Relationships between Land Surface and Near-Surface Atmospheric Variables in the NCEP North American Regional Reanalysis

2007 ◽  
Vol 8 (6) ◽  
pp. 1184-1203 ◽  
Author(s):  
Yan Luo ◽  
Ernesto H. Berbery ◽  
Kenneth E. Mitchell ◽  
Alan K. Betts
Keyword(s):  
Soil Moisture ◽  
Surface Water ◽  
Land Surface ◽  
Cloud Cover ◽  
Near Surface ◽  
Positive Correlation ◽  
Precipitation Anomalies ◽  
North American Regional Reanalysis ◽  
Regional Reanalysis ◽  
Atmospheric Variables

Abstract This study examines the recently released National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) products over diverse climate regimes to determine the regional relationships between soil moisture and near-surface atmospheric variables. NARR assimilates observed precipitation, as well as near-surface observations of humidity and wind, while seeking a balance of the surface water and energy budgets with a modern land surface model. The results of this study indicate that for most basins (of approximate size of 0.5–1.0 × 106 km2) the NARR surface water budgets have relatively small residuals (about 0.2 mm day−1), and slightly larger residuals (about 0.4 mm day−1) for basins with complex terrain like those in the western United States. Given that the NARR is an assimilation system (especially one that assimilates observed precipitation), the NARR does not include feedbacks between soil moisture and precipitation. Nonetheless, as a diagnostic tool anchored to observations, the NARR does show that the extent of positive correlation between anomalies of soil moisture and anomalies of precipitation in a given region depends on that region’s dryness. The existence of correlations among all variables is a necessary—but not sufficient—condition for land–atmosphere feedbacks to exist, as a region with no correlations would not be expected to have feedbacks. Likewise, a high degree of persistence of soil moisture anomalies in a given basin does not by itself guarantee a positive correlation between anomalies of soil moisture and precipitation. Land surface–atmosphere relationships at monthly time scales are identified by examining the associations between soil moisture and surface and boundary layer variables. Low soil moisture is consistently associated with increased net shortwave radiation and increased outgoing longwave radiation through the effects of less cloud cover and lower atmospheric humidity. No systematic association is revealed between soil moisture and total net surface radiation, as this relation varies substantially between different basins. Low soil moisture is positively correlated with increased sensible heat and lower latent heat (reflected in a smaller evaporative fraction), decreased low-cloud cover, and higher lifting condensation level. The relation between soil moisture anomalies and precipitation anomalies is found to be quite variable between the basins, depending on whether availability of surface water exceeds the available energy for evaporation, or vice versa. Wetter basins, like the Columbia and Ohio, display weak or no correlations between soil moisture anomalies and precipitation anomalies. On the other hand, transitional regions between wet and dry regions, like the central Great Plains, manifest a positive correlation between soil moisture anomalies and precipitation anomalies. These results further the understanding of previous predictability studies (in coupled land–atmosphere prediction models), which indicates that in order for precipitation anomalies to emerge in response to soil moisture anomalies in a given region, it is necessary that the region’s seasonal climate be neither too dry nor too wet.

scholarly journals Representation of Terrestrial Hydrology and Large-Scale Drought of the Continental United States from the North American Regional Reanalysis

2012 ◽  
Vol 13 (3) ◽  
pp. 856-876 ◽  
Author(s):  
Justin Sheffield ◽  
Ben Livneh ◽  
Eric F. Wood
Keyword(s):  
United States ◽  
Soil Moisture ◽  
North American ◽  
Land Surface ◽  
Large Scale ◽  
State Of The Art ◽  
Vic Model ◽  
The North ◽  
North American Regional Reanalysis ◽  
Regional Reanalysis

Abstract The North American Regional Reanalysis (NARR) is a state-of-the-art land–atmosphere reanalysis product that provides improved representation of the terrestrial hydrologic cycle compared to previous global reanalyses, having the potential to provide an enhanced picture of hydrologic extremes such as floods and droughts and their driving mechanisms. This is partly because of the novel assimilation of observed precipitation, state-of-the-art land surface scheme, and higher spatial resolution. NARR is evaluated in terms of the terrestrial water budget and its depiction of drought at monthly to annual time scales against two offline land surface model [Noah v2.7.1 and Variable Infiltration Capacity (VIC)] simulations and observation-based runoff estimates over the continental United States for 1979–2003. An earlier version of the Noah model forms the land component of NARR and so the offline simulation provides an opportunity to diagnose NARR land surface variables independently of atmospheric feedbacks. The VIC model has been calibrated against measured streamflow and so provides a reasonable estimate of large-scale evapotranspiration. Despite similar precipitation, there are large differences in the partitioning of precipitation into evapotranspiration and runoff. Relative to VIC, NARR and Noah annual evapotranspiration is biased high by 28% and 24%, respectively, and the runoff ratios are 50% and 40% lower. This is confirmed by comparison with observation-based runoff estimates from 1130 small, relatively unmanaged basins across the continental United States. The overestimation of evapotranspiration by NARR is largely attributed to the evapotranspiration component of the Noah model, whereas other factors such as atmospheric forcings or biases induced by precipitation assimilation into NARR play only a minor role. A combination of differences in the parameterization of evapotranspiration and in particular low stomatal resistance values in NARR, the seasonality of vegetation characteristics, the near-surface radiation and meteorology, and the representation of soil moisture dynamics, including high infiltration rates and the relative coupling of soil moisture with baseflow in NARR, are responsible for the differences in the water budgets. Large-scale drought as quantified by soil moisture percentiles covaries closely over the continental United States between the three datasets, despite large differences in the seasonal water budgets. However, there are large regional differences, especially in the eastern United States where the VIC model shows higher variability in drought dynamics. This is mostly due to increased frequency of completely dry conditions in NARR that result from differences in soil depth, higher evapotranspiration, early snowmelt, and early peak runoff. In the western United States, differences in the precipitation forcing contribute to large discrepancies between NARR and Noah/VIC simulations in the representation of the early 2000s drought.

scholarly journals Great Plains Hydroclimate Variability: The View from North American Regional Reanalysis

2006 ◽  
Vol 19 (12) ◽  
pp. 3004-3010 ◽  
Author(s):  
Alfredo Ruiz-Barradas ◽  
Sumant Nigam
Keyword(s):  
Interannual Variability ◽  
Great Plains ◽  
North American ◽  
Land Surface ◽  
Warm Season ◽  
Surface Model ◽  
Near Surface ◽  
Hydroclimate Variability ◽  
North American Regional Reanalysis ◽  
Regional Reanalysis

Abstract Interannual variability of warm-season rainfall over the Great Plains is analyzed using the recently released North American Regional Reanalysis (NARR). The new dataset differs from its global counterparts in the additional assimilation of precipitation and radiances. This along with the use of a more comprehensive land surface model in generation of NARR offers the prospect of obtaining improved estimates of surface hydrologic and near-surface meteorological fields. NARR’s representation of hydroclimate is used to weigh in on the authors’ recent finding of the dominance of large-scale moisture flux convergence over evaporation in accounting for Great Plains precipitation variations. Evaporation estimates are notoriously uncertain and, while the NARR ones are not assured to be realistic, they are more constrained than those diagnosed before from inline and offline assessments. NARR’s portrayal of warm-season hydroclimate variability corroborates the importance of remote water sources in generation of Great Plains precipitation variability and supports the authors’ claim that some state-of-the-art atmosphere/land surface models vigorously recycle precipitation, erroneously, at least in context of Great Plains interannual variability. These very models have been key to recent claims of strong coupling between soil moisture and precipitation.

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime

2021 ◽  
Author(s):  
Stefano Materia ◽  
Constantin Ardilouze ◽  
Chloé Prodhomme ◽  
Markus G. Donat ◽  
Marianna Benassi ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Heat Waves ◽  
Climate Regime ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Surface Temperatures ◽  
The Mediterranean

AbstractLand surface and atmosphere are interlocked by the hydrological and energy cycles and the effects of soil water-air coupling can modulate near-surface temperatures. In this work, three paired experiments were designed to evaluate impacts of different soil moisture initial and boundary conditions on summer temperatures in the Mediterranean transitional climate regime region. In this area, evapotranspiration is not limited by solar radiation, rather by soil moisture, which therefore controls the boundary layer variability. Extremely dry, extremely wet and averagely humid ground conditions are imposed to two global climate models at the beginning of the warm and dry season. Then, sensitivity experiments, where atmosphere is alternatively interactive with and forced by land surface, are launched. The initial soil state largely affects summer near-surface temperatures: dry soils contribute to warm the lower atmosphere and exacerbate heat extremes, while wet terrains suppress thermal peaks, and both effects last for several months. Land-atmosphere coupling proves to be a fundamental ingredient to modulate the boundary layer state, through the partition between latent and sensible heat fluxes. In the coupled runs, early season heat waves are sustained by interactive dry soils, which respond to hot weather conditions with increased evaporative demand, resulting in longer-lasting extreme temperatures. On the other hand, when wet conditions are prescribed across the season, the occurrence of hot days is suppressed. The land surface prescribed by climatological precipitation forcing causes a temperature drop throughout the months, due to sustained evaporation of surface soil water. Results have implications for seasonal forecasts on both rain-fed and irrigated continental regions in transitional climate zones.

scholarly journals Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

2012 ◽  
Vol 16 (8) ◽  
pp. 2547-2565 ◽  
Author(s):  
G. Tang ◽  
P. J. Bartlein
Keyword(s):  
Soil Moisture ◽  
Surface Water ◽  
Surface Runoff ◽  
Land Surface ◽  
Surface Hydrology ◽  
Large Spatial Scale ◽  
Land Surface Water ◽  
Land Surface Hydrology ◽  
Water Balances ◽  
Global Vegetation

Abstract. Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM) simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i) modify a DGVM for simulating land surface water balances; (ii) evaluate the modified model in simulating actual evapotranspiration (ET), soil moisture, and surface runoff at regional or watershed scales; and (iii) gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH) model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ) DGVM. To evaluate the model we ran LH using historical (1981–2006) climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981–2006 (R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficient > 0.52). The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences < 15%) with observed values for these rivers. Compared to a degree-day method for snowmelt computation, the addition of the solar radiation effect on snowmelt enabled LH to better simulate monthly stream flow in winter and early spring for rivers located at mid-to-high latitudes. In addition, LH-modeled monthly soil moisture for the state of Illinois (US) agreed well (R2 = 0.79, p < 0.01) with observed data for the years 1984–2001. Overall, this study justifies both the feasibility of incorporating satellite-based land covers into a DGVM and the reliability of LH to simulate land-surface water balances. To better estimate surface/river runoff at mid-to-high latitudes, we recommended that LPJ-DGVM considers the effects of solar radiation on snowmelt.

scholarly journals Assessing the Evolution of Soil Moisture and Vegetation Conditions during a Flash Drought–Flash Recovery Sequence over the South-Central United States

2019 ◽  
Vol 20 (3) ◽  
pp. 549-562 ◽  
Author(s):  
Jason A. Otkin ◽  
Yafang Zhong ◽  
Eric D. Hunt ◽  
Jeff Basara ◽  
Mark Svoboda ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Heavy Rainfall ◽  
Recovery Period ◽  
The South ◽  
Recovery Sequence ◽  
Near Surface ◽  
South Central ◽  
Central United States

Abstract This study examines the evolution of soil moisture, evapotranspiration, vegetation, and atmospheric conditions during an unusual flash drought–flash recovery sequence that occurred across the south-central United States during 2015. This event was characterized by a period of rapid drought intensification (flash drought) during late summer that was terminated by heavy rainfall at the end of October that eliminated the extreme drought conditions over a 2-week period (flash recovery). A detailed analysis was performed using time series of environmental variables derived from meteorological, remote sensing, and land surface modeling datasets. Though the analysis revealed a similar progression of cascading effects in each region, characteristics of the flash drought such as its onset time, rate of intensification, and vegetation impacts differed between regions due to variations in the antecedent conditions and the atmospheric anomalies during its growth. Overall, flash drought signals initially appeared in the near-surface soil moisture, followed closely by reductions in evapotranspiration. Total column soil moisture deficits took longer to develop, especially in the western part of the region where heavy rainfall during the spring and early summer led to large moisture surpluses. Large differences were noted in how land surface models in the North American Land Data Assimilation System depicted soil moisture evolution during the flash drought; however, the models were more similar in their assessment of conditions during the flash recovery period. This study illustrates the need to use multiple datasets to track the evolution and impacts of rapidly evolving flash drought and flash recovery events.

scholarly journals Contribution of soil moisture feedback to hydroclimatic variability

2010 ◽  
Vol 14 (3) ◽  
pp. 505-520 ◽  
Author(s):  
N. Y. Krakauer ◽  
B. I. Cook ◽  
M. J. Puma
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cloud Cover ◽  
Temperature Fluctuations ◽  
Substantial Impact ◽  
Empirical Measures ◽  
Spatial And Temporal Scales ◽  
Moisture Variation ◽  
The Impact ◽  
Soil Moisture Feedback

Abstract. While a variety of model experiments and analyses of observations have explored the impact of soil moisture variation on climate, it is not yet clear how large or detectable soil moisture feedback is across spatial and temporal scales. Here, we study the impact of dynamic versus climatological soil moisture in the GISS GCM ModelE (with prescribed sea-surface temperatures) on the variance and on the spatial and temporal correlation scale of hydrologically relevant climate variables (evaporation, precipitation, temperature, cloud cover) over the land surface. We also confirm that synoptic variations in soil moisture have a substantial impact on the mean climate state, because of the nonlinearity of the dependence of evapotranspiration on soil moisture. We find that including dynamic soil moisture increases the interannual variability of seasonal (summer and fall) and annual temperature, precipitation, and cloudiness. Dynamic soil moisture tends to decrease the correlation length scale of seasonal (warm-season) to annual land temperature fluctuations and increase that of precipitation. Dynamic soil moisture increases the persistence of temperature anomalies from spring to summer and from summer to fall, and makes the correlation between land precipitation and temperature fluctuations substantially more negative. Global observation sets that allow determination of the spacetime correlation of variables such as temperature, precipitation, and cloud cover could provide empirical measures of the strength of soil moisture feedback, given that the feedback strength varies widely among models.

scholarly journals Influence of Precipitation Assimilation on a Regional Climate Model’s Surface Water and Energy Budgets

2007 ◽  
Vol 8 (4) ◽  
pp. 642-664 ◽  
Author(s):  
Ana M. B. Nunes ◽  
John O. Roads
Keyword(s):  
Surface Water ◽  
Energy Budget ◽  
Prediction Models ◽  
Regional Climate ◽  
Longwave Radiation ◽  
Specific Humidity ◽  
Climate Simulation ◽  
Energy Budgets ◽  
Near Surface ◽  
Regional Reanalysis

Abstract Initialization of the moisture profiles has been used to overcome the imbalance between analysis schemes and prediction models that generates the so-called spinup problem seen in the hydrological fields. Here precipitation assimilation through moisture adjustment has been proposed as a technique to reduce this problem in regional climate simulations by adjusting the specific humidity according to 3-hourly North American Regional Reanalysis rain rates during two simulated years: 1988 and 1993. A control regional simulation provided the initial condition fields for both simulations. The precipitation assimilation simulation was then compared to the control regional climate simulation, reanalyses, and observations to determine whether assimilation of precipitation had a positive influence on modeled surface water and energy budget terms. In general, rainfall assimilation improved the regional model surface water and energy budget terms over the conterminous United States. Precipitation and runoff correlated better than the control and the global reanalysis fields to the regional reanalysis and available observations. Upward shortwave and downward short- and longwave radiation fluxes had regional seasonal cycles closer to the observed values than the control, and the near-surface temperature anomalies were also improved.

Recurrent Supersynoptic Evolution of the Great Plains Low-Level Jet

2011 ◽  
Vol 24 (2) ◽  
pp. 575-582 ◽  
Author(s):  
Scott J. Weaver ◽  
Sumant Nigam
Keyword(s):  
Great Plains ◽  
Large Scale ◽  
Large Scale Circulation ◽  
Low Level ◽  
The North ◽  
Analysis Strategy ◽  
Precipitation Anomalies ◽  
North American Regional Reanalysis ◽  
Low Level Jet ◽  
Regional Reanalysis

Abstract The evolution of supersynoptic (i.e., pentad) Great Plains low-level jet (GPLLJ) variability, its precipitation impacts, and large-scale circulation context are analyzed in the North American Regional Reanalysis (NARR)—a high-resolution precipitation-assimilating dataset—and the NCEP–NCAR reanalysis. The analysis strategy leans on the extended EOF technique, which targets both spatial and temporal recurrence of a variability episode. Pentad GPLLJ variability structures are found to be spatially similar to those in the monthly analysis. The temporal evolution of the supersynoptic GPLLJ-induced precipitation anomalies reveal interesting lead and lag relationships highlighted by GPLLJ variability-leading precipitation anomalies. Interestingly, similar temporal phasing of the GPLLJ and precipitation anomalies were operative during the 1993 (1988) floods (drought) over the Great Plains, indicating the importance of these submonthly GPLLJ variability modes in the instigation of extreme hydroclimatic episodes. The northward-shifted (dry) GPLLJ variability mode is linked to large-scale circulation variations emanating from remote regions that are modified by interaction with the Rocky Mountains, suggesting that the supersynoptic GPLLJ fluctuations may have their origin in orographic modulation of baroclinic development.

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