scholarly journals Relative Roles of Large-Scale Orography and Land Surface Processes in the Global Hydroclimate. Part II: Impacts on Hydroclimate over Eurasia

2006 ◽  
Vol 7 (4) ◽  
pp. 642-659 ◽  
Author(s):  
Kazuyuki Saito ◽  
Tetsuzo Yasunari ◽  
Kumiko Takata
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Warm Season ◽  
Summer Precipitation ◽  
Winter Precipitation ◽  
Bare Rock ◽  
Surface Scheme ◽  
Water Holding ◽  
East West ◽  
Higher Sensitivity

Abstract A series of simplistic simulations from an AGCM coupled to a simple land surface scheme and water vapor tracers was performed to explore the relative roles of basic factors in land surface conditions, with regard to the seasonal evolution of the hydroclimate over Eurasia. Large-scale orography in Asia and vegetation (further decomposed to soil and vegetation skin) were evaluated, with orography represented in the model by surface altitude, soil represented by water-holding capacity, and vegetation skin represented by surface albedo and roughness. The percentage of global annual precipitation over land (occupying 25.6% of the total surface) was 14.8%, 15.0%, and 21.7% for the mountainless “bare rock” (i.e., vegetationless) surface, and the bare-rock and vegetated surface, respectively. The result for evaporation was 8.9%, 9.0%, and 16.2%, respectively, showing higher sensitivity to the land surface changes than precipitation. The orography and vegetation (i.e., soil and vegetation skin) showed different impacts on Eurasian hydroclimate on the seasonal and regional scales. Thermodynamical forcings to the atmosphere increased over the continent with the inclusion of both. Large-scale orography in Asia exerted east–west contrast in the surface energy exchange in summer in eastern Eurasia. An increase in extratropical winter precipitation with mountains was also noticed because of the atmospheric vapor transport changes. Impact of soil and vegetation skin was clearly found in the warm season in the extratropics; soil impacts extratropical summer precipitation due to enhanced recycling of water and the resultant increased water supply.

scholarly journals Spatial variability and its scale dependency of observed and modeled soil moisture over different climate regions

2013 ◽  
Vol 17 (3) ◽  
pp. 1177-1188 ◽  
Author(s):  
B. Li ◽  
M. Rodell
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Land Surface ◽  
Large Scale ◽  
Warm Season ◽  
Scale Dependency ◽  
Convex Shape ◽  
Spatial Moments ◽  
Environmental Controls

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

scholarly journals Drought Relief and Reversal over North America from 1500 to 2016

2021 ◽  
Vol 25 (1) ◽  
pp. 94-107
Author(s):  
M. C. A. Torbenson ◽  
D. W. Stahle ◽  
I. M. Howard ◽  
D. J. Burnette ◽  
D. Griffin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Land Surface ◽  
Large Scale ◽  
Temporal Stability ◽  
Warm Season ◽  
Cool Season ◽  
Continental Scale ◽  
The North ◽  
Drought Relief

Abstract Season-to-season persistence of soil moisture drought varies across North America. Such interseasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool-season (December–April) and warm-season (May–July) precipitation series and offers new insights on the intra-annual changes in drought for up to 2000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and 3-month-long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool-season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the twentieth century and are used to estimate relief and reversals for the most skillfully reconstructed past 500 years. Subcontinental-to-continental-scale reversals of cool-season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the mid-Atlantic region and the U.S. Southwest. Drought relief at the continental scale may arise in part from macroscale ocean–atmosphere processes, whereas the smaller-scale regional reversals may reflect land surface feedbacks and stochastic variability.

scholarly journals An application of a large scale conceptual hydrological model over the Elbe region

1999 ◽  
Vol 3 (3) ◽  
pp. 363-374 ◽  
Author(s):  
M. Lobmeyr ◽  
D. Lohmann ◽  
C. Ruhe
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Sunshine Duration ◽  
Regional Scale ◽  
Scale Model ◽  
Surface Parameterization ◽  
Extreme Flood ◽  
Daily Sunshine ◽  
Surface Scheme ◽  
The Baltic

Abstract. This paper investigates the ability of the VIC-2L model coupled to a routing model to reproduce streamflow in the catchment of the lower Elbe River, Germany. The VIC-2L model, a hydrologically-based land surface scheme (LSS) which has been tested extensively in the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS), is put up on the rotated grid of 1/6 degree of the atmospheric regional scale model (REMO) used in the Baltic Sea Experiment (BALTEX). For a 10 year period, the VIC-2L model is forced in daily time steps with measured daily means of precipitation, air temperature, pressure, wind speed, air humidity and daily sunshine duration. VIC-2L model output of surface runoff and baseflow is used as input for the routing model, which transforms modelled runoff into streamflow, which is compared to measured streamflow at selected gauge stations. The water balance of the basin is investigated and the model results on daily, monthly and annual time scales are discussed. Discrepancies appear in time periods where snow and ice processes are important. Extreme flood events are analyzed in more dital. The influence of calibration with respect to runoff is examined.

Dryness over the U.S. Southwest, a Springboard for Cold Season Pacific SST to Influence Warm Season Drought over the U.S. Great Plains

2021 ◽  
Vol 22 (1) ◽  
pp. 63-76
Author(s):  
Yizhou Zhuang ◽  
Amir Erfanian ◽  
Rong Fu
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Rainfall Variability ◽  
Warm Season ◽  
Summer Precipitation ◽  
Cold Season ◽  
Early Summer ◽  
Moisture Condition ◽  
Moisture Advection ◽  
The U.S

AbstractAlthough the influence of sea surface temperature (SST) forcing and large-scale teleconnection on summer droughts over the U.S. Great Plains has been suggested for decades, the underlying mechanisms are still not fully understood. Here we show a significant correlation between low-level moisture condition over the U.S. Southwest in spring and rainfall variability over the Great Plains in summer. Such a connection is due to the strong influence of the Southwest dryness on the zonal moisture advection to the Great Plains from spring to summer. This advection is an important contributor for the moisture deficit during spring to early summer, and so can initiate warm season drought over the Great Plains. In other words, the well-documented influence of cold season Pacific SST on the Southwest rainfall in spring, and the influence of the latter on the zonal moisture advection to the Great Plains from spring to summer, allows the Pacific climate variability in winter and spring to explain over 35% of the variance of the summer precipitation over the Great Plains, more than that can be explained by the previous documented west Pacific–North America (WPNA) teleconnection forced by tropical Pacific SST in early summer. Thus, this remote land surface feedback due to the Southwest dryness can potentially improve the predictability of summer precipitation and drought onsets over the Great Plains.

scholarly journals The Role of an Advanced Land Model in Seasonal Dynamical Downscaling for Crop Model Application

2006 ◽  
Vol 45 (5) ◽  
pp. 686-701 ◽  
Author(s):  
D. W. Shin ◽  
J. G. Bellow ◽  
T. E. LaRow ◽  
S. Cocke ◽  
James J. O'Brien
Keyword(s):  
Land Surface ◽  
Crop Yields ◽  
Dynamical Downscaling ◽  
Warm Season ◽  
Regional Model ◽  
Crop Model ◽  
Shortwave Radiation ◽  
Seasonal Climate ◽  
Community Land Model ◽  
Surface Scheme

Abstract An advanced land model [the National Center for Atmospheric Research (NCAR) Community Land Model, version 2 (CLM2)] is coupled to the Florida State University (FSU) regional spectral model to improve seasonal surface climate outlooks at very high spatial and temporal resolution and to examine its potential for crop yield estimation. The regional model domain is over the southeast United States and is run at 20-km resolution, roughly resolving the county level. Warm-season (March–September) simulations from the regional model coupled to the CLM2 are compared with those from the model with a simple land surface scheme (i.e., the original FSU model). In this comparison, two convective schemes are also used to evaluate their roles in simulating seasonal climate, primarily for rainfall. It is shown that the inclusion of the CLM2 produces consistently better seasonal climate scenarios of surface maximum and minimum temperatures, precipitation, and shortwave radiation, and hence provides superior inputs to a site-based crop model to simulate crop yields. The FSU regional model with the CLM2 exhibits some capability in the simulation of peanut (Arachis hypogaea L.) yields, depending upon the convective scheme employed and the site selected.

scholarly journals Representation of Water Table Dynamics in a Land Surface Scheme. Part II: Subgrid Variability

2005 ◽  
Vol 18 (12) ◽  
pp. 1881-1901 ◽  
Author(s):  
Pat J-F. Yeh ◽  
Elfatih A. B. Eltahir
Keyword(s):  
Groundwater Recharge ◽  
Water Table ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Land Surface Scheme ◽  
Groundwater Runoff ◽  
Aquifer Storage ◽  
Mosaic Approach ◽  
Surface Scheme

Abstract A lumped unconfined aquifer model has been developed and interactively coupled to a land surface scheme in a companion paper. Here, the issue of the representation of subgrid variability of water table depths (WTDs) is addressed. A statistical–dynamical (SD) approach is used to account for the effects of the unresolved subgrid variability of WTD in the grid-scale groundwater runoff. The dynamic probability distribution function (PDF) of WTD is specified as a two-parameter gamma distribution based on observations. The grid-scale groundwater rating curve (i.e., aquifer storage–discharge relationship) is derived statistically by integrating a point groundwater runoff model with respect to the PDF of WTD. Next, a mosaic approach is utilized to account for the effects of subgrid variability of WTD in the grid-scale groundwater recharge. A grid cell is categorized into different subgrids based on the PDF of WTD. The grid-scale hydrologic fluxes are computed by averaging all of the subgrid fluxes weighted by their fractions. This new methodology combines the strengths of the SD approach and the mosaic approach. The results of model testing in Illinois from 1984 to 1994 indicate that the simulated hydrologic variables (soil saturation and WTD) and fluxes (evaporation, runoff, and groundwater recharge) agree well with the observations. Because of the paucity of the large-scale observations on WTD, the development of a practical parameter estimation procedure is indispensable before the global implementation of the developed scheme of water table dynamics in climate models.

scholarly journals Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements

2021 ◽  
Vol 14 (11) ◽  
pp. 7007-7023
Author(s):  
Xinyan Li ◽  
Yuanjian Yang ◽  
Jiaqin Mi ◽  
Xueyan Bi ◽  
You Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rural Areas ◽  
Land Surface ◽  
Large Scale ◽  
Quantitative Estimation ◽  
Summer Precipitation ◽  
Bias Score ◽  
Surface Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract. Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.

scholarly journals Predictability of Midsummer Great Plains Low-Level Jet and Associated Precipitation

2020 ◽  
Vol 35 (1) ◽  
pp. 215-235 ◽  
Author(s):  
Kelsey M. Malloy ◽  
Ben P. Kirtman
Keyword(s):  
Great Plains ◽  
North American ◽  
Large Scale ◽  
Linear Regression Analysis ◽  
Intraseasonal Variability ◽  
Warm Season ◽  
Summer Precipitation ◽  
Climate System Model ◽  
Low Level ◽  
Low Level Jet

Abstract Warm-season precipitation in the U.S. “Corn Belt,” the Great Plains, and the Midwest greatly influences agricultural production and is subject to high interannual and intraseasonal variability. Unfortunately, current seasonal and subseasonal forecasts for summer precipitation have relatively low skill. Therefore, there are ongoing efforts to understand hydroclimate variability targeted at improving predictions, particularly through its primary transporter of moisture: the Great Plains low-level jet (LLJ). This study uses the Community Climate System Model, version 4 (CCSM4), July forecasts, made as part of the North American Multi-Model Ensemble (NMME), to assess skill in reproducing the monthly Great Plains LLJ and associated precipitation. Generally, the CCSM4 forecasts capture the climatological jet but have problems representing the observed variability beyond two weeks. In addition, there are predictors associated with the large-scale variability identified through linear regression analysis, shifts in kernel density estimators, and case study analysis that suggest potential for improving confidence in forecasts. In this study, a strengthened Caribbean LLJ, negative Pacific–North American (PNA) teleconnection, El Niño, and a negative Atlantic multidecadal oscillation each have a relatively strong and consistent relationship with a strengthened Great Plains LLJ. The circulation predictors, the Caribbean LLJ and PNA, present the greatest “forecast of opportunity” for considering and assigning confidence in monthly forecasts.

scholarly journals Soil Surface Energy and Water Budgets during a Monsoon Season in Korea

2009 ◽  
Vol 10 (6) ◽  
pp. 1379-1396 ◽  
Author(s):  
Claudio Cassardo ◽  
Seon Ki Park ◽  
Bindu Malla Thakuri ◽  
Daniela Priolo ◽  
Ying Zhang
Keyword(s):  
Heat Flux ◽  
Surface Layer ◽  
Latent Heat ◽  
Land Surface ◽  
Process Model ◽  
Large Scale ◽  
Sensible Heat Flux ◽  
Land Surface Scheme ◽  
Soil Temperatures ◽  
Surface Scheme

Abstract In this study, attention has been focused on the climatology of some variables linked to the turbulent exchanges of heat and water vapor in the surface layer during a summer monsoon in Korea. In particular, the turbulent fluxes of sensible and latent heat, the hydrologic budget, and the soil temperatures and moistures have been analyzed. At large scale, because the measurements of those data are not only fragmentary and exiguously available but also infeasible for the execution of climatologic analyses, the outputs of a land surface scheme have been used as surrogate of observations to analyze surface layer processes [this idea is based on the methodology Climatology of Parameters at the Surface (CLIPS)] in the Korean monsoonal climate. Analyses have been made for the summer of 2005. As a land surface scheme, the land surface process model (LSPM) developed at the University of Torino, Italy, has been employed, along with the data collected from 635 Korean meteorological stations. The LSPM predictions showed good agreement with selected observations of soil temperature. Major results show that, during the rainfall season, soil moisture in the first tenths of centimeters frequently exceeds the field capacity, whereas most of the rainfall is “lost” as surface runoff. Evapotranspiration is the dominant component of the energy budget, sometimes even exceeding net radiation, especially during the short periods between the precipitation events; in these periods, daily mean soil temperatures are about 28°C or even more. The Gyeonggi-do region, the metropolitan area surrounding Seoul, shows some particularities when compared with the neighboring regions: solar radiation and precipitations are lower, causing high values of sensible heat flux and soil temperatures, and lower values of latent heat flux and soil moistures.

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