ALBERT

Description: 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.

Description: Since November 2014, the Meteorological Services of Canada (MSC) has been running a real-time numerical weather prediction system that provides deterministic forecasts on a regional domain with a 2.5-km horizontal grid spacing covering a large portion of Canada using the Global Environmental Multiscale (GEM) forecast model. This system, referred to as the High Resolution Deterministic Prediction System (HRDPS), is currently downscaled from MSC’s operational 10-km GEM-based regional system but uses initial surface fields from a high-resolution (2.5 km) land data assimilation system coupled to the HRDPS and initial hydrometeor fields from the forecast of a 2.5-km cycle, which reduces the spinup time for clouds and precipitation. Forecast runs of 48 h are provided four times daily. The HRDPS was tested and compared to the operational 10-km system. Model runs from the two systems were evaluated against surface observations for common weather elements (temperature, humidity, winds, and precipitation), fractional cloud cover, and also against upper-air soundings, all using standard metrics. Although the predictions of some fields were degraded in some specific regions, the HRDPS generally outperformed the operational system for a majority of the scores. The evaluation illustrates the added value of the 2.5-km model and the potential for improved numerical guidance for the prediction of high-impact weather.

Description: A new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conducted in the present study demonstrated clear improvements in warm season meteorological predictions with SVS compared to the ISBA scheme. The results also revealed considerable reduction of standard error in the SVS-predicted L-band brightness temperature. This demonstrates the scheme’s ability for better hydrological prediction and its potential for providing more accurate soil moisture analysis. The impact of the photosynthesis module within the current implementation of SVS is, however, found to be negligible on near-surface meteorological prediction and slightly negative for brightness temperature.

Description: A new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in higher correlations with observations and reduced errors. The sensitivity tests carried out during this study revealed that the SVS soil moisture results are not affected significantly by the soil texture data from different sources. The vegetation data source, however, has a major impact on the soil moisture results predicted by SVS, and accurate specification of vegetation characteristics is therefore crucial for accurate soil moisture prediction.

Description: There are numerous water features on the Canadian landscapes that are not monitored. Specifically, there are water bodies over the prairies and Canadian shield regions of North America that are ephemeral in nature and could have a significant influence on convective storm generation and local weather patterns through turbulent exchanges of sensible and latent heat between the land and the atmosphere. In this study a series of numerical experiments is performed with Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) model at 2.5-km grid spacing to examine the sensitivity of the atmospheric boundary layer and the resulting precipitation to the presence of open water bodies. Operationally, the land–water fraction in GEM is specified by means of static geophysical databases that do not change with time. Uncertainty is introduced in this study into this land–water fraction and the sensitivity of the resulting precipitation is quantified for a convective precipitation event occurring over the Canadian Prairies in the summer of 2014. The results indicate that with an increase in open water bodies, accumulated precipitation, peak precipitation amounts, and intensities decrease. Moreover, shifts are seen in times of peak for both precipitation amounts and intensities, in the order of increasing wetness. Additionally, with an increase in open water bodies, convective available potential energy decreases and convective inhibition increases, indicating suppression of forcing for convective precipitation.

Description: 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.

Description: This study examines the impacts of assimilating Soil Moisture Active Passive (SMAP) L-band brightness temperatures (TBs) on warm season short-range numerical weather prediction (NWP) forecasts. Focusing upon the summer 2015 period over North America, offline assimilation cycles are run with the Canadian Land Data Assimilation System (CaLDAS) to compare the impacts of assimilating SMAP TB versus screen-level observations to analyze soil moisture. The analyzed soil moistures are quantitatively compared against a set of in situ sparse soil moisture networks and a set of SMAP core validation sites. These surface analyses are used to initialize a series of 48-h forecasts where near-surface temperature and precipitation are evaluated against in situ observations. Assimilation of SMAP TBs leads to soil moisture that is markedly improved in terms of correlation and standard deviation of the errors (STDE) compared to the use of screen-level observations. NWP forecasts initialized with SMAP-derived soil moistures exhibit a general dry bias in 2-m dewpoint temperatures (TD2m), while displaying a relative warm bias in 2-m temperatures (TT2m), when compared to those forecasts initialized with soil moistures analyzed with screen-level temperature errors. Largest impacts with SMAP are seen for TD2m, where the use of screen-level observations leads to a daytime wet bias that is reduced with SMAP. The overall drier soil moisture leads to improved precipitation bias scores with SMAP. A notable deterioration in TD2m STDE scores was found in the SMAP experiments during the daytime over the Northern Great Plains. A reduction in the daytime TD2m wet bias was found when the observation errors for the screen-level observations were increased.

Description: A recent study of significant events of atmospheric mass depletion from the Northern Hemisphere (NH) during the extended boreal winter indicated that Southeast Asian pressure surges were an important physical mechanism that acted to channel the atmospheric mass equatorward out of the NH on a rapid time scale. This study builds upon this finding and examines both the direct and indirect roles of Southeast Asian pressure surges for a particular event of dry atmospheric mass depletion from the NH. The focus of this study is on the enhanced interhemispheric interactions and associated Southern Hemisphere (SH) tropical and extratropical responses resulting from the pressure surges. First, this study examines the conservation of dry atmospheric mass (i.e., the relationship between the dry meridional winds and the area-integrated dry air surface pressure) in the NCEP reanalysis for the 25 significant events of dry atmospheric mass depletion from the NH. Results indicate that the NCEP dry meridional winds are able to qualitatively capture the dry atmospheric mass evacuation from the NH. In a quantitative sense there is very good agreement between the wind and pressure data in the extratropics of both hemispheres. A distinct negative or southward bias in the NCEP vertically and zonally integrated dry meridional winds is apparent between 5° and 17.5°N. This southward bias was not present in the ECMWF Re-Analysis. The source of the southward bias in NCEP appears to result from a weaker analyzed ITCZ. The particular case of dry atmospheric mass depletion from the NH examined in detail is associated with an intense pressure surge over Southeast Asia. A significant enhancement of convection in the monsoon trough region of northern Australia occurs roughly 4 days after the peak intensity of the Siberian high. A low-level westerly wind burst develops in response to this enhanced zonal pressure gradient caused by the pressure surge as part of the onset of an active phase of the Australian summer monsoon. This study shows that three prominent anticyclonic circulations intensify in the SH extratropics, stretching from the south Indian Ocean to the South Pacific, beneath regions of upper-tropospheric dry atmospheric mass convergence, originating partly from the monsoon convection outflow. These anticyclonic circulations are regional manifestations of the dry atmospheric mass increase in the SH.