1 Estimating turnover using radiocarbon data:

2 European Russia case-study

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4

5

6

7 Victor Brovkin1,*, Alexander Cherkinsky2, Sergey Goryachkin3

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11 1Potsdam Institute for Impact Research, P.O.Box 601203, 14412 Potsdam, Germany

12 2Center for Applied Isotope Studies, University of Georgia, 120 Riverbend Rd., Athens, GA

13 30602, USA

14 3Institute of Geography, Russian Academy of Sciences, Staromonetny, 29 Moscow 109017

15 Russia

16

17 *Corresponding author, tel. +49 331 2882592, fax +49 331 2882620, e-mail:

18 [email protected]

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20

21

1 1

2 Abstract

3

4 Turnover rates of for 20 soil types typical for 3.7 million km2 area of European

5 Russia were estimated based on 14C data. The rates are corrected for bomb radiocarbon

6 which strongly affects the 14C balance. The approach is applied for carbon stored in

7 organic and mineral layers of the upper 1 m of soil profile. Turnover rates of carbon in the

8 upper 20 cm are relatively high for (0.16-0.78% yr-1), intermediate for tundra

9 soils (0.25% yr-1), and low for soils (0.02-0.08% yr-1) with exception for southern

10 (0.32% yr-1). In the soil layer at 20-100 cm depth the turnover rates were much

11 lower for all soil types (0.01-0.06% yr-1) except for bog soils of southern taiga (0.14%

12 yr-1). Combined with a map of distribution and a dataset of several hundred soil

13 carbon profiles, the method provides annual fluxes for slowest components of soil carbon

14 assuming that the later is in equilibrium with climate and cover. Estimated carbon

15 flux from the soil is highest for forest soils (12 to 147 gC/(m2⋅yr)), intermediate for tundra

16 soils (33 gC/(m2⋅yr)), and lowest for grassland soils (1-26 gC/(m2⋅yr)). The approach does

17 not distinguish active and recalcitrant carbon fractions and this explains low turnover rates

18 in the top layer. Since changes in soil types will follow changes in climate and ,

19 we suggest that is an important factor influencing future dynamics of soil

20 carbon fluxes. Up to now, an effect of soil type changes, as well a clear evidence from 14C

21 measurements that most of soil organic carbon has millennial time scale are basically st 22 neglected in the global models used for projections of atmospheric CO2 in 21 23 century and beyond.

24

2 1

2 Introduction

3

4 Soil carbon is the main component of terrestrial carbon cycle. Estimates of storages of soil

5 organic matter (SOM) in the upper 1 meter layer vary in the range of 1,500 to 2,000 GtC

6 (Post et al., 1982, 1997; Batjes et al., 1996, Prentice et al., 2001) depending on a way to

7 account for organic carbon storage in wetlands. Soil layers deeper than 1 meter contain

8 several hundred PgC in form of peat in northern (e.g. Gorham, 1991) and

9 organic carbon in moist tropical forest soils (Trumbore et al., 1995). Additionally, about

10 950 PgC are stored in inorganic (carbonate) form, predominantly in drylands (Lal, 2004).

11 Ample storages of soil carbon outweigh by a factor of three to six the plant

12 estimated in the range of 470 to 660 PgC (Prentice et al., 2001). In case of continued 13 emissions, global mean air temperature is projected to increase up to 6.4°C

14 during the 21st century (IPCC-2007, SPM). Consequent drastic changes in plant

15 productivity, soil thermal and hydrological balance will strongly affect terrestrial carbon

16 storage and, through the land- CO2 exchange, the atmospheric CO2

17 concentration. The feedback loop between CO2 and climate most likely has amplified 18 in the past (Scheffer et al., 2006, Torn and Harte, 2006) and could

19 substantially increase global warming in the future (Cox et al., 2000, Kirschbaum, 2000).

20

21 Global coupled climate – carbon cycle models are the best tools currently available for

22 assessment of changes in global carbon balance in the future. Within the Coupled Climate–

23 Carbon Cycle Model Intercomparison Project (C4MIP), eleven coupled climate–carbon

24 cycle simulations of different complexity performed simulations over the twenty-first

25 century (Friedlingstein et al., 2006). All but one model simulated a reduction of SOM

26 turnover time, and some models show a strong negative impact of climate change on

27 turnover time (up to 1 yr decrease per 1°C global warming). This assessment is very

28 preliminary because the SOM balance is one of the most crudely represented processes in

29 the global carbon models. One of the biggest uncertainties is a decomposition of the inert

30 (stable or recalcitrant) organic carbon. It is likely that biological processes consume the

31 recalcitrant SOM as well but little is currently known about these processes (Prentice et al.,

3 1 2001). An effect of soil temperature change on SOM decomposition rate is doubtless, but its

2 magnitude in long-term dynamics is currently a matter of debate (Knorr et al., 2005,

3 Reichstein et al., 2005, Fang et al., 2006).

4

5 Here, we focus on time scales of the SOM decomposition based on radiocarbon

6 measurements using the soil formation model formulated by Cherkinsky and Brovkin

7 (1993). Similar type of model has been applied by Trumbore (1995), Perruchoud (1996) and

8 Gaudinsky et al. (2000) for evaluation of soil carbon cycling in tropical and temperate

9 . Hahn and Buchmann (2004) accounted for two pools (active and passive) based on

10 prescribing a threshold in 14C activity for active carbon in the soil. This method is more

11 advanced than the bulk carbon models mentioned before, but it does require a specification

12 of organic input by aboveground and belowground litter. Since these data were not available

13 for us, we applied hereafter the model by Cherkinsky and Brovkin (1993) of unfractionated

14 SOM and combine it with database on soil carbon storage in different soil types of European

15 Russia.

16 17 18 Methods

19

20 Radiocarbon (14 C) analysis 21

22 The method is based on the fact that due to β-decay the specific carbon activity = 23 I(t) C14 (t) C12 24 of organic matter remaining after the ’s death obeys the exponential decay: −λ 25 It()= Ae t , (1)

26 where λ is the decay-rate of 14 C (14 C half-time is 5730 years), A is the specific activity of

27 atmospheric carbon and Ct12 (), C14 (t) are the contents of carbon isotopes in organic matter

28 (the content of stable 12 C isotope does not change with time).

29 In accordance with equation (1) −1 I(t) 30 T = ln (2) λ A

4 1 is 14 C age of analyzed organic matter.

2

3 Equation (2) is widely used to estimate the period of time since the organism’s death. This

4 equation implicitly assume a stable 14 C concentration in the atmosphere, which is

5 approximately true at least for the last several thousand years. Equations (1) and (2) refer to

6 a "closed carbon system", which presumes no carbon exchange with the environment. In

7 contrast, the soil carbon represents an open system. The 14 C age calculated from equation

8 (2) can thus not be interpreted as absolute age in the context of SOM (Scharpenseel, 1971)

9 but has the meaning of a mean residence time of soil organic carbon. Its reciprocal is a

10 turnover rate of soil carbon, m:

11 m = 1 T . (3)

12

13 is a heterogeneous system and its turnover rate depends on the fraction

14 of soil carbon, depth of soil sample, soil type and many other factors (Schimel et al., 1994).

15

16 Model of soil organic profile formation

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18 The model of a monogenetic soil organic profile formation under stable conditions of

19 pedogenesis is based on the following assumptions:

20 • the soil organic profile develops from the surface downward as a result of the increased

21 involvement of rocks in -forming processes;

22 • the underlying layers are formed later than the overlying ones;

23 • rates of organic input and of humus turnover are constant.

24 Under these assumptions, carbon accumulation in the soil can be represented by dC() t 12 =−−pAmCt()1 ()  dt 12 25  , (4) dC() t  14 =−pA mC() t −λ C () t  dt 14 14

26 where p(1− A ) and p A define the amounts of input of 12 C and 14 C respectively, and

27 C12 (t),C14 (t) are the contents of carbon isotopes in soil. Assuming as a first approximation 28 constant inflow and turnover of carbon, we obtain from (4):

5  pA()1− Ct()= ()1− e−mt  12 m 1  , (5) pA Ct()= (1− e−+()mtλ )  14 m + λ 2 resulting in a specific activity m A − −mt Ct14 () 1 e 3 It()== . (6) +−λ − −+()mtλ Ct12 () (mA )(1 )1 e − 4 Since A ≈ 10 12 , we can replace 1-A with 1 in the equations (5-6).

5 For equilibrium conditions we find from (5): p p A m A 6 C **==,,C I *= , (7) 12m 14 m + λ m + λ 7 where I * is the specific carbon activity for equilibrium case, and A is the specific carbon

8 activity for the input flux of plant detritus.

9 From (7) we have: I * 10 m = λ , (8) AI− *

11 where I * is the specific humus activity, measured by radiocarbon analysis of soil samples.

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13 Case of recent soils

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15 The above model (Eqs. 4-8) is appropriate in case of constant concentration of 14C in the

16 atmosphere. However, the concentration of atmospheric radiocarbon in the past has been

17 subject of fluctuations caused by explosions of supernova stars, variations in solar activity,

18 and oscillations of the geomagnetic field of Earth. These fluctuations are known for the last

19 8 thousand years with quite good precision and during the last decade the calibration curve

20 was extended back to about 40 thousand radiocarbon years before present (Reimer et al.,

21 2004; van der Plicht et al., 2004).

22

23 The concentration of radiocarbon has been also significantly influenced by human activities.

24 Since the industrial period is characterized by the ever increasing use of fossil fuel with no

25 14 C, there has been a slight diminishing of its concentration in the atmosphere by about 1-

26 2% (Suess effect), superimposed by a rapid increase due to nuclear tests in the atmosphere

6 1 started in the early 1950’s. A maximum in the test intensity in 1962-64 resulted in nearly

2 two-fold concentration of 14 C in the atmosphere as compared to the pre-nuclear period.

3 Nuclear power plants also contribute significantly to the increase of the atmospheric

4 radiocarbon concentration. Fig. 1 displays the change of the specific activity of carbon in the

5 atmosphere after 1955 (Bolin, 1986, Levin and Kromer, 1997). This factor resulted in a

6 significantly higher specific radioactivity of the plant organic tissues over the last five

7 decades in comparison with the NBS-standard that provides a standard reference for

8 radiocarbon dating (NBS SRM 4990). This surplus is little dependent on the type of

9 vegetation and ranges within 107 to 110% at present. With a certain time lag the increase of

10 radioactivity takes place in the newly-formed humus, too. This effect is mostly pronounced

11 in humus of soils with fast turnover, especially .

12

13 The fluctuations of 14 C concentration in the atmosphere make it impossible to calculate the

14 radiocarbon age and rates of humus turnover particularly in soils with rapid turnover using

15 equations (2, 8). The parameter A in these equations ceases to be constant in the time

16 interval 1956-1982, and instead, two stationary levels of specific activity are found: A ′ -

17 "pre-nuclear" activity (100%), and A ′′ - atmospheric activity after 1982 (130-135%). If the

18 measured level of specific activity in a soil is, for example, 110%, how can it be interpreted?

19 Calculation of a radiocarbon date with equation (2) if A equals A ′ is senseless, because it

20 yields a negative estimate of mean residence time. With A = A′′ the assessment gives a date

21 of about 1.5 thousand years in contradiction with other radiocarbon dates for the soil.

22 Besides, specific activity of soil is not constant, but gradually increases with time in

23 disagreement to equation (8).

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25 To overcome these difficulties with dating of recent soils, Cherkinsky and Brovkin (1993)

26 suggested a model for calculating the radiocarbon age of soils and the turnover rates of

27 humus that take into account the whole curve of change of specific activity of carbon in the

28 atmosphere after 1956. An equilibration of processes of humification and mineralization is

29 assumed, i.e. input/output fluxes of 12 C do no change. In this case, dynamics of I(t) is

30 determined only by changes in A(t):

7 dI(t) 1 dC (t) m ==−+=−+14 []λλ 1 * pAtmCtmAtmIt() ( )14 () () ( ) (). (9) dt C 12 dt p

2

14 3 Let us assume that by the time t0 (e.g. 1955 year B.P.) the amount of C in soil has = * 4 stabilized, then It()0 I in accordance with equation (7). Thus, if the measured specific ≥ 5 activity of the sample at time t1 t0 is I 1 , it is possible to choose the m that agrees the 6 condition: = 7 I(t1) I1. > ∀ > 8 It may be shown, that if for the atmospheric activity the inequality A(t) A(t0) t t0 holds,

9 then the equation (9) has a single solution: each I(t0) corresponds to a single value of m. 10

11 Let's give several examples illustrating this technique. If the specific activity of soil with fast

12 turnover of the organic material at a rate of 2.2% per year were 99.8% before 1950, then I(t)

13 would increase to more than 100% within several years prohibiting further calculation with

14 equation (2). In a soil with low rate of humus turnover (m = 0.1% per year), the specific

15 activity changes much slower. In this case, I(t) slightly increases from 89.0% to 90.2%

16 during 1950 to 1985, and reaches a value of 90.7% in the year 2000. Because of the slow

17 change of I(t), the equation (2) can still be used for some decades, though it will produce

18 too high rate. Thus, the error of m estimate grows from 15% in 1985 to 20% in 2000. This

19 stresses a need for time-specific adjustment of Eqs. (2) and (8). 20 21 European Russia case study

22

23 Methods and materials

24

25 A by Rozov and Rudneva (1986) with scale 1:16 000 000 provided the spatial

26 database of our study. In order to make this map useful for our objectives we had to refine it

27 and to apply several changes to the map legend:

28 • podbur soils (Entic ) at the Kola Peninsula and Polar Ural were separated;

29 • areas of podzolic soils (Albeluvisols) were subdivided geographically into northern and

30 southern ones due to essential differences in storage and turnover rate of soil carbon;

8 1 • northern in tundra and northern taiga zones were considered separately from

2 those of the more southern areas.

3

4 Some regions were excluded from our analysis: there were no available radiocarbon data for

5 northern islands (e.g. Novaya Zemlya), Kaliningrad region, Azov wetland and rock regions

6 of Northern Caucasus with a high heterogeneity of soil types. The soil map was digitized

7 with the ARC/INFO software, areas of more than two hundred polygons were calculated. 2 8 Total area of analyzed soil cover is 3.7 ml. km .

9

10 For convenience in data processing and because of significant differences in carbon storage

11 and turnover rates, these characteristics were calculated separately for horizons in detritus

12 (litter, peat) and mineral soil (humus). We used published data (Afanasieva, 1966, Fridland

13 and Lebedeva, 1974, Ignatenko, 1979, Nogina and Rode, 1977-1981, Sklyarov and Sharova,

14 1970) as well as our experimental data for estimation of spatial distribution of carbon

15 storage, resulting in a database which includes the volume percentage of carbon for about

16 600 soil profiles. For evaluation of carbon storage we used regression equation suggested by

17 Post et al. (1982):

18 = + + 19 Bdfb01b d b 2lnC , ⋅ -2 20 where Bd is the carbon storage within one layer (gC m ), Cf is the volume percentage of

21 carbon (%) in the considered soil layer, d is its depth (m), and b0 , b1 , b2 are the regression 22 factors depending on the type of soil and the depth of the layer within the profile.

23

24 The information in the database relates to different layers of soil profiles. For the estimation

25 of the average carbon content within a soil profile the approximated average value of carbon

26 storage for every 10-centimeter layer was calculated. This procedure was carried out for all

27 types of soils, and carbon content was determined for both mineral and detritus horizons.

28 The carbon profiles for three different types of mineral soil horizons are shown in Fig. 2.

29 One can see there the sharp decrease of humus storage with depth in podzolic soil

30 (Albeluvisols) (a), and smooth decrease in Chernozems (c). The most fertile type of soil,

31 , stores much more humus than the other soils.

9 1

2 Carbon in litter of tundra and forest soils is accumulated in the upper 10 cm. In the bog-

3 podzolic soils (Gleyic Albeluvisols), accumulation of litter and peat is usually limited to 30

4 cm depth. For Histosols we accounted for one meter layer of peat as owing to the

5 radiocarbon data that is the lower limit of biologic activity of carbon in this soil type. In the

6 soils of forest-steppe and steppe regions, a carbon storage in litter was neglected because of

7 its small magnitude and because a considerable part of arable lands in this zone has no litter

8 at all. The comparison of carbon storage for the different soil types is presented in Fig. 3.

9

10 For an evaluation of the annual carbon exchange in the upper soil horizons of ETR we used

11 the model (4-8) and corrected for radiocarbon activity in layers of recent soils in accordance

12 with equation (9). The results of 14 C-dating, which had been fulfilled in the Radiocarbon

13 Laboratory of the Institute of Geography of Russian Academy of Sciences (Margolina et al.,

14 1988, Chichagova and Cherkinsky, 1993, Cherkinsky and Goryachkin, 1993), were used as a

15 data basis of our analysis. Owing to an absence of uniform thickness of soil samples, we had

16 to use the approximation processing of data for generating a homogeneous database. The

17 dependence of turnover rate m on soil depth h was approximated by one of the following

18 types of function:

+ 19 • exponential, m(h) = ea bh ,

20 • power, m(,h) = a ⋅hb

21 where parameters a and b were validated based on the experimental data. The function with

22 the highest level of authenticity was chosen for every soil type. For example, the more

23 adequate function for sod-podzolic soils (Eutric Albeluvisols) was a power function (Fig. 4),

24 which is more suitable as a description of the sharp decrease of turnover rate in depth. On

25 the other hand, we used an exponential model for Chernozem because of smoother decline of

26 turnover rate with depth (Fig. 4).

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28 Results

29

30 The spatial distribution of soil carbon storage is presented in Fig. 5,a. All values were

31 accounted for the upper one meter of soil surface. In some bogs the peat storage extends on

10 1 the depth to 12 meters. Nevertheless, we did not take into account the carbon lies deeper

2 than 1 meter, as it is practically not involved into decomposition process. The hydromorphic

3 soils - Histosols, Gleyic Albeluvisols – store significant amounts of peat in the organic -2 4 horizon. The highest storage of about 60 kgC⋅m corresponds to Histosols; more than 90 %

5 of this carbon is a peat. In terms of a geographical comparison from north to south, the -2 6 storage is about 20 kgC⋅m in the northern areas of podbur (Entic Podzols) and tundra-gley -2 7 (Turbic Cryosol) soils. It decreases to 10 kgC⋅m for podzolic (Dystric Albeluvisols) and

8 sod-podzolic forest soils (Eutric Albeluvisols). Grassland fertile soils, Chernozems, are -2 9 characterized by much higher humus storages from 20 to 35 kgC⋅m (Table 1). 50-60% of

10 Chernozems area is used as an arable land, and 10-15% of Chernozems are used as

11 and pastures. Agricultural lands have lower carbon content in comparison with soils left

12 undisturbed in protected natural forest and grassland areas. The storage of carbon in natural -2 13 chernozem soils in upper 1 meter layer could be up to 40-50 kg⋅m (Afanasieva, 1966).

14 Chestnut and semidesert soils ( and ) in the south-eastern region of -2 15 Russia have a relatively low soil carbon content of 3 to 10 kgC⋅m . In total, the analyzed

16 territory of about 3.7 million km2 contains 55 Pg of carbon in the upper 1 meter of soil.

17

18 Turnover rates of carbon in the upper 20 cm are relatively high for forest soils (0.16-0.78%

19 yr-1), intermediate for tundra soils (0.25% yr-1), and low for grassland soils (0.02-0.08% yr-1)

20 with exception for southern Chernozems (0.32% yr-1), see Fig. 5,b. In the soil layer at 20-

21 100 cm depth the turnover rates were much lower for all soil types (0.01-0.06% yr-1) except

22 for peat bog soils of southern taiga (0.14% yr-1), see Table 1 and Fig. 5,c. Estimated carbon

23 flux from the soil is highest for forest soils (12 to 147 gC/(m2⋅yr)), intermediate for tundra

24 soils (33 gC/(m2⋅yr)), and lowest for grassland soils (1-26 gC/(m2⋅yr)), see Fig. 5,d.

25 Calculated for the whole area, the annual soil carbon decomposition flux is 77 TgC/yr. Let

26 us caution that our approach does not distinguish active and recalcitrant carbon fractions and

27 this explains low turnover rates in the top layer and low fluxes of carbon from the soil.

28

29 Discussion

30

11

1 Our approach is based on direct measurements of 14 C intensity of soil samples. For

2 interpretation of results of measurements, the model of processes of 14 C accumulation and

3 decomposition in soil (Cherkinsky and Brovkin, 1993) has been applied. The main model

4 assumption is that 12 C in the soil is in equilibrium state, or by another words, soils had been

5 in final stage of their evolution. An assumption of steady-state carbon cycle before pre-

6 industrial era is routinely used in models of global carbon cycle (Cramer et al., 2001).

7

8 It is important to compare different methods for estimation of carbon turnover rate. For

9 example, Bazilevich and Shmakina (1986) considered carbon balance in grass . In

10 accordance with their data, annual net in steppe zone of Central Russia is -2 -1 -2 -1 11 600-900 gC⋅m yr in agroecosystems and 1200 gC⋅m yr in natural . In the -2 -1 12 former ecosystem, the biomass is allocated into agricultural production (400-500 gC⋅m yr ) -2 -1 -2 -1 13 and plant residues (400 gC⋅m yr ), while in grassland 400 gC⋅m yr are allocated to above- -2 -1 14 ground litter and 800 gC⋅m yr - to the below-ground root mortmass. Average time of

15 decomposition of above-ground litter is one year. About 20% of residues form the fast pools

16 of litter and mortmass have lifetime of 2-10 years. Small part of them is transformed into

17 humus: in accordance with Gilmanov (1978), humification rate of plant residues is 2-6% or -2 -1 18 20 - 70 gC⋅m yr . Annual exchange rate of humus carbon calculated by radiocarbon data -2 -1 19 for Chernic Chernozems soils in this area is only 13 gC⋅m yr , or 2-4 times lower. This

20 discrepancy could be explained by two reasons:

21 • carbon humification and mineralization rates are not measured directly. Therefore, the -2 -1 22 expert guess in gC⋅m yr could exceed the real humification rate;

23 • in our study, mostly stable organic carbon part with very low turnover rate has been

24 analyzed.

25

26 This example suggests that only a minor part of annual carbon input flux to grass ecosystem

27 is involved into slow exchange processes in soils. The major part of carbon rotates in fast

28 active pools. Their fraction in SOM storage in 1 meter layer could be as low as 10-20%.

29 Moreover, the most part of Chernozem soils is used for agricultural purposes and negative

30 balance of soil organic matter is typical for this region. In forest ecosystems, a carbon

31 storage in living phytomass and mortmass has decadal lifetime. Harkness et al. (1986)

12 1 distinguished the soil carbon into “young” and “old” fractions with the age less than 20 years

2 and more than 300 years, respectively. The “young” cycling component comprises about

3 50% of humus in the top 10 cm of the soil, and less then 5% in more deep horizons. Hahn

4 and Buchmann (2004) found a ratio of active to passive humus of 43 to 62% in the top layer

5 (up to 20 cm) of different European forest soils. They assumed that all carbon below is

6 passive.

7

8 High-latitude ecosystems contain about 25% of the total world soil carbon pool in the

9 permafrost and the seasonally-thawed soil layer (Prentice et al., 2001). A recent report of

10 Intergovernmental Panel on Climate Change suggests that “migration of boreal forest

11 northward into tundra would initially lead to an increase in carbon storage in the ecosystem

12 due to the larger biomass of trees than of herbs and shrubs, but over a longer time (e.g.,

13 centuries), changes in soil carbon would need to be considered to determine the net effect”

14 (Denman et al., 2007). Results of our study indicate that SOM has faster turnover times in

15 forests than in tundra ecosystems. This could be explained not only by generally higher

16 temperatures in forest ecosystems, but also by changes in soil type. This is in line with

17 conclusions by Goryachkin et al. (2000) that an essential enhancement of humus

18 decomposition rate in high latitudes in Eurasia is possible in case of a noticeable northward

19 shift of the treeline.

20

21 For the southern forest boundary our results for soil carbon cycling indicate a possibility of

22 behaviour opposite to that on the taiga-tundra boundary. Projections of precipitation changes

23 over land in 21st century are equivocal, but in general they suggest an increase in continental

24 aridity. In case of drier climate and reduction of forest cover, forest soils could be replaced

25 with grassland soils with slower turnover. This conclusion, in principle, is supported by

26 geological evidence from the past. Alexandrovsky and Chichagova (1998a,b) reported a

27 study of buried Chernozem soils in European Russia which were formed during the mid-

28 Holocene climatic optimum and replaced later by forest soils because climate became cooler

29 and wetter. A similar Holocene soil dynamics was reported by Bork (1998) for a site in

30 Germany. However, a direct analogue with the Holocene period is inappropriate because

31 anthropogenically-induced, decadal-scale climate change is much faster than slow,

13 1 millennial-scale climate dynamics in the Holocene. Besides, vegetation cover in this region

2 is fully controlled by an agricultural practice, and future formation of fertile natural

3 grassland is very unlikely. However, with changes in vegetation cover, the soil type – and

4 carbon cycling – will be changed as well.

5

6 Since changes in soil types will follow changes in climate and land cover, we suggest that

7 pedogenesis is an important factor influencing future dynamics of soil carbon fluxes. Up to

8 now, an effect of soil type changes, as well a clear evidence from 14C measurements that

9 most of soil organic carbon has millennial time scale are basically neglected in the global st 10 carbon cycle models used for projections of atmospheric CO2 in 21 century and beyond.

14 1 Acknowledgements

2

3 This study, performed during the early 1990s when all authors were working in scientific

4 institutions in Moscow, was strongly influenced and encouraged by Prof. Yuri Svirezhev, a

5 leader of international school in global biospheric modelling. We will always remember him

6 as an outstanding scientist with an extraordinary gift to see simplicity of very complex

7 processes and a tremendous ability to synthesise knowledge from very different scientific

8 disciplines.

15 1

2 References 3 4 Afanasieva, E.A., 1966. Chernozems of the Mid-Russian Upland. Nauka, Moscow, 224 pp. 5 (in Russian) 6 Alexandrovskiy, A.L., and Chichagova, O.A., 1998a. The 14C age of humic substances in 7 . Radiocarbon, 40 (2), 991-997. 8 Alexandrovsky, A.L., and Chichagova, O.A., 1998b. Radiocarbon age of Holocene 9 paleosols of the East European forest-steppe zone, Catena, 34, 197-207. 10 Basilevich, N.I. and Shmakova, E.I., 1986. Change of the ’s steppe geosystems 11 under agricultural use. In: Grin, A.M. and Mukhina, L.I. (Editors), The Structure and 12 Functioning of Geosystems. Proceedings of the All-Union Conference on Geosystem 13 Monitoring, Institute of Geography, Moscow, pp. 123-143 (in Russian). 14 Batjes, N.H.. 1996. and in the Soils of the World. European Journal 15 of 47: 151-163. 16 Bolin, B., 1986. How much CO2 will remain in the atmosphere? The carbon cycle and 17 projections for the future. In: Bolin, B., Doos, B.R., Jager, J. and Warrick, R.A. (Editors), 18 The Greenhouse Effect, Climate Change and Ecosystems. SCOPE Report 29, John 19 Wiley, New York, pp. 157-203. 20 Cherkinsky, A.E. and Brovkin, V.A., 1993. Dynamics of radiocarbon in soils. Radiocarbon, 21 35: 363-367. 22 Cherkinsky, A.E. and Goryachkin, S.V., 1993. Distribution and renovation time of soil 23 carbon in boreal and subarctic ecosystems of European Russia. In: Vilson, T.S. and 24 Kolchugina, T.P. (Editors), Carbon Cycling in Boreal Forests and Sub-Arctic 25 Ecosystems, Proc. Int. Workshop, September 1991, EPA/600/R-93/084. Corvallis, 26 Oregon, pp. 65-70. 27 Chichagova, O.A. and Cherkinsky, A.E., 1993. Problems in radiocarbon dating of soils. 28 Radiocarbon, 35: 351-362. 29 Cox, P.M., Betts, R.A., Jones, C.D., Spall, S.A. and Totterdell, I.J., 2000. Acceleration of 30 global warming due to carbon-cycle feedbacks in a coupled . Nature, 408: 31 750-750. 32 Cramer, W., Bondeau, A., Woodward, F.I., Prentice, I.C., Betts, R.A., Brovkin, V., Cox, 33 P.M., Fisher, V., Foley, J.A., Friend, A.D., Kucharik, C., Lomas, M.R., Ramankutty, N., 34 Sitch, S., Smith, B., White, A. and Young-Molling, C., 2001. Global response of 35 terrestrial ecosystem structure and function to CO2 and climate change: results from six 36 dynamic global vegetation models. Global Change Biology, 7: 357-373. 37 Denman, K.L., G. Brasseur, A. Chidthaisong, P. Ciais, P.M. Cox, R.E. Dickinson, D. 38 Hauglustaine, C. Heinze, E. Holland, D. Jacob, U. Lohmann, S Ramachandran, P.L. da 39 Silva Dias, S.C. Wofsy and X. Zhang, 2007. Couplings Between Changes in the Climate 40 System and . In: Climate Change 2007: The Physical Science Basis, Ed. 41 by Solomon, S., et al. Cambridge University Press, Cambridge. 42 Fang, C., Smith, P. and Smith, J.U., 2006. Is resistant soil organic matter more sensitive to 43 temperature than the labile organic matter? Biogeosciences, 3: 65-68. 44 FAO/ISRIC/ISSS, 1998. World reference base for soil resources. FAO Soils Bulletin No. 45 84. FAO, Rome. 88 p

16 1 Friedlingstein, P., Cox, P., Betts, R., Bopp, L., Von Bloh, W., Brovkin, V., Cadule, P., 2 Doney, S., Eby, M., Fung, I., Bala, G., John, J., Jones, C., Joos, F., Kato, T., Kawamiya, 3 M., Knorr, W., Lindsay, K., Matthews, H.D., Raddatz, T., Rayner, P., Reick, C., 4 Roeckner, E., Schnitzler, K.G., Schnur, R., Strassmann, K., Weaver, A.J., Yoshikawa, C. 5 and Zeng, N., 2006. Climate-carbon cycle feedback analysis: Results from the C4MIP- 6 model intercomparison. Journal of Climate, 19: 3337-3353. 7 Fridland, V.M. and Lebedeva, I.I. (Editors), 1974. Chernozems of the USSR. Kolos, 8 Moscow, 558 pp. (in Russian) 9 Gaudinski, J.B., Trumbore, S.E., Davidson, E.A. and Zheng, S.H., 2000. Soil carbon cycling 10 in a temperate forest: radiocarbon-based estimates of residence times, sequestration rates 11 and partitioning of fluxes. Biogeochemistry, 51: 33-69. 12 Gilmanov, T.G.., 1978. Mathematical Modelling of Biochemical Cycles in Grasslands. 13 Moscow State University, Moscow, 169 pp. (in Russan). 14 Bork, H.-R., Dalchow, C., Faust, B., Piorr, H.-P., Schatz, Th., 1998. 15 Landschaftsentwicklung in Mitteleuropa. Gotha und Stuttgart (Klett-Perthes), 328 pp. 16 Gorham, E., 1991, Northern peatlands: Role in the carbon cycle and probable responses to 17 climatic warming: Ecological Applications, 1:182-195. 18 Goryackin, S.V., Cherkinsky, A.E., and Chichagova, O.A., 2000. The soil organic carbon 19 dynamics in high latitudes of Eurasia using 14C data and the impact of potential climate 20 change. R. Lal, J.M. Kimble, and B.A. Stewart (Eds.). Global Climate Change and Cold 21 Regions Ecosystems. Lewis Publishers Boca Raton, FL, 145-162. 22 Hahn, V. and Buchmann, N., 2004. A new model for soil organic carbon turnover using 23 bomb carbon. Global Biogeochemical Cycles, 18, GB1019, doi:10.1029/2003GB002115. 24 Harkness, D.D., Harrison, A.F. and Bacon, P.J., 1986. The temporal distribution of ‘bomb’ 25 14C in a forest soil. Radiocarbon, 28: 328-337. 26 Ignatenko, I.V., 1979. Soils of East European tundra and forest-tundra. Nauka, Moscow, 27 280pp. (in Russian) 28 Ivanov, I.V. and Aleksandrovsky, A.L., 1984. The methods of studying soil evolution and 29 age. Reprint of the Scientific Center of Biological Researches, Putchino, 51 pp. (in 30 Russian) 31 Kirschbaum, M.U.F., 2000. Will changes in soil organic matter act as a positive or negative 32 feedback on global warming? Biogeochemistry 48: 21-51. 33 Knorr, W., Prentice, I. C., House, J. I., and Holland, E. A., 2005. Longterm sensitivity of 34 soil carbon turnover to warming, Nature, 433, 298–301. 35 Lal, R., 2004. Soil Impacts on Global Climate Change and Food 36 Security. Science 304 (11) 1623-1627. 14 37 Levin I., Kromer B., 1997, Twenty years of atmospheric CO2 observations at Schauinsland 38 Station, Germany. Radiocarbon, Vol. 39: 205-218. 39 Margolina, N.Y., Alexandrovsky, A.L., Ilichev, B.A., Cherkinsky, A.E. and Chichagova, 40 O.A., 1988. Age and evolution of Chernozems. Nauka, Moscow, 144 pp. (in Russian) 41 Nogina N.A. and Rode A.A. (Editors), 1977-1981. Podzolic soils of the European part of the 42 USSR. Nauka, Moscow, v. 1-4. 1043 pp. (In Russian) 43 Perruchoud, D., 1996. Modeling the dynamics of nonliving organic carbon in a changing 44 climate: a case study for temperate forests. Diss. ETH No. 11900, Swiss Federal Institute 45 of Technology: Zürich, Switzerland, 213 pp.

17 1 Post, W.M., Emanuel, W.R., Zinke, P.J. and Stangenberger, A.G., 1982. Soil carbon pools 2 and world life zones. Nature 298: 156-159. 3 Post, W. M., A. W. King, and S. D. Wullschleger. 1997. Historical variations in terrestrial 4 biospheric carbon storage. Global Biogeochemical Cycles 11:99-109. 5 Prentice IC, Farquhar GD, Fasham MJR et al., 2001. The carbon cycle and atmospheric 6 . In. Climate Change 2001: The Scientific Basis (eds Houghton JT et al.), 7 Cambridge Univ. Press, New York 8 Reichstein, M., Kätterer, T., Andrén, O., Ciais, P., Schulze, E.-D., Cramer, W., Papale, D., 9 and Valentini, R., 2005. Temperature sensitivity of decomposition in relation to soil 10 organic matter pools: critique and outlook, Biogeosciences, 2, 317-321. 11 Reimer PJ, Baillie MGL, Bard E, Bayliss A, Beck JW, Bertrand C, Blackwell PG, Buck CE, 12 Burr G, Cutler KB, Damon PE, Edwards RL, Fairbanks RG, M Friedrich, Guilderson TP, 13 Hughen KA, Kromer B, McCormac FG, Manning S, Bronk Ramsey C, Reimer RW, 14 Remmele S, Southon JR, Stuiver M, Talamo S, Taylor TW, van der Plicht J, and 15 Weyhenmeyer CE, 2004. IntCal04 Terrestrial Radiocarbon Age Calibration, 0-26 cal kyr 16 BP Radiocarbon 46: 1029-1058. 17 Rozov and Rudneva, 1986. Soil map of the USSR . In: Atlas of the USSR, GUGK, Moscow. 18 Schimel, D.S., Braswell, B.H., Holland, E.A., McKeown, R., Ojima, D.S., Painter, T.H., 19 Parton, W.J. and Townsend, A.R., 1994. Climatic, edaphic and biotic controls over 20 storage and turnover of carbon in soils. Global Biogeochemical Cycles, 8: 279-293. 21 Sharpenseel, H.W., 1971. Radiocarbon dating of soils-problems, troubles, hopes. In 22 : Origin, Nature and Dating of Paleosoils. Jerusalem: 77-88. 23 Scheffer, M., Brovkin, V. and Cox, P.M.: 2006, Positive feedback between global warming 24 and atmospheric CO2 concentration inferred from past climate change, Geophysical 25 Research Letters, 33, doi:10.1029/2005GL025044. 26 Sklyarov, G.A. and Sharova, A.S.,1970. Soils of European North forests. Nauka, Moscow, 27 271 pp. (in Russian) 28 Torn, M.S., and Harte, J., 2006. Missing feedbacks, asymmetric uncertainties, and the 29 underestimation of future warming. Geophysical Research Letters, 33, L10703, 30 doi:10.1029/2005GL025540. 31 Trumbore, S.E., Davidson, E.A., Decamargo, P.B., Nepstad, D.C. and Martinelli, L.A., 32 1995. Belowground Cycling of Carbon in Forests and Pastures of Eastern Amazonia. 33 Global Biogeochemical Cycles, 9: 515-528. 34 van der Plicht, J, Beck JW, Bard E, Baillie MGL, Blacjwell PG, Buck CE, Friedrich M, 35 Guilderson TP, Hughen KA, Kromer B, McCormac FG, Bronk Ramsey C, Reimer PJ, 36 Reimer RW, Remmele S, Richards DA, Southon JR, Stuiver M, and Weyhemmeyer, 37 2004. NotCal04 - Comparison/Calibration 14C Records 26-50 cal kyr BP. Radiocarbon, 38 46:1225-1238. 39 40 41 42 43 44

18 1 Table 1. Carbon storages and carbon turnover rates for upper 1 m soil of European Russia 2 ______3 N Soil type Area, Carbon Turnover rate, Annual C fluxes 2 4 ______103 storage, 10-3 yr-1 g/(m ⋅yr) Tg/yr 2 2 5 Russian WRB km kg/m depth, cm 6 classification (FAO, 1998) 0-20 20-100 7 1 Podbur Entic 60 20.7 4.4 0.29 59.5 3.6 8 Podzols 9 2 Tundra gley Turbic 110 17.4 2.5 0.17 32.4 3.6 10 Cryosol 11 3 Peat bog, Histosols 104 37.0 1.6 0.25 39.1 4.1 12 northern taiga 13 4 Gley- Gleyic 187 10.7 2.0 0.08 14.8 2.8 14 podzolic Albeluvisols 15 5 , Haplic 199 10.0 2.8 0.15 22.4 4.5 16 northern taiga Podzols 17 6 Bog-podzolic Gleyic 199 18.5 4.5 0.32 51.0 10.2 18 Albeluvisols 19 7 Podzolic Dystric 315 9.1 2.1 0.15 12.9 4.1 20 Albeluvisols 21 8 Podzol, Haplic 178 10.5 2.2 0.53 15.6 2.8 22 middle taiga Podzols 23 9 Peat bog Histosols 57 63.8 8.0 1.39 147.3 8.4 24 southern taiga 25 10 Sod-podzolic Eutric 616 7.4 4.3 0.20 18.5 11.4 26 Albeluvisols 27 11 Sod-podzolic Ferric 245 5.9 7.8 0.49 29.9 7.3 28 Podzols 29 12 Grey Forest 253 15.8 1.6 0.06 11.8 3.0 30 13 Leached Luvic 226 30.3 0.7 0.10 10.0 2.3 31 Chernozems Chernozems 32 14 Typical Chernic 86 31.9 0.7 0.25 13.0 1.1 33 Chernozems Chernozems 34 15 Ordinary Calcic 118 26.0 0.8 0.40 13.6 1.6 35 Chernozems Chernozems 36 16 Meadows Gleyic 37 33.4 0.4 0.13 7.5 0.3 37 Chernozems Chernozems 38 17 Southern Calcic 122 19.3 3.2 0.42 26.0 3.2 39 Chernozems Chernozems 40 18 Chestnut Haplic 208 10.3 1.0 0.32 5.3 1.1 41 Kastanozems 42 19 Calcareous Calcic 122 23.9 0.7 0.23 8.8 1.1 43 Chernozem Chernozems 44 20 Semidesert Calcisols 269 2.9 0.2 0.12 0.6 0.2 45 46 Total: 3,711 76.7

19 1 Figure captions. 2 3 Figure 1. Change of specific carbon activity (% of NBS) in the atmosphere used in the Eq. 9 4 (Bolin, 1986). 5 6 Figure 2. Profiles of soil carbon storages of mineral layers for podzolic, sod-podzolic 7 (loam), and typical chernozem soils. The storage is shown in kgC/m2 per 10 cm layer 8 centred at the layer depth. 9 10 Figure 3. Soil carbon storage in kgC/m2 for 1 m soil layer shown separately for organic and 11 mineral layers. Soil type number is in accordance with Table 1. The type number roughly 12 corresponds to a north-south transect of European territory of Russia. 13 14 Figure 4. Profiles of turnover rates of soil carbon (10-3 yr-1) for mineral layers of podzolic, 15 sod-podzolic (loam), and typical chernozem soils. 16 17 Figure 5. Maps of soil carbon traits for analyzed territory of European Russia. Both mineral 18 and organic soil layers are accounted for. A) Soil carbon storage (kgC/m2), 0-100 cm. B) 19 Turnover rate (10-3 yr-1), 0-20 cm layer. C) Turnover rate (10-3 yr-1), 20-100 cm layer. D) 20 Soil turnover flux, (gC m-2 yr-1), 0-100 cm.

20 200

150

100

% of NBS of % 50

0

1950 1960Year 1970 1980

Figure 1. Atmospheric 14C activity 6 Podzolic

Sod-Podzolic, 5 loam Typical Chernozems 4

3 kg C/m2

2

1

0 5 152535455565758595 Soil depth, cm

Figure 2. Profiles of soil carbon storage (10 cm layers) 70

60 Organic layers Mineral layers 50

2 40

kgC/m 30

20

10

0 1234567891011121314151617181920 Soil type

Figure 3. Soil carbon storage (soil type number accordingly to Table 1) Podzolic 10 Sod-Podzolic, loam Typical Chernozems

1 -1 -1 yr -3

0.1 Turnover rate, 10 0.01

0.001 5 152535455565758595 Soil depth, cm

Figure 4. SOM turnover rates, mineral layer Figure 5a. Soil carbon storage (kgC/m2) Figure 5b. Soil turnover rate (10-3 yr-1), 0-20 cm layer. Figure 5c. Soil turnover rate (10-3 yr-1), 0-20 cm layer. Figure 5d. Soil turnover flux (gC m-2 yr-1), 0-100 cm layer.