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Abstract Precipitation forcing is critical for hydrological modeling as it has a strong impact on the accuracy of simulated river flows. In general, precipitation data used in hydrological modeling are provided by weather stations. However, in regions with sparse weather station coverage, the spatial interpolation of the individual weather stations provides a rough approximation of the real precipitation fields. In such regions, precipitation from interpolated weather stations is generally considered unreliable for hydrological modeling. Precipitation estimates from reanalyses could represent an interesting alternative in regions where the weather station density is low. This article compares the performances of river flows simulated by a watershed model using precipitation and temperature estimates from reanalyses and gridded observations. The comparison was carried out based on the density of surface weather stations for 316 Canadian watersheds located in three climatic regions. Three state-of-the-art atmospheric reanalyses—ERA-Interim, CFSR, and MERRA—and one gridded observations database over Canada—Natural Resources Canada (NRCan)—were used. Results showed that the Nash–Sutcliffe values of simulated river flows using precipitation and temperature data from CFSR and NRCan were generally equivalent regardless of the weather station density. ERA-Interim and MERRA performed significantly better than NRCan for watersheds with weather station densities of less than 1 station per 1000 km2 in the mountainous region. Overall, these results indicate that for hydrological modeling in regions with high spatial variability of precipitation such as mountainous regions, reanalyses perform better than gridded observations when the weather station density is low.

Abstract Approximately 10 years ago, convection‐permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1–4 km) decadal‐long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal (~1 km; ~1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs. This article is categorized under: Climate Models and Modeling > Earth System Models

Abstract Lightning climate change projections show large uncertainties caused by limited empirical knowledge and strong assumptions inherent to coarse-grid climate modeling. This study addresses the latter issue by implementing and applying the lightning potential index parameterization (LPI) into a fine-grid convection-permitting regional climate model (CPM). This setup takes advantage of the explicit representation of deep convection in CPMs and allows for process-oriented LPI inputs such as vertical velocity within convective cells and coexistence of microphysical hydrometeor types, which are known to contribute to charge separation mechanisms. The LPI output is compared to output from a simpler flash rate parameterization, namely the CAPE $$\times$$ × PREC parameterization, applied in a non-CPM on a coarser grid. The LPI’s implementation into the regional climate model COSMO-CLM successfully reproduces the observed lightning climatology, including its latitudinal gradient, its daily and hourly probability distributions, and its diurnal and annual cycles. Besides, the simulated temperature dependence of lightning reflects the observed dependency. The LPI outperforms the CAPE $$\times$$ × PREC parameterization in all applied diagnostics. Based on this satisfactory evaluation, we used the LPI to a climate change projection under the RCP8.5 scenario. For the domain under investigation centered over Germany, the LPI projects a decrease of $$4.8\%$$ 4.8 % in flash rate by the end of the century, in opposition to a projected increase of $$17.4\%$$ 17.4 % as projected using the CAPE $$\times$$ × PREC parameterization. The future decrease of LPI occurs mostly during the summer afternoons and is related to (i) a change in convection occurrence and (ii) changes in the microphysical mixing. The two parameterizations differ because of different convection occurrences in the CPM and non-CPM and because of changes in the microphysical mixing, which is only represented in the LPI lightning parameterization.

CENTURY 4.0, a simulation model of carbon and nitrogen dynamics of terrestrial ecosystems based on the relationships between climate, soil texture, plant productivity, decomposition and human management, was tested against observed data along the boreal forest transect case study (BFTCS) in central Canada. The results show that the simulated average aboveground biomass and net N mineralization were consistent with observed data. The modeled estimates for soil carbon were consistent with those from regional‐scale empirical regression models. High correlation ( R 2 = 0.92) with data was obtained for the simulation of soil carbon dynamics of the boreal forest, but the model overestimated soil carbon (O–20 cm) by 2–8% for fine‐textured soil and underestimated soil carbon by 5–18% for sandy soil. The effects of climatic variation on temporal changes in biomass and soil carbon storage over the past century were found to be very different for southern and northern sites but relatively insensitive to site‐specific soil texture. The main discrepancies between observed data and CENTURY 4.0 results are associated with the effects of soil texture and an inadequate representation of fire disturbance on C dynamics of boreal forests. Further improvements, particularly in the representation of disturbance regimes and in the simulation of slow pool C dynamics, are suggested to enhance its predictive capability.

The effects of climate change and doubling atmospheric CO 2 on carbon dynamics of the boreal forest in the area of the Boreal Forest Transect Case Study in central Canada were investigated using the process‐based plant‐soil model CENTURY 4.0. The results presented here suggest that (1) across the transect climate change would result in increased total carbon in vegetation biomass but decreased overall carbon in soil; (2) increased atmospheric CO 2 concentration under current climatic patterns would result in increased total carbon in vegetation and in soil organic matter; and (3) combined climate change and elevated CO 2 would increase both net primary productivity and decomposition rates relative to the current climate condition, but their combined action would be a reduction of soil carbon losses relative to those due to climate change alone. The interactive effects of climate change and elevated CO 2 , however, are not a simple additive combination of the individual responses. The responses to climate change and elevated CO 2 vary across the climate gradient from southern to northern sites on the transect. The present simulations indicate that the northern sites are more sensitive to climate change than the southern sites are, but these simulations do not consider likely changes in the disturbance regime or changes in forest species distribution.

Summary Aim To investigate effects of within‐season and interannual climate variability on the behaviour of boreal forest ecosystems as simulated by the FORSKA2 patch model. Location Eleven climate station locations distributed along a transect across the boreal zone of central Canada. Methods FORSKA2′s water balance submodel was modified to enable it to behave more realistically under a varying climate. Long‐term actual monthly time‐series of temperature and precipitation data were detrended and used to drive the modified model. Long‐term monthly averages of the same detrended data were used to drive the unmodified model. Results Modifications created significant improvements when simulating species composition at sites in boreal Canada. Simulated forest biomass values were slightly higher than those obtained from the unmodified model using averaged climate records, but resembled the observed distribution of vegetation more closely. Main conclusions Modified FORSKA2 suggests that boreal forest composition and distribution may be more sensitive to changes in monthly rainfall data than to changes in temperature. Climate variability affects seasonal water balances and should be considered when using patch models to forecast vegetation dynamics during and following a period of climate transition. The modified model provided improved representation of the latitudinal trend in spatially averaged biomass density in this region.

Summary Aim Possible effects of current and future climates on boreal vegetation dynamics and carbon (C) cycling were investigated using the CENTURY 4.0 soil process model and a modified version of the FORSKA2 forest patch model. Location Eleven climate station locations distributed along a transect across the boreal zone of central Canada. Methods Both models were driven by detrended long‐term monthly climate data. Using a climate change signal derived from the GISS general circulation model (GCM) 2×CO 2 equilibrium climate scenario, the output from the two models was then used to compare simulated current and possible future total ecosystem C storage at the climate station locations. Results After allowing for their different underlying structures, comparison of output from both models showed good agreement with local field data under current climate conditions. CENTURY 4.0 was able to reproduce spatial variation in soil and litter C densities satisfactorily but tended to overestimate biomass productivity. FORSKA2 reproduced aboveground biomass productivity and spatially averaged biomass densities relatively well. Under the GISS 2×CO 2 scenario, both models generally predicted small increases in aboveground biomass C density for forest and tundra locations, but CENTURY 4.0 predicted greater decreases in soil and litter pools, for overall decreases in ecosystem C storage in the range 16–19%. Main conclusions With some caveats, results imply that effects of increased precipitation (as simulated by the GISS GCM) would more than compensate for any negative effects of increased temperature on forest growth. Increased temperature would also increase decomposition rates of soil and litter organic matter, however, for a net overall decrease in total ecosystem C storage.