Enhanced weathering strategies for stabilizing climate and ... · Tropical areas are weathering...

L.L. Taylor et al. SI-i

1Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK 2Earth Institute, Columbia University, 475 Riverside Drive, New York, 10027, USA 3Goddard Institute for Space Studies, NASA, 2880 Broadway, New York, 10025, USA 4Department of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK 5Department of Earth Sciences, University of California, Riverside CA, 92521, USA 6Department of Maths, University of Sheffield, Sheffield S10 2TN, UK 7Kroto Research Institute, University of Sheffield, Sheffield S3 7HQ, UK *Email [email protected]

Enhanced weathering strategies for stabilizing climate and averting ocean acidification

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2882

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

http://dx.doi.org/10.1038/nclimate2882

L.L. Taylor et al. SI-ii

Contents: Page

Additional discussion…………………………………………………………………………. SI-1

Supplementary Figures

Fig. S1 Modelling framework……………………………………………………………… SI-4

Fig. S2 Effect of mixing depth on CO2 consumption through enhanced weathering in the

Amazon basin……………………………………………………………………… S1-5

Fig. S3 Identifying tropical weathering hotspots…………………………………………… S1-6

Fig. S4 Relationship between modelled soil respiration and pCO2 of soil solutions……….. SI-7

Fig. S5 Relationship between modelled soil respiration rates and soil pH…………………. SI-8

Fig. S6 Validation of the modelling approach at both global and catchment scales………... SI-9

Fig. S7 Mineral saturation state responses – no addition of crushed silicates……………… SI-10

Fig. S8 Mineral saturation state responses – added silicates at 10cm mixing depth……….. SI-11

Fig. S9 Mineral saturation state responses – added silicates at 30cm mixing depth……….. SI-13

Fig. S10 Examples of modelled weathering kinetics………………………………………... SI-14

Fig. S11 RCP scenarios and outputs from the five ensemble GCMs………………………... SI-16

Fig. S12 Responses of outputs from the Sheffield Dynamic Global Vegetation Model to

RCP scenarios……………………………………………………………………… SI-17

Fig. S13 Annual global CO2 consumption by weathering without addition of silicate rocks.. SI-18

Fig. S14 Annual global CO2 consumption by weathering with addition of silicate rocks…... SI-19

Fig. S15 Effect of rock applications on 21st century atmospheric CO2 consumption and

runoff pH in the Amazon and Congo basins……………………………………….. SI-20

Fig. S16 Modelled runoff pH for tropical weathering hotspots over the 21st century under

different enhanced weathering scenarios…………………………………………... SI-21

Fig. S17 Mean global air temperature over the 21st century under different RCP and

enhanced weathering scenarios…………………………………………………….. SI-22

Fig. S18 Effect of enhanced weathering on ocean pH by year 2100………………………... SI-23

Supplementary Tables

Table S1 CMIP5 climate models…………………………………………………………… SI-25

Table S2 Weathering reaction stoichiometries……………………………………………… SI-25

Table S3 Rate laws for mineral dissolution reactions………………………………………. SI-26

Table S4 Weight fractions of minerals in geoengineering rocks…………………………… SI-26

Table S5 Modelled run-off and observed pH of river waters draining tropical basins……... SI-27

Table S6 Catchment runoff and CO2 consumption through enhanced weathering as

modelled for the Amazon and Congo basins…………………………………….. SI-27

Table S7 Max. annual dunite mining capacity and estimated global dunite resources……... SI-28

Table S8 Major known resources of harzburgite…………………………………………… SI-30

Table S9 Potential available basalt resources from LIPs.…………...…………………….. SI-30

Table S10 Combined capital and operational costs associated with mining, grinding,

transport and spreading of material from the air…………………………………. SI-31

Table S11 Monetary expenditure for initial consumption of 50 ppm atmospheric CO2…….. SI-31

Additional references…………………………………………………………………………... SI-32

NB. Reference numbers are independent of those in the main paper

L.L. Taylor et al. SI-1

Additional discussion

Mixing depth and particle size

We assume that the rock grains applied would be mixed into the soil by water, bioturbation or both. This

assumption is supported by several field studies. Fishkis et al.1 applied fluorescent-labelled silt-sized

phytoliths to the surface of in situ loamy sand (cambisol) and silty loam (luvisol) soils in Germany and

reported mean transport rates of 4 cm yr-1. At the end of the year-long study, they observed labelled

phytoliths at the maximum sampling depth of 50 cm in the luvisol. The authors suggested that both water

percolation and bioturbation were responsible for transport, and identified the need to correlate phytolith

transport with macropore networks. Phytoliths were observed to 40 cm depth in the earthworm-free

cambisol. Active earthworm populations homogenised the uppermost 20 cm of several well-drained Scottish

mineral soils within 30 years of 137Cs deposition, with the depth of mixing and faunal activity controlled by

water table fluctuations2. In the tropics, biotic mixing rates can be over an order of magnitude higher than

those of temperate locations3.

Soil structural features also play a key role in regulating mixing. Old tree root channels are known to

be important for water infiltration in acid tropical soils under high rainfall4. Although the main tree root

systems are primarily found in the upper 40 cm, roots up to 4 cm in diameter are found down to 100 cm, and

smaller fibrous roots even deeper in Nigeria and Indonesia. In the humid tropics, ultisols in particular may

have natural soil pipes which often drain the bottom of the soil 1.3–1.5 m deep, but vertical cicada burrows

extending 60 cm deep have been observed in Malaysia5. Earthworms, however, are the most important

bioturbators in the tropics3. In an Ivory Coast forest, earthworm burrows have been observed down to 60–90

cm depth, but most earthworms were found in the uppermost 20 cm (Ref. 6).

This evidence supports our assumption that applied 10-µm silicate grains would be transported to at

least 30 cm deep, and that as the century progressed, further additions of pulverized silicates would be found

deeper in the soil. Given typical depths for the majority of roots and earthworms in the humid tropics, a 30

cm mixing depth is taken as a conservative estimate, but we have also included simulations with a 10 cm

mixing depth in our analyses. Detailed modelling of bioturbation and advective particle transport are beyond

the scope of this work, and we specify mixing depths based on observations of macropores in tropical soils.

Defining macro-engineering ‘hotspot’ weathering regions

Tropical areas are weathering hotspots because high forest primary production and warm, wet climates

promote mineral dissolution. For each macro-engineering CDR scenario, the tropical grid-points for the

median of the general circulation models (GCMs) were sorted in order of decreasing cumulative CO2

consumption and binned to give percentages of total flux, allowing creation of Fig. S3a. This sorting

identifies ‘hotspots’ corresponding to a given maximum land area. These hotspots do not necessarily reflect

conditions for any given year, but rather for the century. Fig. S3b displays the tropical fluxes for the 5 kg


harzburgite m-2 yr-1 scenario. Grid-points other than tropical hotspots are masked out in Fig. S3c, which

indicates that many of the "hottest" spots are found in Southeast Asia. Some of these hotspots coincide with,

or are near to, tropical peat swamp forests in coastal Sumatra, Malaysia and Borneo, which are also global

biodiversity hotspots currently under pressure from logging, fire and land use change7. The macro-

engineering scenarios described here have the potential to exacerbate land use change and to change the

ecology of treated areas.

Ecological effects of pulverized silicate rock applications

Increases in soil pH with basalt additions are not as dramatic as observed for lime applications at equivalent

rates8. Gillman et al.8 applied up to 50 Mg ha-1 (5 kg m-2) of basalt powder to seven typical Queensland soils

representing the major landforms and parent materials of tropical coastal Queensland. Their particle size

distribution (90% <150 microns, 25% <15 microns in diameter) and application rates are comparable with

our scenarios. After three months, soil pH increased by at least half a pH unit for most soils under

application rates higher than 25 Mg ha-1 (2.5 kg m-2), but soil calcium and magnesium increased at the

expense of exchangeable potassium8. Higher applications of basalt grains up to 180 Mg ha-1 (18 kg m-2)

have increased crop yields in the tropics9, but any implementation of our application scenarios should be

exercised with caution. Recommended liming rates on Oxisols in the tropics are 4–6 Mg ha-1 about every

three years (0.1–0.2 kg m-2 yr-1) for most annual crops10, comparable with lime application in the United

States of 5 Mg ha-1 every five years (0.1 kg m-2 yr-1)11. These liming rates are lower and less frequent than

the application scenarios we describe, but limestone weathers several orders of magnitude faster than

silicates.

Application of ground basalt can significantly improve the iron nutrition of crops (e.g., peanuts) by

providing fresh exposed surfaces of iron-bearing minerals from which plant roots and mycorrhizal fungi are

able to mobilize iron12. Over-liming on tropical soils can raise the concentrations of metals toxic to plants,

including aluminium and toxic heavy metals such as manganese. Micronutrient deficiencies, including iron,

are well-documented for over-limed croplands10. In agricultural plants, iron deficiency is relatively common

in calcareous soils (termed “lime-induced chlorosis”) but can be compensated with the addition of basalt

powders12. Provision of basalt grains can also stimulate proliferation of arbuscular mycorrhizal (AM) fungi

whose hyphae physically and chemically degrade constituent minerals for elemental uptake in return for

carbon supplies from tree roots13. AM fungi are ubiquitous symbionts of the roots of most tropical forest tree

lineages and are highly evolved for acquiring phosphorus from soils.

In freshwater lakes, ponds and streams subjected to liming in Scandinavia, pH shock to resident

organisms occurs above pH 9, organic matter decomposers in lake sediments shift from fungi to faster-acting

bacteria, and dissolved organic carbon (DOC) and phosphorus are precipitated14. Liming can reduce the

DOC in freshwater and the loss of acidophilous species in Scandinavian wetlands15, with implications for the

highly biodiverse tropical forest peatlands mentioned above7. Because harzburgite produces the highest

mean annual runoff pH in the scenarios considered here (Table S3), its use could result in drastic ecological


community shifts. Its mineralogy, which often includes asbestiform serpentine, is another reason to caution

against the widespread distribution of fine harzburgite grains.

Serpentine

Serpentine refers to alteration products of olivine and sometimes orthopyroxene, formed by hydration before

or during emplacement of ultramafic rocks. In the case of harzburgite, lizardite forms first from fresh

olivine, whereas the asbestiform mineral chrysotile is associated with extensive alteration, appearing after

fresh olivine is depleted16. Chrysotile comprises 90–95% of asbestos mined worldwide17. Harzburgites of

the Samail ophiolite in Oman are 50–60% serpentinised18,19 with chrysotile in microcracks19. Exposure to

chrysotile dust is associated with disease in humans20-22 and even living in proximity to ophiolites can

increase the risk of asbestos-related disease by almost 10% (Ref. 23).

Dust mitigation

Any type of dust (particles <75 microns in diameter), may adversely affect human health. Grinding results in

a log-normal particle size distribution20 which is likely to include particles <10 µm in diameter capable of

entering the human thorax, and particles <4 µm in diameter which could enter the lungs24. Mafic rocks such

as basalt have a lower silica content than other silicate rocks and are less likely to cause silicosis, but they are

not necessarily safer. Fe2+ of pyroxenes and amphiboles in volcanic ash is thought to provoke a similar toxic

reaction in the lungs24,25, although Fe2+ oxidises to Fe3+ during weathering.

Mitigation strategies would be required not only during drilling, crushing, grinding and transport26 of

pulverized silicate rocks during, and probably after, spreading. Given the large land areas to be covered in

the scenarios considered here, dry spreading is likely to be contraindicated. Dust mitigation strategies often

include the use of water20,26, suggesting the possibility of aerial spraying of liquid particle suspensions. The

behaviour of such suspensions sprayed by aircraft, including the extent of trapping by forest canopies or by

grasses and forbs, could be tested in the field and predicted by modifying dispersion models developed for

pesticide applications27. Further dispersion or computational fluid dynamics modelling20 and field trials

would then be necessary to predict the fate of particles which may subsequently dry and become re-

suspended by wind. Trees and shrubs are efficient dust filters at roadsides28 and on agricultural land subject

to windblown volcanic ash29. However, dust has variable effects on vegetation depending on plant species,

dust particle size and dust chemistry. These include leaf damage, stomatal occlusion and increased water

loss, as well as shifts in community composition28.

Fine silicate dust is already produced as a by-product of crushing rock for aggregate, amounting to an

estimated 3.3 Pg yr-1 globally30 and is presenting a storage and management problem for the mining

industry31. Manning and Renforth32 suggest that adding industrial waste including calcium silicate fines to

urban brownfield site soil could lead to considerable storage of carbon via carbonate mineral precipitation.

Basaltic quarry fines have promise as agricultural fertilisers33 as well as for remineralisation of depleted soils

as discussed in the section on ecological effects above31,34.


Δ

Ca0.9Mg0.9Na0.1Al0.4Fe0.2Si1.9Ti0.1O6

[(Ca,Na)(Mg,Fe2+,Al,Ti)(Al,Si)2O6](s)+ 4H+ + 2H2O(l)

0.9Ca2+ + 0.9Mg2+ + 0.1Na+ + 0.4Al3+ + 0.2Fe + 1.9H4SiO4(aq)

MgSiO3 (s) + 2H+ + H2O(l) Mg2+ + H4SiO4(aq)

Fe2SiO4(s) + 4H+ + 4 H2O(l) → 2Fe + H4SiO4(aq)

Mg2SiO4(s) + 4H+ + 4 H2O(l) 2Mg2+ + H4SiO4(aq)

KAlSi3O8(s) + 4H+ + 4 H2O(l) K+ + Al3+ + 3H4SiO4(aq)

Ca0.6Na0.4Al1.6Si2.4O8(s) + 6.4H+ + 1.6H2O(l)

0.6Ca2+ + 0.4Na+ + 1.6Al3+ + 2.4H4SiO4(aq)

Mg3Si2O5(OH)4(s) + 6H+ H2O(l) + 3Mg2+ + 2H4SiO4(aq)

Mg5Al2Si3O10(OH)8(s) + 16H+

6H2O(l) + 5Mg2+ + 2Al3+ + 3H4SiO4(aq)

SiO2(s) + 2H2O(l) H4SiO4(aq)



Al(OH)3(s) + 3H+ Al3+ + 3H2O(l)

Al2Si2O5(OH)4(s) + 6H+ H2O + 2Al3+ + 2H4SiO4(aq)

CaCO3(s) Ca2+ + CO32-

CaMg(CO3)2(s) + Ca2+ + Mg2+ + 2CO32-

CaSO4 (2H2O)(s) Ca2+ + SO42- +2H2O(l)

FeTiO3(s) + 2H+ + H2O(l) → Fe2+ + Ti(OH)4(aq)


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