Interactive comment on “ Microbial dormancy and its impacts on Arctic terrestrial ecosystem carbon budget ” by Junrong Zha and Qianlai Zhuang

We thank the Associate Editor and two referees for their providing constructive comments to this manuscript. Below we detail how we have revised the manuscript following their suggestions. 1. The authors almost completely ignored in their discussion previous efforts to include microbial dormancy in soil biogeochemistry models. It would be interesting, for example, to know what is the contribution of this work to those previous efforts; and what are the consistencies/inconsistencies of their findings in comparison with results from other “dormancy models”. Response: Thanks for the comments. We have strengthened the discussion by comparing our results with previous ones in the revised manuscript. 2. The language. There are so many small language issues that make the reading of this manuscript hard. The authors jump between active and pas-


Introduction 76
The land ecosystems in northern high latitudes (>45 ºN) occupy 22% of the global 77 surface and store over 40% of the global soil organic carbon (SOC) ( with existing data of carbon stocks and fluxes, our study incorporated the microbial module into 158 an extant MIC-TEM that simulates carbon data dynamically. This coupling enables us to 159 extrapolate our model to whole northern high-latitudes region, rather than only for temperate 160 forest region in He et al. (2015). In our new model (MIC-TEM-dormancy), microbial biomass 161 pool was divided into two fractions, including the dormant and active microbial biomass pools. 162 The two microbial biomass pools and the reversible transition between them have been 163 considered explicitly in the new model (Figure 1), which was ignored in MIC-TEM. 164 In previous MIC-TEM, heterotrophic respiration (RH) is calculated as: 165 (1) 166 Where ASSIM and CUE represent microbial assimilation and carbon use efficiency, respectively. 167 For detailed carbon dynamics in MIC-TEM, see Zha & Zhuang (2018). 168 Here we revised MIC-TEM by incorporating microbial dormancy dynamics according to 169 He et al. (2015). In MIC-TEM-dormancy, the soil heterotrophic respiration RH is comprised of 170 three parts: the maintenance respiration from the active and dormant microorganisms and the CO2 171 production through the process of microbial assimilation (He et al., 2015): 172 Here parameter  is maintenance weight (h -1 ), CNsoil and CNmic denotes the C:N ratios of soil and 188 that of microbial biomass. Besides, Φ is the substrate saturation level and defined as in He et al. 189 (2015) and Wang et al. (2014): 190 Where Ks is the half saturation constant for substrate uptake as indicated by the Michaelis-Menten 192 kinetic, and S is soluble C substrates that are directly accessible for microbial assimilation (Wang 193 et al., 2014). Here we quantified concentration of soluble C substrates that are directly accessible 194 for microbial assimilation by using conceptual framework from Davidson et al. (2012): 195 The term 'Soluble C' denotes the state variable of soluble carbon pool. Dliq is the diffusion 197 coefficient of the substrate in the liquid phase, and is formulated as: 198 Where BD is the bulk density and PD is the soil particle density. θ is the volumetric soil moisture.   Where rdeath and rEnzProd are the rate constants of microbial death and enzyme production,  With the modification of microbial carbon dynamics by considering microbial life-history trait, 236 soil decomposition is changed since it is controlled by microbes. When microbial dormancy is 237 considered, the number of active microbes that participate in soil decomposition is much less. The 238 changes in soil decomposition directly influence the amount of soil respiration, and further 239 influence soil nitrogen (N) mineralization that determines soil N availability for plants, affecting 240 gross primary production (GPP). Since both GPP and RH can be affected by microbial dormancy, 241 net ecosystem production (NEP) will also be affected.  Table 1). Here we calibrated the MIC-TEM-dormancy at six representative 246 sites with gap-filled monthly net ecosystem productivity (NEP, gCm -2 mon -1 ) data in northern high 247 latitudes (Table 2). Site-level climatic data and soil texture data were organized for driving model.  (Table 1) to minimize the difference between the monthly simulated and measured 253 NEP at the chosen sites. The cost function of the minimization is: 254 Where NEP obs,i and NEP sim,i are the observed and simulated NEP, respectively. k is the number of 256 data pairs for comparison. Except for the parameters of microbial dormancy, other parameters are 257 derived directly from MIC-TEM (Zha & Zhuang, 2018). The optimized parameters were used for 258 model validation and regional simulations. 259 For model validation, we chose another six sites that containing monthly NEP data from 260 AmeriFlux network (Table 3). Moreover, we also conducted site-level validations with monthly 261 soil respiration data from AmeriFlux network and Fluxnet dataset. The site information was 262 provided in Table 4. For these sites, we assumed 50% of soil respiration was heterotrophic 263 respiration (RH) for forest (Hanson et al., 2000), 60% and 70% of that was RH for grassland (Wang 264 et al., 2009) and tundra (Billings et al., 1977). Because there is a limited amount of available RH 265 data, we could not conduct a regional validation for all pixels in northern high latitudes. Instead,

Spatial extrapolation 276
For historical simulations during the 20 th century, two sets of regional simulations using 277 MIC-TEM-dormancy and MIC-TEM at a spatial resolution of 0.5° latitude × 0.5° longitude were 278 conducted. Our model simulation contains two parts: spin-up and transient simulation. A typical 279 spin-up was conducted to get the model to a steady state for each spatial location, which will be from literatures. In our model, we assumed that soil texture, elevation, and potential natural 293 vegetation data only vary spatially, not vary over time (Zhuang et al., 2015). 294 In addition, regional simulations over the 21 st century were conducted under two 295 Intergovernmental Panel on Climate Change (IPCC) climate scenarios (RCP 2.6 and RCP 8.5). 296 The future climatic forcing data under these two climate change scenarios were derived from the 297 HadGEM2-ESmodel, which is a member of CMIP5project213 (https://esgf-298 node.llnl.gov/search/cmip5/). Then the regional estimations were obtained by summing up the 299 gridded outputs for our study region. The positive simulated NEP represents a CO2 sink from the 300 atmosphere to terrestrial ecosystems, while a negative value represents a source of CO2 from 301 terrestrial ecosystems to the atmosphere. 302

Parameter equifinality effects 303
Our previous studies using TEM has demonstrated that equifinality derived from site-level 304 parameterization will affect the uncertainty in the estimation of regional carbon dynamics (Tang 305 and Zhuang, 2008Zhuang, , 2009). Here equifinality refers to that a number of sets of parameters result in 306 model simulations that all match the data similarly well. To quantify this effect on our simulation 307 uncertainty, we conducted ensemble regional simulations with 50 sets of parameters for both 308 historical and future studies. The 50 sets of parameters were obtained according to the method in 309 Tang and Zhuang (2008). 310

Inversed Model Parameters and model validation 312
Using SCE-UA ensemble method, 50 independent sets of parameters were converged to 313 minimize the objective function. Then the optimized parameters are calculated as the mean of these 314 50 sets of inversed parameters. The boxplot of parameter posterior distributions reflects different 315 ecosystem properties at these sites ( Figure 3). For instance, growth yield was higher in tundra types 316 than in forests, meaning microorganisms in environment with higher energy limitation tend to 317 enhance the efficiency of energy transportation. Besides, alpha, the maintenance weight, was also 318 higher in tundra types than in forests. From the plot for parameter beta, the ratio of dormant 319 maintenance rate to specific maintenance rate for active biomass in tundra types is lower than that 320 in forest types. Other microbial related parameters did not differentiate much among different 321 vegetation types. 322 After parameterization, the MIC-TEM-dormancy was validated with monthly NEP data for 323 six representative ecosystems, and the comparisons between monthly observed NEP and 324 simulated NEP were presented in Figure 4. With the optimized parameters, the dormancy-based 325 model was used to reproduce NEP to compare with the measured NEP (Table 5). The R 2 ranges 326 from 0.67 for Atqasuk to 0.93 for Bartlett Experimental Forest (Table 5). Generally, our new 327 model performs better for forest ecosystems than for tundra ecosystems. Compared with MIC-328 TEM, dormancy model performs better for alpine tundra, temperate coniferous forest, and 329 grassland. For other sites, both models show similar performance (Table 5). Besides, a set of 330 monthly soil respiration data were selected to evaluate the estimated RH. The comparisons 331 between monthly observed RH and simulated RH from two contrasting models were conducted 332 ( Figure 5). MIC-TEM-dormancy has higher R 2 and lower root mean square error (RMSE) ( Table  333 6). Sixty-one sites with average annual RH in northern high-latitude regions were used to further 3.2 Regional carbon dynamics during the 20 th century 341 Regional extrapolation with both models estimated a regional carbon sink but with different 342 magnitudes (Figure 7c). With optimized parameters, MIC-TEM estimated a regional carbon sink 343 of 77.6 Pg with the interannual standard deviation of 0.21 Pg C yr -1 during the 20 th century. 344 However, MIC-TEM-dormancy nearly doubles the sink at 153.5 Pg with the interannual standard 345 deviation of 0.12 Pg C yr -1 during the last century (Figure 7c). At the end of the century, MIC-346 TEM estimated that NEP reaches 1.0 Pg C yr -1 in comparison with MIC-TEM-dormancy estimates 347 of 1.5 Pg C yr -1 (Figure 7c). Both models simulated similar trends for regional NPP, RH and NEP 348 ( Figure 7). Generally, they show an increasing trend in the 20th century (Figure 7). Meanwhile, 349 with optimized parameters, MIC-TEM-dormancy estimated NPP and RH at 7.94 Pg C yr -1 and 6.4 350 Pg C yr -1 , which are 5.8% and 16.3% less than the estimations from MIC-TEM, respectively 351 (Figures 7a and 7b). This pronounced difference of NEP between two models comes from the 352 disparity between the simulated NPP and RH with them since NEP is calculated as the difference 353 between NPP and RH. Without considering dormancy, MIC-TEM estimates more active microbial 354 biomass since it assumes the whole microbial biomass pool will participate in soil decomposition. 355 The fact is only active part of microbial biomass can affect organic matter decomposition, meaning 356 MIC-TEM overestimates RH. On the other hand, overestimation of RH can induce higher nitrogen 357 uptake by plants, which will accelerate rate of photosynthesis and further enhance NPP projection. 358 Although MIC-TEM estimates higher NPP and RH than MIC-TEM-dormancy does, NEP estimated 359 from MIC-TEM is actually lower. 360 The average annual seasonal patterns of NPP, RH and NEP during the 1990s were also 361 organized from regional simulations with two models (Figure 8). Temporally, both models 362 projected higher NPP and RH in summer than in winter (Figures 8a and 8b)   Pg C with an interannual standard deviation of 0.1 Pg C yr -1 (Figure 9). MIC-TEM-dormancy 392 estimates NPP and RH at 9.9 Pg C yr -1 and 8.7 Pg C yr -1 , which are 0.5 Pg C yr -1 and 1.7 Pg C yr -393 1 less than the estimations from MIC-TEM, respectively (Figure 9). Moreover, simulations under 394 the two contrasting climate scenarios (RCP 2.6 and RCP 8.5) exhibit a large difference of 81.1 395 Pg C of cumulative NEP during the 21 st century by MIC-TEM, but only 6.3 Pg C of that by 396 MIC-TEM-dormancy. This difference indicates microbes provide a resistant response to climate 397 change due to dormancy to some extent (Treseder et al., 2011). 398 The average annual seasonal patterns of NPP, RH and NEP during the 2990s by two 399 models were also presented ( Figure 10). MIC-TEM-dormancy estimated higher RH in winter, but 400 lower RH in summer under both future scenarios ( Figure 10). NPP is the same in winter with or 401 without dormancy, and in the late summer is higher than that without dormancy, especially in the 402 RCP 8.5 scenario. The combined flattening patterns of NPP and RH result in different patterns 403 for NEP. Under the RCP 2.6 scenario, MIC-TEM-dormancy predicts higher NEP from June to 404 October, but lower NEP from January to April compared to MIC-TEM ( Figure 10). Under the 405 RCP 8.5 scenario, MIC-TEM-dormancy predicts higher NEP from June to September, but much 406 lower NEP in other months than MIC-TEM ( Figure 10). 407

Regional uncertainty considering equifinality effects during 20 th and 21 st centuries 408
The ensemble simulations for the 20 th century is shown in Figure 11. Given the 409 uncertainty in parameters, MIC-TEM-dormancy predicted that the regional cumulative carbon and 8.83 Pg C yr -1 from a no-dormancy model, which is 21.3% higher than the dormancy model. 448 Although their study region and simulation period are different from our study, the results can 449 still be comparable. Both studies indicated that the magnitude of RH from no-dormancy model 450 are higher than dormancy models. Second, high soil respiration stimulates N mineralization in 451 soils (Zhuang et al., 2001(Zhuang et al., , 2002, making more nutrients for photosynthesis of plants (Raich et 452 al., 1991;McGuire et al., 1995). Therefore, NPP will be higher due to the N enrichment from 453 higher RH. However, how NEP will change is still unclear. Our estimates of the northern While our analysis suggests it is important to incorporate microbial dormancy dynamics 496 into a process-based biogeochemistry model to more adequately simulate carbon dynamics in 497 northern high latitudes, we do confront modeling dilemmas. First, our process-based models 498 have a relatively large number of parameters, which unavoidably creates the "equifinality" 499 problem as recognized in our previous studies for the model (e.g., Zhuang, 2008, 500 2009). To alleviate this problem in this analysis, we have conducted parameter ensemble 501 simulations at both site and regional levels and presented our results with uncertainties, which 502 could be a standard approach for process-based complex biogeochemistry modeling analyses. 503 Second, incorporating more ecosystem processes increases the number of parameters in our 504 model, inducing even larger uncertainties for both site level and regional simulations. On the 505 one hand, the more complex model to a certain degree helps capture observations, on the other 506 hand, the model uncertainty has not been constrained or even enlarged. We highlight the need to 507 further investigate this trade-off within the modeling research community. 508 509

Conclusions 510
This study incorporated microbial dormancy into a detailed microbial-based soil 511 decomposition biogeochemistry model to examine the fate of large Arctic soil carbon under 512 changing climate conditions. Regional simulations using MIC-TEM-dormancy indicated that, 513 over the 20 th century, the region is a carbon sink of 166.8 ± 97.7 Pg. This sink could decrease to 514 175.9 ± 105.4 Pg under the RCP 8.5 scenario or 125.4 ± 85.5 Pg under the RCP 2.6 scenario 515 during the 21 st century. Whether considering microbial dormancy or not can cause large 516 differences in soil decomposition estimation between two models. Meanwhile, due to available 517 nitrogen affected by soil decomposition, net primary production is consequently influenced in 518 these two centuries. The combined changes in soil decomposition and net primary production led 519 to large differences in carbon budget estimation between two models. Compared with MIC-520 TEM, MIC-TEM-dormancy projected 75.9 Pg more C stored in the terrestrial ecosystems over 521 the last century, 50.4 Pg and 125.2 Pg more C under the RCP 8.5 and RCP 2.6 scenarios, 522 respectively. This study highlights the importance of the representation of microbial dormancy in 523 earth system models in order to adequately quantify the carbon dynamics in northern high 524 latitudes. 525