The authors were trying to evaluate the impacts of using prognostic and diagnostic wildfire schemes on ecosystem carbon and hydrological cycles simulated by CLM5-BGC. They found the default CLM5-BGC overestimated/underestimated the burned area in Eastern Siberia/Alaska, causing the overestimating/underestimation of wildfire carbon emissions. In contrast, the CLM5-BGC prescribed with observational burned area showed evident improvement in simulating wildfire carbon emissions for both regions. They further compared the two simulations with different wildfire schemes, in terms of major carbon and water fluxes, and showed larger influences on NEE than those for other variables. The modeling idea was unique, in terms of prescribing remote-sensing burned area within land surface model, especially across the high latitudes regions. However, the model evaluations and intercomparisons still need to be improved. For example, the authors may compare these two model results for more variables (e.g., LAI, GPP, ET, Soil Moisture) using different sources of observations or observation-based products. The authors could also add more thoughts/analyses on thow to quantify and reduce wildfire related biases for CLM5-BGC, in terms of drivers, processes and parameters. Additionally, sentences between lines 231 and 234 read confusing; and color scheme in Fig. 3 needs to be reversed?

Hocheol Seo and Yeonjoo Kim integrated GFED burned area dataset into CLM5-BGC model and investigated how fire activity affect ecosystem carbon and water fluxes over Alaska and Easter Siberia. They found that using GFED observed burned area, CLM5-BGC performed better in capturing fire emissions. Moreover, the carbon emissions over Alaska was sensitive to wildfire, while transpiration over Easter Siberia was insensitive to burn. The paper is well constructed. Below are major concerns:

• The design of the model experiment needs to be improved to fully account the impact of historical fires and for a fair comparison between OL vs EXP-GFED4 runs.

This study used BGC version of CLM5, that have carbon, nutrient, water cycles. The 200 year spin up might be enough to stabilize soil temperature and moisture, but is too short to stabilize soil carbon pool and ensure a quasi-steady state condition. Suggestion: plot out total ecosystem carbon for the last 10 or 20 years of spinup period, the changes of total ecosystem carbon should be trivial. Also, long-term average of net ecosystem exchange (NEE) should be near zero in the end of spinup. If not, tried a longer spinup period.

After the spinup, a long-term transient simulation (starting from year 1850 or 1901) is necessary to ensure the land use, warming, and CO2 enrichment signals are all appropriately picked up by the CLM5-BGC model. For example, in this period, we often see initial decline of vegetation carbon due to land use, and then enhanced vegetation growth in response to warming and higher CO2 concentration will overwhelm. Such historical changes of vegetation activities, soil moisture conditions will affect fuel availability and combustibility for simulations from year 2001-2012 (period of focus for analysis).  Otherwise, without appropriate spinup and transient simulation, the comparison of fire emissions and transpiration fluxes and others might not be convincing.

• CLM5-BGC baseline model performance and improvement

In order to understand how much of improvement was due to the integration of GFED burned area dynamics, it will be necessary to first showed CLM5-BGC baseline model performance over the area of interest. It will be good to compare OL and EXP-GFED4 simulations against observations (Figure 6-10). Suggested datasets are e.g., FLUXCOM GPP/NEP, MODIS LAI, GLEAM ET, GEOCARBON vegetation biomass.

• Area of focus

The box area in Figure 1 seems arbitrary. It will be better to use geographic boundary (e.g., state of Alaska) instead of a random box.

• Plant functional type differences

It is not clear, how each different plant functional type (PFT) handled when GFED data is integrated to CLM5-BGC at gridcell level. At each gridcell, CLM has multiple PFT that have different level of fuel conditions (e.g., arctic grass vs boreal tree). When the observed burned area gets integrated, how to reasonably assign the burn to each PFT? For example, the observed burn may occur over forest, while in CLM the observed burn is imposed on the whole gridcell that have both trees and grasses.

• Temporal scale issue

GFED is a monthly product, while CLM5-BGC runs at a much finer temporal resolution (e.g., 30min). How to assigned monthly burned area to each individual time step in CLM5-BGC? It will make a big difference to apply the burned area to the beginning versus to the end of each month?