Abstract. In autumn, southeastern France is often affected by heavy precipitation events which may result in damaging flash-floods. The 20 October and 1 November 2008 are two archetypes of the meteorological situations under which these events occur: an upper-level trough directing a warm and moist flow from the Mediterranean towards the Cévennes ridge or a quasi stationary meso-scale convective complex developing over the Rhone valley. These two types of events exhibit a contrasting level of predictability; the former being usually better forecast than the latter. Control experiments performed with the Meso-NH model run with a 2.5 km resolution confirm these predictability issues. The deterministic forecast of the November case (Cévennes ridge) is found to be much more skilful than the one for the October case (Rhone valley). These two contrasting situations are used to investigate the sensitivity of the model for cloud physics parameterisation uncertainties. Three 9-member ensembles are constructed. In the first one, the rain distribution intercept parameter is varied within its range of allowed values. In the second one, random perturbations are applied to the rain evaporation rate, whereas in the third one, random perturbations are simultaneously applied to the cloud autoconversion, rain accretion, and rain evaporation rates. Results are assessed by comparing the time and space distribution of the observed and forecasted precipitation. For the Rhone valley case, it is shown that not one of the ensembles is able to drastically improve the skill of the forecast. Taylor diagrams indicate that the microphysical perturbations are more efficient in modulating the rainfall intensities than in altering their localization. Among the three ensembles, the multi-process perturbation ensemble is found to yield the largest spread for most parameters. In contrast, the results of the Cévennes case exhibit almost no sensitivity to the microphysical perturbations. These results clearly show that the usefulness of an ensemble prediction system based upon microphysical perturbations is case dependent. Additional experiments indicate a greater potential for the multiprocess ensemble when the model resolution is increased to 500 m.

Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009–2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In midlatitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation–snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty, and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the midlatitudes.

Dynamic vegetation models provide the ability to simulate terrestrial carbon pools and fluxes and a useful tool to study how these are affected by climate variability and climate change. At the continental scale, the spatial distribution of climate, in particular temperature and precipitation, strongly determines surface vegetation characteristics. Model validation exercises typically consist of driving a model with observation-based climate data and then comparing simulated quantities with their observation-based counterparts. However, observation-based datasets themselves may not necessarily be consistent with each other. Here, we compare simulated terrestrial carbon pools and fluxes over North America with observation-based estimates. Simulations are performed using the dynamic vegetation model Canadian Terrestrial Ecosystem Model (CTEM) coupled to the Canadian Land Surface Scheme (CLASS) when driven with two reanalysis-based climate datasets. The driving ECMWF reanalysis data (ERA40) and NCEP/NCAR reanalysis I data (NCEP) show differences when compared to each other, as well as when compared to the observation-based climate research unit (CRU) data. Most simulated carbon pools and fluxes show important differences, particularly over eastern North America, primarily due to differences in precipitation and temperature in the two reanalysis. However, despite very different gross fluxes, the model yields fairly similar estimates of the net atmosphere-land CO 2 flux when driven with the two forcing datasets. The ERA40 driven simulation produces terrestrial pools and fluxes that compare better with observation-based estimates. These simulations do not take into account land use change or nitrogen deposition, both of which have been shown to enhance the land carbon sink over North America. The simulated sink of 0.5 Pg C year −1 during the 1980s and 1990s is therefore lower than inversion-based estimates. The analysis of spatial distribution of trends in simulated carbon pools and fluxes shows that the simulated carbon sink is driven primarily by NPP enhancements over eastern United States.

Shugong (2021) Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. The Cryosphere, 15 (2). pp. 771-791.

Because of its location, Canada is particularly affected by snow processes and their impact on the atmosphere and hydrosphere. Yet, snow mass observations that are ongoing, global, frequent (1–5 days), and at high enough spatial resolution (kilometer scale) for assimilation within operational prediction systems are presently not available. Recently, Environment and Climate Change Canada (ECCC) partnered with the Canadian Space Agency (CSA) to initiate a radar-focused snow mission concept study to define spaceborne technological solutions to this observational gap. In this context, an Observing System Simulation Experiment (OSSE) was performed to determine the impact of sensor configuration, snow water equivalent (SWE) retrieval performance, and snow wet/dry state on snow analyses from the Canadian Land Data Assimilation System (CaLDAS). The synthetic experiment shows that snow analyses are strongly sensitive to revisit frequency since more frequent assimilation leads to a more constrained land surface model. The greatest reduction in spatial (temporal) bias is from a 1-day revisit frequency with a 91% (93%) improvement. Temporal standard deviation of the error (STDE) is mostly reduced by a greater retrieval accuracy with a 65% improvement, while a 1-day revisit reduces the temporal STDE by 66%. The inability to detect SWE under wet snow conditions is particularly impactful during the spring meltdown, with an increase in spatial RMSE of up to 50 mm. Wet snow does not affect the domain-wide annual maximum SWE nor the timing of end-of-season snowmelt timing in this case, indicating that radar measurements, although uncertain during melting events, are very useful in adding skill to snow analyses.

This study explores the performance of Environment Canada’s Surface Prediction System (SPS) in comparison to in situ observations from the Brightwater Creek soil moisture observation network with respect to soil moisture and soil temperature. To do so, SPS is run at hyperresolution (100 m) over a small domain in southern Saskatchewan (Canada) during the summer of 2014. It is shown that with initial conditions and surface condition forcings based on observations, SPS can simulate soil moisture and soil temperature evolution over time with high accuracy (mean bias of 0.01 m3 m−3 and −0.52°C, respectively). However, the modeled spatial variability is generally much weaker than observed. This is likely related to the model’s use of uniform soil texture, the lack of small-scale orography, as well as a predefined crop growth cycle in SPS. Nonetheless, the spatial averages of simulated soil conditions over the domain are very similar to those observed, suggesting that both are representative of large-scale conditions. Thus, in the context of the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) project, this study shows that both simulated and in situ observations can be upscaled to allow future comparison with upcoming satellite data.

Although soil moisture is an essential variable within the Earth system and has been extensively investigated, there is still a limited understanding of its spatiotemporal distribution and variability. Thus, the objective of this study is to attempt to reproduce the spatial variability of soil moisture and brightness temperature as measured by point-based and airborne remote sensing measurements. To do so, Environment and Climate Change Canada’s Surface Prediction System (SPS) is run at very high resolution (100 m) over a region of Manitoba (Canada) where an extensive soil moisture experiment took place in the summer of 2012 [SMAP Validation Experiment 2012 (SMAPVEX12)]. Results show that realistic finescale soil texture improves the quality of SPS outputs. Soil moisture spatial average evolution in time is well simulated by SPS. Simulated spatial variability is underestimated when compared to point-based measurements, although results are improved when examined domainwide versus comparisons using grid points corresponding to measurement sites. SPS brightness temperature fields compare well with remote sensing data in terms of spatial variability. It is shown that during drier periods, factors other than soil texture become important with respect to soil moisture spatial variability. However, during periods with plenty of precipitation, soil texture seems essential in improving simulated soil moisture spatial variability at high resolutions. These results support the conclusion that SPS could provide very high–resolution soil moisture products for research and operational purposes if high-resolution soil texture and vegetation products are made available on a larger scale.

lagged correlations between precipitation/temperature and LAI. Improved biosphere-atmosphere interactions and long-term memory in the CRCM5 simulation with interactive phenology leads to better interannual variability, particularly noticeable in the biosphere and atmosphere states during anomalously wet and dry years. This study thus provides useful insights related to the added value of interactive phenology in CRCM5 as well as the nature and variability of biosphere-atmosphere interactions over North America.