biasing effects are considered spatially explicitly. In fact, some regional studies have found no, or only a weak, effect of temperature on weathering flux (Bluth and Kump, 1994; Riebe et al., 2004; Eiriksdottir et al., 2011; Hartmann and Moosdorf, 2011; Moosdorf et al., 2011b). This hints to the question, what span of temperature for sampled catchments applied to derive flux model parameterization is needed to gain robust temperature dependencies of weathering rates (c.f. discussion in Hartmann and Moosdorf, 2011).

ii. There are expected to be additional effects of increasing temperature on viscosity of water (Richards and Kump, 2003) and on gas fluxes in the soil column (Godderis et al., 2013). These will also influence the dissolution rates at mineral surfaces being in contact with water.

iii. There is little field data on the apparent temperature-dependence of sedimentary rock weathering, yet these lithologies cover about 2/3 of the terrestrial surface and are particularly important in large catchments such as the Amazon, where minerals pass through a range of different temperature regimes as they are transported to the oceans (Moquet et al., 2011).

iv. The role of saturation state in the weathering environment may modulate the temperature effect (e.g., Godderis et al., 2009; Maher, 2011; Godderis et al., 2012), because most activation energies identified for single minerals were determined far away from equilibrium. Not only might this change the apparent activation energy as applied at catchment scales, but it may suggest that the classical Arrhenius-type term solely is not the best first-order representation of the temperature-dependence of weathering, if a relevant proportion of weathering takes place close to equilibrium, where enthalpy-dependence should provide a better description. This leads back to the importance of the representation of hydrolog-ical pathways and the residence time of water being in contact with minerals (Section 5.1.2).

It doubtlessly remains a significant task for the chemical weathering community to identify appropriate apparent activation energies for application to whole areas, considering the hydrological state of the soils, rock properties, climate variability, and ecosystem effects. The challenge is to identify these activation energies with sufficient granularity as to be suitable for application of regional to global scale studies considering the available data and quality of data for the chosen model. Still, despite the many remaining uncertainties about the temperature-dependence of weathering, the results presented here indicate that already identified activation energies can be applied with reasonable results at the global scale considering the model structure.

5.1.4. Soil shielding

There is a particular challenge in trying to quantify the soil shielding effect because of limited detail in existing global-scale soil datasets (c.f. approach to estimate soil mineral contribution to chemical weathering fluxes in Roelandt et al., 2010). Current global soil databases do not provide reliable information from which soil shielding functions could be calculated and verified, making it necessary to infer the soil shielding factor by comparing model predictions with calculated weathering flux data from large rivers.

In this analysis, soil shielding is considered in a binary manner: if a certain soil type exists, then the estimated shielding factor of 0.1 (equal to a 90% reduction of the flux) is applied, derived from the temperature corrected island arc model for areas with soils contributing to this effect. The reality of soil shielding is clearly more complex. Arctic regions, which may have partly shielding soils, are also areas with a strong thaw-freeze effect that may introduce important further dynamics to the weathering system (Huh and Edmond, 1999), leading to an un-derrepresentation of fluxes in the current model applying the temperature term and soil shielding effect (see discussion in Section 5.2.1). On the other hand, for tropical, humid regions, the data (c.f. data in Appendix 2) suggest that the currently estimated soil shielding effect may be an underestimate.

For temperate regions insufficient data from large catchments not affected by strong anthropogenic activity were available to estimate an additional soil shielding factor or a better functional term describing the dependence on soil properties. Since the soil shielding effect from temperate and also from some subtropical areas would probably be lower and more variable than for the tropical humid areas, it could result in increased global fluxes compared to those estimated here.

The "uplift" of nutrients by plants (biological pumping) from the root zone to the upper soil layers via the plant system and adjacent deposition of plant litter (Lucas, 2001;Jobbagy and Jackson, 2004) may also be important in modulating the soil shielding effect. Dissolved matter taken up by ecosystems due to hydraulic lift is released when organic matter decays (c.f.discussion in Zakharova et al., 2005), and literature suggests that plants are capable of transporting significant amounts of solutes to the surface in areas with thick, highly weathered soils (Jobbagy and Jackson, 2004). It remains unknown to what extent this process influences the estimation of the chemical weathering rates and

the P-release at the global scale. This biological pump is still not recognized in continental to global scale weathering models, but may be important under certain local conditions. The parameterization of the biological pump effect would require further studies covering the possible ranges of soil or saprolith properties and ecosystem functioning.

Overall, there is simply not enough information to rigorously constrain the extent of soil shielding in specific environments (e.g. temperate regions or some hotspot areas as Southeast Asia) in order to derive a global parameterization for the various combinations of rock, soil and land cover types. Since the analysis here illustrates that the soil shielding factor is important to CW-fluxes at the global scale, improvement in understanding of these processes will clearly be vital to constraining Earth system biogeochemical processes from the global to the ecosystem scale. Parameters needed would be soil depth, physical structure (e.g., permeability, porosity) and mineralogical composition in addition to transfer functions of cations and nutrients via the biological pump for the various ecosystems.

5.1.5. Physical erosion and the effect of age

The soil shielding effect could arguably be replaced with a more mechanistically representative quantitative dependence on physical erosion, since shielding arises mostly from a denudation system in which weathering occurs at a "supply limit" (West et al., 2005; West, 2012). An additional value of directly considering physical erosion would be to address the effect on the aging of mineral surfaces (Hodson and Langan, 1999; White and Brantley, 2003; Pasquini et al., 2005). In the present analysis, the surface area for reaction and the aging of surface areas or the weathering history of sediment grains are not represented directly. Explicitly considering erosion rates would make this possible, at least in part (West, 2012), but would require an accurate spatially explicit determination of current and past erosion rates, which is not currently possible at the global scale due to the lack of calibrated spatially explicit models and associated data.

Full consideration of the age of mineral surfaces (White and Brantley, 2003) at the global scale would also require attention to the aging of rocks and unconsolidated material (Dahlgren et al., 1999; Yoo and Mudd, 2008; Goldsmith et al., 2010). This is true in two respects. One is through the aging of new extrusive igneous rocks; e.g., basalts are present at the Earth surface as relatively young basalts in highly active areas and as "old" flood basalt such as the Deccan traps or the Siberian flood basalt. The structure of these rocks as well as the age of surfaces, including the closure of fissures by secondary minerals impacting hydrological pathways, should influence weathering rates. To consider this effect, the lithological database must be enhanced at least by information of rock age, which might allow the regional scaling of the history of rocks representing effects of rock aging with weathering fluxes.

The age effect is also important in terms of sedimentary recycling through the rock cycle. The sediments in the central Amazon or the foreland of the Himalaya, for example, have been through several weathering cycles and become thus more depleted in mobile elements (Bouchez et al., 2012; Lupker et al., 2012). The weathering function for unconsolidated sediments (SU) derived from Japan is likely to yield excessively high weathering fluxes in such settings, because source regions of minerals are much closer to their deposition place in Japan. This may explain to a significant part why the central Amazon appears to have unreasonably high rates (Fig. 2) in areas with gaps in the reported presence of deeply weathered soil types (Fig. 4).

A reliable scaling between CWR and physical erosion for unconsolidated sediments would thus probably demand a term addressing effects of the transport from the source regions and multiple weathering cycles. Thus the age of minerals, the weathering history grains have faced, and the carbonate content of sediments is essential to capture in a physical erosion term which should substitute the soil shielding function as introduced here. The latter effect is not generally recognized, but the study of Moquet et al. (2011) suggests that there exists a significant

carbonate contribution to the chemical weathering fluxes in the upper parts of the Amazon.

5.1.6. Vegetation and soil processes

Rock weathering provides nutrients to plants and fungi and it is known that microorganisms and fungi are able to increase the dissolution rates of minerals (Eckhardt, 1979; Gadd, 2007; Montross et al., 2012). The impact of vegetation on chemical weathering has been shown by field studies (Brady et al., 1999; Moulton et al., 2000), which indirectly include the effects ofmicroorganisms. On larger scales, the impact of vegetation and the belowground ecosystems becomes more complex to identify. A parameter to integrate these processes, including the possible influence of the biological pump mentioned above, is land cover. For the element potassium, e.g., Jobbagy and Jackson (2004) estimated that plant uplift could cause a doubling of the exchangeable K-pool in the upper 20 cm of soil, and increase the Na, Ca and Mg pools. Further, silicon is affected strongly by the biological pump (Alexandre et al., 1997; Conley, 2002). Changes of land cover also can lead to large fluctuations in dissolved and biological silica fluxes from landscapes (Balogh-Brunstad et al., 2008; Struyf et al., 2010). Although Moosdorf et al. (2011b) showed an influence of land cover on alkalinity fluxes by chemical weathering, most recent efforts to disentangle quantitatively the different influences of various ecosystem types using a variety of global land cover data sets (e.g., Tateishi et al., 2003; Bartholome and Belward, 2005; Arino et al., 2007) on global chemical weathering rates were unsuccessful. This could be because of the similar influence on the magnitude of variation within the same climate zone, a potential covariation with climatic parameters like runoff and temperature, or simply the insufficient detail in available land cover classification systems to identify the land cover type signals. A further limitation may be due to the limited hydrochemical data, specifically the temporal resolution of data in databases covering large areas. Exceptions are some land use types representing anthropogenic rather than natural ecosystem effects, as discussed below (Hartmann et al., 2010; Hartmann and Moosdorf, 2011; Moosdorf et al., 2011b).

The present analysis also ignores important soil processes, such as the influence of clay mineral weathering and other processes related to secondary minerals (like adsorption and exchange of ions). Processes like cation exchange remain difficult to quantify at the global scale (Godderis et al., 2009). Given that all of these processes may significantly influence chemical weathering fluxes, there remains a need for a global spatially explicit assessment of soil processes in order to better quantify spatially explicit variability in weathering.

5.1.7. Land use

The model attempts to present pristine chemical weathering rates: the data from Japan applied for calibrating the island arc model are from the 1940s to 1950s and sampling locations were mostly from upstream areas of villages and therefore present as far as possible pristine catchments. Similarly, the global validation dataset of large rivers is comprised of large catchments, with relatively little anthropogenic impact.

This analysis therefore effectively ignores the effect of land disturbance. Actual weathering fluxes are likely to be larger in agricultural areas because of soil mixing by tilling, application of acids due to fertilization, or liming (Paces, 1983; Semhi et al., 2000; Oh and Raymond, 2006; Hamilton et al., 2007; Raymond et al., 2008; Pierson-Wickmann et al., 2009). Similarly, clear felling, despite later reforestation, is known to increase weathering fluxes at least temporarily (Balogh-Brunstad et al., 2008), and specifically those of silica bound previously in the form of soil biogenic silica (Conley et al., 2008; Struyf et al., 2010).

The additional contribution to present-day weathering fluxes from land use is difficult to quantify at the global scale because there are few studies with globally representative, appropriate parameters

for global application, and because it remains challenging to separate anthropogenic contributions to river solutes even for single catchments (c.f., Roy et al., 1999; Garcia-Esteves et al., 2007).

For continental to global scale chemical weathering products, only Moosdorf et al. (2011b) could estimate the changes of chemical weathering derived alkalinity fluxes due to land use and land cover. Jansen et al. (2010), Struyf et al. (2010), Moosdorf et al. (2011a) and Carey and Fulweiler (2012) suggest further that land use and land cover changes are a relevant factor for the mobilization of silica fluxes into rivers. Particularly interesting land cover types for future CW-flux estimates at the global scale are urban areas. They have been identified to strongly increase DIC fluxes (Baker et al., 2008; Barnes and Raymond, 2009; Moosdorf et al., 2011b) and cation fluxes (Feller, 2005; Williams et al., 2005) in rivers, which may be associated with increased weathering or use of groundwater for water supply in these areas. In case a relevant proportion of the variability of dissolved fluxes in rivers would be due to land use effects, it would be preferable to use the term "apparent" natural chemical weathering rates in the future for affected areas if the anthropogenic contribution could not be disentangled confidently.

5.2. Insights into weathering considering northern latitudes and global "hot spots"

5.2.1. Northern latitudes

The modeled weathering fluxes from the six large arctic catchments underestimate observed fluxes by about 50% if Eq. (3) is applied. However, it has been suggested that permafrost effects may increase chemical weathering rates by enhancing surface area for reaction, by mixing of soils, and by altering percolation patterns (Huh and Edmond, 1999). Altogether, for these arctic environments, the soil shielding effect in the present model is represented weakly, but current field data is not sufficient to parameterize the effect of the freeze-thaw process spatially explicitly in the empirical model. Only few studies exist that published data on arctic weathering fluxes and relevant processes (e.g., Gislason et al., 1996; Millot et al., 2003; Pokrovsky et al., 2005; Zakharova et al., 2005; Keller et al., 2007; Tank et al., 2012). More detailed field work needs to be carried out to specifically address the multiple processes affected by the freeze-thaw effect in permafrost areas, for identifying needed parameters in empirical based or mechanistic models.

Understanding processes in these regions is important for two reasons. First, high-latitude fluxes comprise a relevant portion of the total global flux. Based on the underestimation from the model, for the six largest arctic rivers would contribute about ~8% to the global CW-fluxes. Based on that those catchments represent about half of the area of arctic basins, which in total contribute 12% of the global runoff discharging into the Arctic seas from watersheds covering about 18% of the land mass (Fekete et al., 2002; Tank et al., 2012), it is reasonable to assume that the all arctic rivers contribute up to 16% to the global flux. This high value despite the low average mean temperatures can be explained by the high contribution from carbonate dissolution, which is one of the major drivers for weathering derived fluxes from the arctic river basins in total (c.f. Tank et al., 2012). This is consistent with no strong temperature dependence for carbonate dissolution in the weathering model. Second, understanding the effect of freeze-thawing specifically in active soil layers of permafrost areas (Keller et al., 2007) and identifying the relevant places and their properties could be essential to understanding the effect of changing climate, not only for the projected changes in the future, but also for the time periods of glacial-interglacial shifts of the Earth's system. Thus, accurately representing arctic CW-fluxes is relevant for completing the picture of global weathering.

5.2.2. Islands and areas of volcanic arcs

Gaillardet et al. (2012) hypothesized that orographically-driven chemical denudation of tropical island arcs may play an important

role as a long-term climate feedback. Other studies have also identified arc areas as regionally important loci of chemical reaction (Louvat and Allegre, 1997; Dessert et al., 2003; Hartmann et al., 2009; Schopka et al., 2011). Results from Table 4 (areas shown in Fig. 8) confirm the importance of such settings at the global scale, supporting the notion that the creation of tectonic arcs plays a substantial role in chemical weathering and land-ocean matter fluxes in the Earth system.

The arc islands in tropical and sub-tropical settings clearly play a particularly important role in the global picture, in part because of the globally high runoff in these settings (Fekete et al., 2002) accompanied by high solid matter fluxes (Milliman and Meade, 1983) impacting soil properties and thus soil shielding. This hints to the sensitivity of global land-ocean weathering fluxes to runoff changes specifically in humid tropical to subtropical island areas; changes in runoff in these regions are more likely to prominently influence the land ocean dissolved matter fluxes. However, the impact of rare, strong precipitation events (e.g., Typhoons) on areas with high weathering rates has not been studied so far in context of Earth system dynamics.

Regions like Kamchatka are less important for the global chemical weathering budget than Southeast Asia, Japan, or New Zealand (Table 4). This is partly due to the temperature effect at the high latitude impacting the CWR, and also because portions of the Kamchatka peninsula are also covered by a significant amount of siliciclastic and acid volcanic lithologies with lower relative modeled weathering rates than basic volcanic rocks (c.f. Appendix 1). This emphasizes the fact that the geochemical composition of exposed arc volcanic lithologies may contribute to their role in the global weathering budget. Over geological time scales, the abundance of subduction zones and related tectonic arc areas may control a significant proportion of chemical weathering related matter fluxes and their variation over time and may have a pronounced effect on the long-term carbon cycle, not only because of the CO2-consumption by chemical weathering but also because of the increased release of the nutrients silica and phosphorous. This implies further potential changes of the feedback functions among the compartments of the Earth system if the surface area or geochemical quality of arc areas change significantly with time (c.f. Section 5.3.2). This hypothesis, however, still needs to be tested, but potential tools for this tasks are available (c.f. approach in Godderis et al., 2012) if an appropriate reconstruction of the paleogeography including those areas is applied.

523. Foreland basins

The contribution of relatively "fresh" foreland basin sediments of major mountain belts such as the Andes or the Himalaya to the global chemical weathering fluxes could not be well resolved using the applied datasets. A detailed comparison of the runoff data used in the model in this study with hydrological measurements provided in Moquet et al. (2011) for the Amazon foreland areas and parts of the Andes suggests that the runoff used in this study may be too low, at least for these areas (and too high for some downstream areas). This results in a significant underestimation of the CWR from this very active weathering area. More than 4.7% of the calculated global CW-flux may be overlooked in our calculations if for the sampling location Solimoes at Tabatinga (Table A2) data in Moquet et al. (2011) are taken as reference, even though this catchment area represents only 0.65% of the terrestrial ice-free area. This example represents an extreme mismatch, but emphasizes that an appropriate spatially explicit representation of global runoff in relation to lithology is vital for accurately representing fluxes. The importance of accurate local runoff is illustrated by the difference in the rating curves of runoff and chemical weathering fluxes in Fig. 7. The steeper angle of the CW-flux curve for highly active weathering areas if compared to the runoff curve indicates an increased abundance of more "easy" to weather lithological classes in high runoff regions without given soils causing a soil shielding effect. This can be identified when analyzing the areal proportions of lithological classes for different runoff fields (Hartmann et al., 2009).

In addition, improvements of the spatial resolution of geochemi-cal properties in sediments are relevant for reliable CW-flux estimates. Results from the Ganga basin and the Amazon suggest that sediments become significantly cation depleted during their transit through floodplains in the foreland of the Andes or the Himalaya, and carbonate dissolution from certain areas may contribute significantly to the dissolved load of the rivers (Bouchez et al., 2012; Lupker et al., 2012). Thus either the geochemical properties of foreland sediments should be better spatially explicitly resolved for improved global CW-models or generalized models need to be developed characterizing the alteration of sediment geochemistry following the main flowpath of water from mountain ranges via the sediment floodplains to the ocean. The latter task would demand the inclusion of knowledge of the major water pathways and their history, specifically if improved global CW-models considering geological time scales are to be designed.

5.3. Implications for understanding global biogeochemical cycles

5.3.1. P release by chemical weathering

The results of this global study imply that soil shielding exerts a strong control on the global supply rate of P to ecosystems and soils by chemical weathering (Table 3). Specifically in Southeast Asian regions, and also in other humid tectonic arc regions, chemical weathering contributes substantially more P to the soil and ecosystems than in the central Amazon or Congo basins, despite a similarly high runoff (Table 4). A significant proportion of these areas with estimated very high P-release rates by chemical weathering are forested. Since the main plant nutrients are N, P, and K, and limitation by any of these nutrients is assumed to potentially restrict productivity (Tripler et al., 2006; Hyvonen et al., 2007), it remains an open research question how much and where chemical weathering steers these ecosystems (Porder et al., 2007; Vitousek et al., 2010). The relevance of this question is highlighted by a meta-analysis of Cleveland et al. (2011) for tropical forests, which indicates that some studies show that net-primary production (NPP) in tropical forests is limited by P availability, while other studies show that tropical forests have a sufficient labile P pool in the surface soil (c.f. references in Cleveland et al., 2011). This difference may be associated with variable soil shielding or other soil properties, as well as P-contents in underlying rocks and their susceptibility to weathering.

A reasonable hypothesis is that in areas of increased P-release the biosphere should be less P-limited, and NPP of ecosystems consequently increased, as long as soil processes (e.g. adsorption/incorporation of P to certain Fe-minerals) do not hinder the uptake of P by the ecosystem. Such a hypothesis could be tested through experimental studies and more detailed, mechanistic models linking chemical weathering rates to observed variability in P release and fate of P in the soil system (c.f. approaches to estimate P-content and sources in soils: Yang et al., 2012).

One important feature of the P-release rating curve (Fig. 7) is that it is steeper than the runoff curve and also steeper than the chemical weathering curve. This implies that lithological classes with increased P-content and high susceptibility to weathering are abundant in regions with high runoff and low soil shielding. The applied data for determining the rock P content are based on a global compilation of data (Hartmann et al., 2012). Because the P-content is highly variable for unconsolidated and consolidated sediments, e.g., due to differences in facies and genesis, local or even regional estimates are relatively uncertain and their uncertainty cannot be quantified without further more detailed, spatially resolved geochemical data. Significant regional improvements in P-release estimates can be expected if improved and regional-specific P-contents are attributed to the polygons of the GLiM lithological map used in calculating fluxes (Hartmann and Moosdorf, 2012). The present assessment of total P release is likely to be a conservative estimate, as 103,000 samples from two rock databases covering the U.S. (USGS, 2001,2008) providing P2O5 data indicate higher concentrations

than assumed here (0.27% for silicate dominated lithological classes, with a maximum of 2.8% among the 3404 carbonate samples).

In addition, biota may enhance the weathering of individual P-rich minerals to improve their nutrient supply (Moulton et al., 2000; Rogers and Bennett, 2004; Gadd, 2007; Uroz et al., 2009), increasing P-release relative to the estimates presented here, though the quantitative effects of this process on long-term P-supply to ecosystems by chemical weathering are yet unquantified and demand further research for a better parameterization in global CW-models, specifically if targeting geological time scales.

5.3.2. Relevance to Earth system models

Earth system models usually use a box model approach to analyze global matter cycles and feedbacks on time scales of the Phanerozoic (Arvidson et al., 2006; Berner, 2006). Within these models erosion and erosion related matter fluxes are scaled with global runoff, neglecting the spatial distribution of a soil shielding effect or spatial runoff-lithology correlations, which might have changed over the Phanerozoic period causing changes in land ocean chemical weathering fluxes (Gibbs et al., 1999). The likely importance of such processes or spatial inter-correlations between drivers of chemical weathering on geological time scales was demonstrated by work of the group of Godderis et al. (Donnadieu et al., 2006; Godderis et al., 2008; Godderis etal., 2012) and also by Gibbs etal. (1999), using early spatially explicit models. A recent study on the effect of vegetation, plant roots and fungal symbionts on the release of Ca and Mg by chemical weathering from silicates (Taylor et al., 2012) supports the idea that land cover and ecosystem abundance should be considered in a spatially explicit manner in Earth system models incorporating lateral land-ocean dissolved matter fluxes. However, the geodata used in the Taylor et al. study had a coarser resolution compared to the resolution in this study, which might mean that not all relevant hotspots of weathering were captured in their analysis (see discussion below).

Our results support the importance of the spatial correlation between rock types and climate, in particular for small areas, like volcanic arc islands, which contribute disproportionately to chemical weathering, CO2 drawdown and nutrient fluxes, in spatially explicit modeling approaches. This is particularly true because the global chemical weathering and P-release rating curves are different from the runoff rating curve (Fig. 7), as a result of spatial correlations between runoff, lithology (including the geochemical properties), soil shielding properties, and temperature. Spatially resolved Earth system models should also consider soil shielding if soil properties are not resolved explicitly taking into account mechanistically the relevant processes causing this effect.

The incorporation of the impact of phosphorous supply by chemical weathering on the global carbon cycle has not yet been carefully considered in Earth system models, with one exception known to the authors (Goll, 2012). The outcome of applying our model approach to a set of four state of the art Earth system models (Goll, 2012) suggests that, on geological time scales, chemical weathering feedback processes causing variability in P-release may be relevant to the coupled carbon and phosphorus cycles. Another relevant outcome was that even current, relatively highly resolved, Earth system models underestimate the global P-release by 25% compared to the estimates in this study, despite using the same model. This could partly be explained by unrepresented small arc areas (see Fig. 8) in the coarser geodata base of the Earth system models. One of the resulting problems is that lithological data representing small areas (which may be responsible for high fluxes) are replaced by other lithological classes when lithological and geochemical data are compiled to coarser resolutions of Earth system models. In the case of small islands, these may even be missing from the coarse resolution models (c.f. Moosdorf et al., 2010). Thus Earth system modelers should be aware of a possibly weak representation of hotspots.

6. Conclusions

We developed an empirical approach to determine spatially explicit weathering fluxes at the global scale and used this to assess the importance of specific determining factors, including runoff, lithology, temperature, and soil shielding. The results of this estimation are discussed considering remaining relevant research questions, gaps in parameteriza-tions, and missing geodata.

The global distribution of runoff and its coincidence with highly weatherable lithologies indicates that some humid tropical regions are hotspots of chemical weathering. This supports recent arguments for a strong hydrological control on weathering rates specifically in highly active areas, e.g. islands in subduction zone settings (Gaillardet et al., 2012). However, in other humid, tropical areas (e.g. precambrian cratons), soil shielding reduces chemical weathering fluxes significantly (Edmond etal., 1995; Stallard, 1995), despite the high runoff. As a result, at the global scale, runoff and chemical weathering fluxes are to some extent disconnected.

The application of a spatially explicit lithology-runoff-temperature-soil shielding approach results in about 20% less global CW-fluxes from silicates and carbonates than estimated by Gaillardet et al. (1999) based on 60 large rivers. This may be attributed to specific underestimates in parts of the spatially explicit model. Arctic river basin fluxes are probably underrepresented by 8% of the globally calculated flux. Fluxes in the foreland of the Amazon include at least 4.7% of the global flux that is missing due to inaccuracies in the hydrological data used in the model. Also taking into account that for temperate and subtropical areas, as well as arctic regions in general, the soil shielding effect is weaker than in humid tropical areas, global CW-flux values might well approach those of the previous study extrapolated using river data of catchments covering about 50% of the exorheic land surface.

Results illustrate that it is possible to scale P-release dynamically using chemical weathering functions by coupling lithological and geo-chemical databases. Many global ecosystem models or Earth System models assume that P-liberation by chemical weathering is effectively constant or release rates are based on data from the few available soil sites investigated (c.f. page 2267 in Wang et al., 2010). The results presented here highlight that the P-release rates by rock weathering vary significantly and should be taken into account in rock-soil-ecosystem models. Better understanding of the consequences of

Table A1-1

b-parameters for CWR from silicates (second column) and carbonates (third column) used in Eqs. (1) and (2). The total CWR in Eq. (1) was calculated using the sum of both parameters. Note that for the lithological class evaporites only an estimate for carbonate dissolution is provided. For the lithological classes VI and PI the parameters for VB and PA were chosen, respectively.

Lithological classes (GLiM) CW from silicates CW from carbonates

Unconsolidated sediments (SU) 0.021333 0

Siliciclastic sedimentary rocks (SS) 0.019699 0.0081

Mixed sedimentary rocks (SM) 0.021806 0.035915

Carbonate sedimentary rocks (SC) 0 0.151243

Pyroclastics (PY) 0.076876 0

Basic volcanic rocks (VB) 0.04054 0

Intermediate volcanic rocks (VI) 0.04054 0

Acid volcanic rocks (VA) 0.020762 0

Basic plutonic rocks (PB) 0.04054 0

Intermediate plutonic rocks (PI) 0.019307 0.007603

Acid plutonic rocks (PA) 0.019307 0.007603

Metamorphics (MT) 0.011918 0.020118

Evaporites (EV) 0 0.151243

Ice and glaciers (IG) 0 0

Water bodies (WB) 0 0

spatially variable P release will rely on future research to parameterize the fate of P released by chemical weathering (e.g. incorporated into Fe-minerals, taken up by ecosystems, adsorbed by clay minerals). This will be important to improve the regional to global scale assessment of how the P-cycle influences ecosystem function, the carbon cycle and the climate system on geological time scales.

There are many shortcomings to the approach used in this study simply because not enough is known about the global weathering system or how to parameterize certain processes at the large scale. Major issues include:

• While field data suggest that for tropical regions the assumed soil shielding effect is appropriate, for other areas the value for soil shielding is not well known. It would be a valuable focus of future research efforts to derive functional relationships of soil shielding as a function of environmental settings like soil structure, soil depth and hydrological flow pathway.

• Apparent activation energies are also poorly constrained, and can significantly influence results — for example if the apparent activation energy was altered by ±20 kJ/mol, this would reduce or increase the fluxes at 20 °C mean temperature by 14% or 25%, respectively.

• It remains difficult to address the influence of ecosystems or land cover/use in empirical weathering flux models (Hartmann et al., 2010; Jansen et al., 2010; Moosdorf et al., 2011b), despite their potential influence having been shown in a series of plot scale to local scale studies (Bormann et al., 1998; Moulton et al., 2000).

• The applied coupling of geochemical and lithological data is based on global averages. Thus a more regional refinement should improve the prediction accuracy, specifically for sediments, as was shown for the Amazon. The contribution of carbonates from lithological classes other than carbonate sedimentary rocks (SC) should be evaluated too, as it is based on the applied coupling.

• There exists a lack in knowledge of how to represent carbonate weathering from carbonate sedimentary rocks (SC) for the varying existing settings (c.f. Szramek et al., 2007). Note, the applied parameters based on the GEM-CO2 model represent settings from French catchments.

• The influence of freeze-thaw on enhanced weathering rates is an emerging topic, and new approaches and specifically field data are needed for a conceptual parameterization if the problem is to be solved with mechanistical terms.

Despite these shortcomings, the global chemical weathering fluxes calculated here are consistent with previously published results derived from independent methods. In some ways this is remarkable given that relatively few parameters have been used in the present model. This raises an important question: Why is such a simple model performing relatively well at the global scale, and also reproducing sensible regional variation in fluxes when compared to observed data? There may be a couple of reasons for this. One is that factors that are not explicitly included in the present analysis, like land cover, hydrological pathways (besides the aggregation term runoff), and seasonality, may be at least partly represented by the parameters that are applied in the model. Another is that the highly heterogeneous spatial and temporal variability at the local to plot scale may average out at the larger spatial scales represented in this analysis, specifically at the global scale. In general, it is important to understand which factors need to be included for a prediction facing a certain time and areal scale, and which can be neglected for large scale approaches to achieve sufficient accuracy and yet still provide sufficiently simple representations to be realistically implemented in Earth system models that extend through the geologic past.

A major obstacle to improve the empirical approaches such as that presented here is the limitation in existing monitoring data. There are few available data covering a higher temporal resolution as well as a

global coverage of sampling locations. Such data would be important for being able to consider including additional processes which might be relevant, like hydrological flowpaths, land use, sediment quality, age of distinguished lithological units or land cover. Recently available data compilations do not provide enough data to parameterize reliably these missed processes at the global scale.

The empirical, parametric approach that we have adopted reflects one of a number of different ways to model chemical weathering fluxes across the Earth's surface. Alternative approaches model the system more mechanistically: for example, based on the percolation of water (top to down), making the soil column very important for the weathering processes (c.f. Godderis et al., 2009), or considering the hydrological pathways and concentration differences in soil-rock compartments conceptually, via the inclusion of relief parameters (c.f. Hartmann et al., 2010). A major challenge for more mechanistic models is that the location of weathering within the soil-saprolite-rock system remains poorly constrained under varying environmental and geomorphic regimes (Anderson et al., 2002; West, 2012). Nonetheless, the most rigorous understanding of weathering systems is likely to derive from exploring the convergence between the kind of empirical analysis presented in this study, and the mechanistic models being developed independently considering the local, regional and global scale. Emphasizing one of these approaches over the other risks missing important information about processes relevant for the global-scale.


Jens Hartmann, Nils Moosdorf and Ronny Lauerwald were supported through the German Science Foundation (DFG-project HA 4472/6-1 and the Cluster of Excellence 'CliSAP', EXC177, Universität Hamburg). A. Joshua West was supported by the US National Science Foundation (EARgrants 1227192 and 1053504). The anonymous reviewers are acknowledged for their constructive criticism of the manuscript.

Appendix 1. Basic parameters and geochemical data applied

The release of cations (Ca + Mg + Na + K) and SiO2 from silicates and CaCO3 from carbonates was estimated using the empirical calibrated b-parameters applied in Eq. (1) and was retrieved from the basic data presented in Hartmann et al. (2010) and Hartmann and Moosdorf (2011). These are shown in Table A1-1. Ca-excess of silicate dominated lithological classes was retrieved from Hartmann and Moosdorf (2011) to account for the carbonate weathering and addressed to the given lith-ological classes. The sum of both given b-parameters makes up for the used b-parameter applied in Eq. (1). For carbonate sedimentary rocks the b-parameter after Amiotte-Suchet and Probst (1993) was used.

The geochemical composition of rocks (Table A1-2) was derived from the data compilation in Hartmann et al. (2012), and used to estimate the proportion of P to released cations and SiO2 from the calculated CWR

Table A1-2

Geochemical composition of lithological classes (based on Hartmann et al., 2012). The composition of VI and PI was calculated based on a mean value from VB and VA as well as PB and PA, respectively.

Sediments, semi- Siliciclastic Mixed Basic and Acid Basic-ultrabasic Acid Metamorphic Carbonate Evaporiti

to unconsolidated sedimentary sedimentary intermediate volcanic plutonics (PB) plutonics rocks (MT) sedimentary (EP)

(SU) rocks (SS) rocks (SM) volcanic rocks rocks (VA) (PA) rocks (SC)

SiO2 52.3 60.32 52.3 51.26 72.7 46.39 67.78 65.91 7.52 15.87

TiO2 0.58 0.67 0.58 1.59 0.28 0.88 0.46 0.6 0.05 0.21

AI2O3 11.37 12.98 11.37 16.02 13.25 9.89 15.25 15.3 1.94 4.47

Fe2Os 2.74 3.04 2.74 3.65 1.48 3.32 1.32 1.57 0.99 1.23

FeO 2.1 2.28 2.1 6.34 1.11 7.13 2.37 2.84 0.99 0.98

Fe Tot as FeO 4.56 5.02 4.56 9.63 2.44 10.12 3.56 4.25 1.87 2.09

MnO 0.07 0.07 0.07 0.18 0.06 0.27 0.07 0.08 0.07 0.04

MgO 2.79 2.51 2.79 5.87 0.39 19.49 1.4 1.93 4.32 2.11

CaO 10.6 4.73 10.6 8.78 1.14 7.34 3.17 3.79 43.86 21.55

Na2O 0.85 0.99 0.85 3.04 3.54 1.45 3.72 3.64 0.08 11.4

K2O 2.62 2.97 2.62 1.23 4.29 0.37 3.17 2.97 0.57 1.18

P2O5 0.12 0.12 0.12 0.31 0.07 0.52 0.16 0.18 0.11 0.06

CO2 8.82 3.9 8.82 0.09 0.08 0.27 0.07 0.07 36.74 10.67

C 0.48 0.52 0.48 0.01 0.02 0.02 0.02 0.02 0.23 0.21

C Tot as C 2.88 1.58 2.88 0.04 0.04 0.09 0.04 0.04 10.25 3.13

S 0.17 0.17 0.17 0.14 0.06 0.02 0.02 0.02 0.11 0.09

SO3 0.69 0.53 0.69 0.08 0.01 0.02 0.01 0.02 1.55 12.77

S Tot as S 0.44 0.39 0.44 0.17 0.06 0.03 0.02 0.03 0.73 5.2

H2O 3.64 4.11 3.64 1.32 1.41 2.55 0.95 0.99 0.84 4.14

Cl 0.07 0.08 0.07 0.06 0.11 0.07 0.05 0.05 0.04 13.02

Ca 7.58 3.38 7.58 6.28 0.81 5.25 2.27 2.71 31.35 15.40

Mg 1.68 1.51 1.68 3.54 0.24 11.75 0.84 1.16 2.61 1.27

Na 0.63 0.73 0.63 2.26 2.63 1.08 2.76 2.70 0.06 8.46

K 2.17 2.47 2.17 1.02 3.56 0.31 2.63 2.47 0.47 0.98

Cat + SiO2 64.36 68.41 64.36 64.35 79.94 64.77 76.28 74.95 42.00 41.98

P content of rock in 0.052 0.052 0.052 0.135 0.031 0.227 0.070 0.079 0.048 0.026

in % P content in % 0.0814 0.0766 0.0814 0.2102 0.0382 0.3504 0.0915 0.1048 0.1143 0.0624

rel. to Cat + SiO2


Appendix 2

In total 39 large catchments were used to evaluate the model and were retrieved from three sampling programs and three references (Martins, 1982; Probst et al., 1992; Tardy et al., 2004; ORE-HYBAM: Cochonneauet a., 2006; CAMREX: Richey et al., 2008; PARTNERS: McClelland et al., 2008). Relevant data are shown in Table A2.

Table A2

Calculated and modeled weathering fluxes shown in Fig. 1 based on major cations and SiO2.

River sampling location

Reference Latitude Longitude Area Runoff Chemical weathering fluxes

Calculated Island arc Including Including soil Including soil

flux runoff-lithology temperature shielding (no shielding and

model (no soil shielding) temperature) temperature

103 km2 mm a1 t km 2 a 1 t km 2 a 1

t km 2 a

Kolyma at Cherskiy Partners 68.7584 161.3029 649 165 2.20 3.50 1.23 2.29 0.81

Lena at Zhigansk Partners 66.8054 123.5447 2206 208 9.94 14.35 8.18 12.93 7.45

Yenisey at Dudinka Partners 69.4104 86.0421 2539 236 13.16 20.96 10.02 17.11 8.26

Ob at Salekhard Partners 66.6065 66.5469 2684 150 12.86 11.68 6.04 8.64 4.71

Mackenzie river at Tsiigehtchic Partners 67.4572 -133.7229 1647 166 12.67 8.96 5.00 8.10 4.55

Yukon at Pilot Station AK Partners 61.9117 - 162.8587 819 221 7.82 5.52 2.47 3.51 1.67

Vargem Grande Camrex -3.3480 - 71.8511 863 166 4.51 4.36 9.52 2.70 4.72

Rio Ica Camrex -3.0391 - 68.2094 120 2593 2.84 8.99 25.06 1.30 3.22

Santo Antonio do Ica Camrex -3.0187 - 67.8938 1145 579 17.83 18.35 50.16 4.76 10.31

Xibeco Camrex -2.6557 - 67.1991 1152 594 18.58 18.81 51.74 5.10 11.49

Rio Jutai Camrex - 2.8393 -66.9312 78 2706 2.09 4.56 15.44 2.42 8.37

Tupe Camrex - 2.4933 - 65.8612 1236 738 24.46 23.76 68.56 7.67 20.36

Rio Jurua Camrex -2.7062 -65.7898 182 1034 4.76 4.26 13.98 2.18 7.06

Rio Japura Camrex - 1.8102 - 65.6859 254 2211 5.00 14.50 41.91 3.95 9.68

Jutica Camrex - 3.6349 - 64.2352 1737 1045 40.02 45.77 136.31 16.09 45.45

Itapeua Camrex -4.0518 -63.0186 1787 1070 41.95 47.78 143.79 17.54 50.82

Anori Camrex - 3.8145 -61.6398 1819 1081 41.95 48.93 148.08 17.85 51.97

Rio Purus Camrex - 3.7433 -61.4313 380 980 6.03 8.20 27.80 5.01 17.16

Manacapuru Camrex -3.3356 - 60.5679 2204 1066 50.64 57.36 176.74 22.93 69.41

Rio Negro Camrex - 3.0686 - 60.3031 714 1752 6.52 27.36 89.18 16.07 53.03

Sao Jose do Amatari Camrex - 3.2402 - 58.9882 2944 1237 60.62 85.63 269.35 39.45 124.15

Rio Madeira Camrex - 3.4439 - 58.8022 1325 951 23.58 32.22 87.17 13.06 32.32

Paura Camrex - 2.3855 - 57.4408 4402 1162 94.04 122.75 374.01 54.49 163.86

Obidos Camrex -1.9355 -55.5190 4702 1196 96.10 135.08 415.22 56.08 169.09

Niger at Lokoja Martins (1982) 7.7893 6.7519 1733 105 4.49 3.73 10.17 2.76 7.54

Niger at Bamako Tardy et al. (2004) 12.6314 - 7.9440 115 443 1.09 1.08 2.70 0.48 1.22

Kongo at Brazzaville Probst et al. (1992) - 4.2984 15.2726 3643 345 20.78 26.65 68.93 8.48 20.66

Rio Maranon at Borja Ore Hybam - 4.4704 - 77.5483 115 63 0.24 0.30 0.34 0.30 0.34

Rio Ucayali at Atalaya Aval Ore Hybam - 10.6782 -73.8179 192 128 1.17 0.78 0.63 0.78 0.63

Rio Napo at Francisco de Ore Hybam - 0.4733 - 76.9825 12 300 0.10 0.10 0.20 0.07 0.13

Orellana (Coca)

Rio Amazonas (Peru) at Ore Hybam - 4.1208 - 70.0357 883 171 5.89 4.60 10.21 2.73 4.79


Rio Solimoes at Tabatinga Ore Hybam - 4.2500 - 69.9333 883 171 6.08 4.61 10.22 2.73 4.79

Rio Purus at Labrea Ore Hybam - 7.2522 - 64.8000 228 723 3.59 3.82 11.85 1.87 5.76

Rio Beni at Rurrenabaque Ore Hybam - 14.4453 - 67.5343 70 424 0.76 1.09 1.75 1.09 1.75

Madeira at Porto Velho Ore Hybam - 8.7367 - 63.9203 981 626 14.23 17.95 43.23 9.28 20.05

Madeira at Fazenda Vista Ore Hybam - 4.8972 - 60.0253 1318 946 23.95 31.93 86.06 12.95 31.91


Rio Tapajos at Itaituba Ore Hybam - 4.2833 - 55.9833 461 902 5.30 9.72 29.44 3.06 9.17

Rio Maroni at Langa Tabiki Ore Hybam 4.9862 - 54.4368 63 897 0.71 1.23 3.33 0.12 0.33

Rio Oyapock at Saut Maripa Ore Hybam 3.8017 - 51.8847 24 1416 0.37 0.75 2.17 0.07 0.22


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