Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.
Land surface processes are vital to the performance of regional climate models in dynamic downscaling application. In this study, we investigate the sensitivity of the simulation by using the weather research and forecasting (WRF) model at 10-km resolution to the land surface schemes over Central Asia. The WRF model was run for 19 summers from 2000 to 2018 configured with four different land surface schemes including CLM4, Noah-MP, Pleim-Xiu and SSiB, hereafter referred as Exp-CLM4, Exp-Noah-MP, Exp-PX and Exp-SSiB respectively. The initial and boundary conditions for the WRF model simulations were provided by the National Centers for Environmental Prediction Final (NCEP-FNL) Operational Global Analysis data. The ERA-Interim reanalysis (ERAI), the GHCN-CAMS and the CRU gridded data were used to comprehensively evaluate the WRF simulations. Compared with the reanalysis and observational data, the WRF model can reasonably reproduce the spatial patterns of summer mean 2-m temperature, precipitation, and large- scale atmospheric circulation. The simulations, however, are sensitive to the option of land surface scheme. The performance of Exp-CLM4 and Exp-SSiB are better than that of Exp-Noah-MP and Exp-PX assessed by Multivariable Integrated Evaluation (MVIE) method. To comprehensively understand the dynamic and physical mechanisms for the WRF model’s sensitivity to land surface schemes, the differences in the surface energy balance between Ave-CLM4-SSiB (the ensemble average of Exp-CLM4 and Exp-SSiB) and Ave-NoanMP-PX (the ensemble average of Exp-Noah-MP and Exp-PX) are analyzed in detail. The results demonstrate that the sensible and latent heat fluxes are respectively lower by 30.42 W·m−2and higher by 14.86 W·m−2in Ave-CLM4-SSiB than that in Ave-NoahMP-PX. As a result, large differences in geopotential height occur over the simulation domain. The simulated wind fields are subsequently influenced by the geostrophic adjustment process, thus the simulations of 2-m temperature, surface skin temperature and precipitation are respectively lower by about 2.08 ℃, 2.23 ℃ and 18.56 mm·month−1in Ave-CLM4-SSiB than that in Ave-NoahMP-PX over Central Asia continent.
more » « less- Award ID(s):
- 1849654
- NSF-PAR ID:
- 10273906
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Climate Dynamics
- Volume:
- 57
- Issue:
- 7-8
- ISSN:
- 0930-7575
- Page Range / eLocation ID:
- p. 2249-2268
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract This study evaluates the impact of land surface models (LSMs) and urban heterogeneity [using local climate zones (LCZs)] on air temperature simulated by the Weather Research and Forecasting model (WRF) during a regional extreme event. We simulated the 2017 heatwave over Europe considering four scenarios, using WRF coupled with two LSMs (i.e., Noah and Noah‐MP) with default land use/land cover (LULC) and with LCZs from the World Urban Database and Access Portal Tools (WUDAPT). The results showed that implementing the LCZs significantly improves the WRF simulations of the daily temperature regardless of the LSMs. Implementing the LCZs altered the surface energy balance partitioning in the simulations (i.e., the sensible heat flux was reduced and latent heat flux was increased) primarily due to a higher vegetation feedback in the LCZs. The changes in the surface flux translated into an increase in the simulated 2‐m relative humidity and 10‐m wind speed as well as changed air temperature within cities section and generated a temperature gradient that affected the temperatures beyond the urban regions. Despite these changes, the factor separation analysis indicated that the impact of LSM selection was more significant than the inclusion of LCZs. Interestingly, the lowest bias in temperature simulations was achieved when WRF was coupled with the Noah as the LSM and used WUDAPT as the LULC/urban representation.
-
more » « less
Abstract We present a new ensemble of 36 numerical experiments aimed at comprehensively gauging the sensitivity of nested large-eddy simulations (LES) driven by large-scale dynamics. Specifically, we explore 36 multiscale configurations of the Weather Research and Forecasting (WRF) Model to simulate the boundary layer flow over the complex topography at the Perdigão field site, with five nested domains discretized at horizontal resolutions ranging from 11.25 km to 30 m. Each ensemble member has a unique combination of the following input factors: (i) large-scale initial and boundary conditions, (ii) subgrid turbulence modeling in the
gray zone of turbulence, (iii) subgrid-scale (SGS) models in LES, and (iv) topography and land-cover datasets. We probe their relative importance for LES calculations of velocity, temperature, and moisture fields. Variance decomposition analysis unravels large sensitivities to topography and land-use datasets and very weak sensitivity to the LES SGS model. Discrepancies within ensemble members can be as large as 2.5 m s−1for the time-averaged near-surface wind speed on the ridge and as large as 10 m s−1without time averaging. At specific time points, a large fraction of this sensitivity can be explained by the different turbulence models in the gray zone domains. We implement a horizontal momentum and moisture budget routine in WRF to further elucidate the mechanisms behind the observed sensitivity, paving the way for an increased understanding of the tangible effects of the gray zone of turbulence problem.Significance Statement Several science and engineering applications, including wind turbine siting and operations, weather prediction, and downscaling of climate projections, call for high-resolution numerical simulations of the lowest part of the atmosphere. Recent studies have highlighted that such high-resolution simulations, coupled with large-scale models, are challenging and require several important assumptions. With a new set of numerical experiments, we evaluate and compare the significance of different assumptions and outstanding challenges in multiscale modeling (i.e., coupling large-scale models and high-resolution atmospheric simulations). The ultimate goal of this analysis is to put each individual assumption into the wider perspective of a realistic problem and quantify its relative importance compared to other important modeling choices.
-
more » « less
Abstract Since the 1970s, land cover in central Argentina has shifted away from perennial crops and grasses toward annual crops, largely soy. In this study, we use observations and modeling to understand how this shift in land use has affected the sub‐surface, surface, and atmospheric fluxes of moisture and energy in a flat agricultural area. We analyze the flux tower data from a paired site at Marcos Juarez in central Argentina during the period of the RELAMPAGO field campaign (2018–2019). When compared to perennial alfalfa, the observations over soy show lower evapotranspiration (ET) and specific humidity, higher sensible heat, higher outgoing shortwave radiation, and soil temperature. Water table (WT) depth is shallower below the soy than the alfalfa sites. To better understand the long‐term temporal behavior from 1970s to present, the Budyko framework is used to show that the trends in ET cannot be explained by climate variables alone. We then use the Noah‐MP land surface model calibrated at both soy and alfalfa sites. Long‐term simulations of the calibrated model suggests that ∼95% of precipitation is evaporated in the alfalfa site with negligible recharge and runoff. Contrarily in the case of soy, ET is about 68% of precipitation, leaving nearly 28% for recharge and 4% for runoff. Observed increases in streamflow and decreases in WT depth over time are likely linked to shifts in land cover. Furthermore, the partitioning of net radiation shifts from latent heat to sensible heat resulting in a 250% increase in Bowen ratio (from 0.2 to 0.7).
-
more » « less
Abstract Radially outward-propagating, diurnal pulses in tropical cyclones (TCs) are associated with TC intensity and structural changes. The pulses are observed to feature either cloud-top cooling or warming, so-called cooling pulses (CPs) or warming pulses (WPs), respectively, with CPs posing a greater risk for hazardous weather because they often assume characteristics of tropical squall lines. The current study evaluates the characteristics and origins of simulated CPs using various convection-permitting Weather Research and Forecasting (WRF) Model simulations of Hurricane Dorian (2019), which featured several CPs and WPs over the tropical Atlantic Ocean. CP evolution is tested against choice of microphysics parameterization, whereby the Thompson and Morrison schemes present distinct mechanisms for CP creation and propagation. Specifically, the Thompson CP is convectively coupled and propagates outward with a rainband within 100–300 km of the storm center. The Morrison CP is restricted to the cirrus canopy and propagates radially outward in the upper-level outflow layer, unassociated with any rainband, within 200–600 km of the storm center. The Thompson simulation better represents the observations of this particular event, but it is speculated that CPs in nature can resemble characteristics from either MP scheme. It is, therefore, necessary to evaluate pulses beyond just brightness temperature (e.g., reflectivity, rain rate), especially within simulations where full fields are available.
Significance Statement Tropical cyclone size and structure are influenced by the time of day. Identifying and predicting such characteristics is critical for evaluating hazardous weather risk of storms close to land. While satellite observations are valuable for recognizing daily fluctuations of tropical cyclone clouds as seen from space, they do not reliably capture what occurs at the surface. To investigate the relationship between upper-level cloud oscillations and rainbands, this study analyzes simulations of a major hurricane along the coast of Florida. The results show that rainbands are not always tied to changes in cloud tops, suggesting multiple pathways toward the daily oscillation of upper-level tropical cyclone clouds.
