ORCID Profile
0000-0003-3030-0330
Current Organisation
The University of Texas at Austin
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Publisher: American Meteorological Society
Date: 06-04-2012
DOI: 10.1175/JCLI-D-12-00005.1
Abstract: An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model (WRF) version 3.3 embedded in the National Center for Atmospheric Research's (NCAR's) Community Atmosphere Model (CAM). The GCM climatological means and the litudes of interannual variations are adjusted based on the National Centers for Environmental Prediction (NCEP)–NCAR global reanalysis products (NNRP) before using them to drive WRF. In this study, the WRF downscaling experiments are identical except the initial and lateral boundary conditions derived from the NNRP, original GCM output, and bias-corrected GCM output, respectively. The analysis finds that the IDD greatly improves the downscaled climate in both climatological means and extreme events relative to the traditional dynamical downscaling approach (TDD). The errors of downscaled climatological mean air temperature, geopotential height, wind vector, moisture, and precipitation are greatly reduced when the GCM bias corrections are applied. In the meantime, IDD also improves the downscaled extreme events characterized by the reduced errors in 2-yr return levels of surface air temperature and precipitation. In comparison with TDD, IDD is also able to produce a more realistic probability distribution in summer daily maximum temperature over the central U.S.–Canada region as well as in summer and winter daily precipitation over the middle and eastern United States.
Publisher: Elsevier BV
Date: 09-2018
Publisher: Springer Science and Business Media LLC
Date: 16-01-2023
Publisher: MDPI AG
Date: 19-10-2020
DOI: 10.3390/RS12203434
Abstract: This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.
Publisher: American Geophysical Union (AGU)
Date: 10-05-2008
DOI: 10.1029/2007JD009216
Publisher: American Geophysical Union (AGU)
Date: 22-02-2014
DOI: 10.1002/2013GL058931
Publisher: American Geophysical Union (AGU)
Date: 03-02-2010
DOI: 10.1029/2009JD012035
Publisher: American Geophysical Union (AGU)
Date: 08-2020
DOI: 10.1029/2020EF001552
Publisher: Elsevier BV
Date: 10-2018
Publisher: Copernicus GmbH
Date: 04-07-2023
DOI: 10.5194/ESSD-15-2755-2023
Abstract: Abstract. Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS-2) four-LSM ensemble, this paper describes a dataset simulated from an ensemble of 48 physics configurations of the Noah LSM with multi-physics options (Noah-MP). The 48 Noah-MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The dataset spans from 1980 to 2015 over the conterminous United States (CONUS) at a monthly temporal resolution and a 1/8∘ spatial resolution. The dataset variables include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The dataset is available at 0.5281/zenodo.7109816 (Zheng et al., 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS estimations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilitates the identification of model deficiencies. The parameterizations of shallow snow, spatially varying groundwater dynamics, and near-surface atmospheric turbulence should be improved in future model versions.
Publisher: Elsevier BV
Date: 07-2003
Publisher: American Geophysical Union (AGU)
Date: 20-12-2014
DOI: 10.1002/2014JD022113
Publisher: American Meteorological Society
Date: 10-2011
Abstract: The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the litude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.
Publisher: Copernicus GmbH
Date: 22-07-2019
Abstract: Abstract. The leaf area index (LAI) is a crucial parameter for understanding the exchanges of mass and energy between terrestrial ecosystems and the atmosphere. In this study, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model with explicit carbon and nitrogen components (CLM4CN) by assimilating Global Land Surface Satellite (GLASS) LAI data. Within this framework, four sequential assimilation algorithms, including the kernel filter (KF), the ensemble Kalman filter (EnKF), the ensemble adjust Kalman filter (EAKF), and the particle filter (PF), are thoroughly analyzed and compared. The results show that assimilating GLASS LAI into the CLM4CN is an effective method for improving model performance. In detail, the assimilation accuracies of the EnKF and EAKF algorithms are better than those of the KF and PF algorithm. From the perspective of the average and RMSD, the PF algorithm performs worse than the EAKF and EnKF algorithms because of the gradually reduced acceptance of observations with assimilation steps. In other words, the contribution of the observations to the posterior probability during the assimilation process is reduced. The EAKF algorithm is the best method because the matrix is adjusted at each time step during the assimilation procedure. If all the observations are accepted, the analyzed LAI seem to be better than that when some observations are rejected, especially in low-latitude regions.
Publisher: Wiley
Date: 08-04-2022
Publisher: American Geophysical Union (AGU)
Date: 09-01-2014
DOI: 10.1002/2013JD020792
Publisher: American Geophysical Union (AGU)
Date: 07-10-2020
DOI: 10.1029/2019JD031914
Abstract: The Tibetan Plateau has experienced substantial warming during the last few decades. As a result, the cryosphere of the Tibetan Plateau, including the snow cover, has shown significant changes. We characterized the changes in snow over the Tibetan Plateau using several snow‐related indices, including snow depth and snow‐covered days based on the MERRA‐2 and JRA‐55 reanalysis data set and passive microwave (MW) satellite observations. The analyses were performed over different time periods—including the last 38, 30, 20, and 10 snow seasons—from 1980 to 2018. The results show that the significant trends in snow depth and snow‐covered days are dependent on both the season and region and vary with the data set and snow‐related index used. There was a clear decrease in the annual maximum consecutive snow‐covered days (CSCDMax), and this was characterized by a later begin time and an earlier end time for both MERRA‐2 and JRA‐55, but only a later begin time for MW during the time period 1980–2018. The changes in CSCDMax were generally dominated by changes in the begin time, which were mainly caused by the increase in the average 2‐m air temperature for November–December. The relatively weak trends in the end time were a result of the combined impact of warmer temperatures and more precipitation in March–April. The CSCDMax averaged over the Tibetan Plateau showed decreasing trends in all studied time periods for JRA‐55 and MW and in the last 38 and 10 snow seasons for MERRA‐2 during the time period 1980–2018.
Publisher: Wiley
Date: 30-01-2017
DOI: 10.1002/JOC.4993
Publisher: American Geophysical Union (AGU)
Date: 08-2019
DOI: 10.1029/2019MS001634
Publisher: Elsevier BV
Date: 09-2014
Publisher: Elsevier BV
Date: 07-2003
Publisher: Elsevier BV
Date: 03-2011
Publisher: American Geophysical Union (AGU)
Date: 12-02-2015
DOI: 10.1002/2014JD022479
Publisher: Copernicus GmbH
Date: 23-01-2017
Abstract: Abstract. Dust plumes emitted from the narrow Arabian Red Sea coastal plain are often observed on satellite images and felt in local population centers. Despite its relatively small area, the coastal plain could be a significant dust source however, its effect is not well quantified as it is not well approximated in global or even regional models. In addition, because of close proximity to the Red Sea, a significant amount of dust from the coastal areas could be deposited into the Red Sea and serve as a vital component of the nutrient balance of marine ecosystems.In the current study, we apply the offline Community Land Model version 4 (CLM4) to better quantify dust emission from the coastal plain during the period of 2009–2011. We verify the spatial and temporal variability in model results using independent weather station reports. We also compare the results with the MERRA Aerosol Reanalysis (MERRAero). We show that the best results are obtained with 1 km model spatial resolution and dust source function based on Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements. We present the dust emission spatial pattern, as well as estimates of seasonal and diurnal variability in dust event frequency and intensity, and discuss the emission regime in the major dust generation hot spot areas. We demonstrate the contrasting seasonal dust cycles in the northern and southern parts of the coastal plain and discuss the physical mechanisms responsible for dust generation.This study provides the first estimates of the fine-scale spatial and temporal distribution of dust emissions from the Arabian Red Sea coastal plain constrained by MERRAero and short-term WRF-Chem simulations. The estimate of total dust emission from the coastal plain, tuned to fit emissions in MERRAero, is 7.5 ± 0.5 Mt a−1. Small interannual variability indicates that the study area is a stable dust source. The mineralogical composition analysis shows that the coastal plain generates around 76 ± 5 kt of iron oxides and 6 ± 0.4 kt of phosphorus annually. Over 65 % of dust is emitted from the northern part of the coastal plain.
Publisher: Copernicus GmbH
Date: 06-10-2022
Publisher: American Geophysical Union (AGU)
Date: 07-04-2007
DOI: 10.1029/2006JD007522
Publisher: American Geophysical Union (AGU)
Date: 21-03-2009
DOI: 10.1029/2008JD010756
Publisher: Elsevier BV
Date: 12-2013
Publisher: American Geophysical Union (AGU)
Date: 04-2021
DOI: 10.1029/2020MS002214
Publisher: American Geophysical Union (AGU)
Date: 09-2015
DOI: 10.1002/2014WR016650
Publisher: American Geophysical Union (AGU)
Date: 16-02-2020
DOI: 10.1029/2019JD030417
Publisher: Springer Science and Business Media LLC
Date: 23-09-2023
Publisher: American Meteorological Society
Date: 02-2011
Abstract: The ability of two versions of the Noah land surface model (LSM) to simulate the water cycle of the Little Washita River experimental watershed is evaluated. One version that uses the standard hydrological parameterizations of Noah 2.7 (STD) is compared another version that replaces STD’s subsurface hydrology with a simple aquifer model and topography-related surface and subsurface runoff parameterizations (GW). Simulations on a distributed grid at fine resolution are compared to the long-term distribution of observed daily-mean runoff, the spatial statistics of observed soil moisture, and locally observed latent heat flux. The evaluation targets the typical behavior of ensembles of models that use realistic, near-optimal sets of parameters important to runoff. STD and GW overestimate the ratio of runoff to evapotranspiration. In the subset of STD and GW runs that best reproduce the timing and the volume of streamflow, the surface-to-subsurface runoff ratio is overestimated and simulated streamflow is much flashier than observations. Both models’ soil columns wet and dry too quickly, implying that there are structural shortcomings in the formulation of STD that cannot be overcome by adding GW’s increased complexity to the model. In its current formulation, GW extremely underestimates baseflow’s contribution to total runoff and requires a shallow water table to function realistically. In the catchment (depth to water table & m), GW functions as a simple bucket model. Because model parameters are likely scale and site dependent, the need for even “physically based” models to be extensively calibrated for all domains on which they are applied is underscored.
Publisher: Elsevier BV
Date: 04-2013
Publisher: American Geophysical Union (AGU)
Date: 2007
DOI: 10.1029/2006GL028364
Publisher: American Geophysical Union (AGU)
Date: 15-08-2003
DOI: 10.1029/2002JD003326
Publisher: American Meteorological Society
Date: 08-2003
Abstract: The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system a runoff parameterization following the TOPMODEL concept a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as ex les, are compared with observations and with the simulation of three other land models [the Biosphere-Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94)].
Publisher: Springer Science and Business Media LLC
Date: 13-07-2023
DOI: 10.1038/S43247-023-00922-2
Abstract: Drought and pluvial transitions have attracted widespread attention. However, the dynamic evolution and underlying mechanisms of drought and pluvial transitions remain poorly understood. Here, we demonstrate that there is a significant increase in the occurrence of drought and pluvial transitions (0.24−1.03% per year) globally during 1980−2020. We use convergent cross mapping to detect causal relationships between time series variables in the climate system and find that drought and pluvial transitions can be explained by an indirect transitive causal chain. Specifically, the soil moisture−latent heat flux−precipitation causal chain is likely to trigger the rainfall following the dryness in humid regions where enhanced evaporation increases the actual atmospheric moisture favoring the pluvial occurrence. By contrast, the soil moisture−moisture convergence−precipitation causal chain enhances the post-drought rainfall in arid regions due to atmospheric circulation dynamics. Our results indicate that land−atmosphere feedbacks play an important role in triggering the drought-pluvial shift in a changing climate.
Publisher: American Geophysical Union (AGU)
Date: 04-01-2005
DOI: 10.1029/2004JD005130
Publisher: American Geophysical Union (AGU)
Date: 12-08-2022
DOI: 10.1029/2022GL098952
Abstract: Land surface skin temperature, a critical indicator of climate change, connects the water and energy cycles between the land and the atmosphere. Here, we evaluate the simulations of land surface skin temperature from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models with satellite‐based datasets and reanalysis. We find systematic cold skin temperature biases over arid regions in CMIP5/CMIP6 simulations. Over arid and semi‐arid regions, latent heat biases drive skin temperature biases by evaporative cooling. Over humid regions, surface downward shortwave and albedo biases are relatively more critical. Spatial patterns of biases remain similar in the latest CMIP6 simulations, suggesting systematic biases in land‐atmosphere interactions. These biases need to be corrected or considered while using models for future projections.
Publisher: Elsevier BV
Date: 12-2010
Publisher: American Geophysical Union (AGU)
Date: 02-06-2014
DOI: 10.1002/2014GL059980
Publisher: American Geophysical Union (AGU)
Date: 2003
DOI: 10.1029/2002GL015628
Publisher: Research Square Platform LLC
Date: 15-04-2021
DOI: 10.21203/RS.3.RS-424811/V1
Abstract: Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. Here, we construct a set of bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a nonlinear trend from the mean of 18 CMIP6 models. The dataset spans the historical period of 1979–2014 and future scenarios (SSP245 and SSP585) of 2015–2100 with a horizontal resolution of 1.25° × 1.25° and 6-hourly intervals. Our evaluation suggests that the bias-corrected data shows clearly better quality than in idual CMIP6 models evaluated in terms of climatological mean, interannual variance and extreme events. The presented dataset will be useful for the dynamical downscaling projections of future climate, atmospheric environment, hydrology, agriculture, wind power, etc.
Publisher: Springer Science and Business Media LLC
Date: 24-07-2020
DOI: 10.1038/S41467-020-17581-Y
Abstract: Groundwater provides critical freshwater supply, particularly in dry regions where surface water availability is limited. Climate change impacts on GWS (groundwater storage) could affect the sustainability of freshwater resources. Here, we used a fully-coupled climate model to investigate GWS changes over seven critical aquifers identified as significantly distressed by satellite observations. We assessed the potential climate-driven impacts on GWS changes throughout the 21 st century under the business-as-usual scenario (RCP8.5). Results show that the climate-driven impacts on GWS changes do not necessarily reflect the long-term trend in precipitation instead, the trend may result from enhancement of evapotranspiration, and reduction in snowmelt, which collectively lead to ergent responses of GWS changes across different aquifers. Finally, we compare the climate-driven and anthropogenic pumping impacts. The reduction in GWS is mainly due to the combined impacts of over-pumping and climate effects however, the contribution of pumping could easily far exceed the natural replenishment.
Publisher: Springer Science and Business Media LLC
Date: 14-12-2020
Publisher: Elsevier BV
Date: 12-2013
Publisher: American Meteorological Society
Date: 17-12-2015
Abstract: This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30°N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of −1.54 × 102 km2. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow–no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044 m (0.19 m) and −0.010 m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.
Publisher: American Geophysical Union (AGU)
Date: 27-04-2008
DOI: 10.1029/2007JD009232
Publisher: American Meteorological Society
Date: 15-02-2017
Abstract: Continental-scale snow radiance assimilation (RA) experiments are conducted in order to improve snow estimates across snow and land-cover types in North America. In the experiments, the ensemble adjustment Kalman filter is applied and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB observations are assimilated into an RA system composed of the Community Land Model, version 4 (CLM4) radiative transfer models (RTMs) and the Data Assimilation Research Testbed (DART). The performance of two snowpack RTMs, the Dense Media Radiative Transfer–Multi-Layers model (DMRT-ML), and the Microwave Emission Model of Layered Snowpacks (MEMLS) in improving snow depth estimates through RA is compared. Continental-scale snow estimates are enhanced through RA by using AMSR-E TB at the 18.7- and 23.8-GHz channels [3% (DMRT-ML) and 2% (MEMLS) improvements compared to the cases using the 18.7- and 36.5-GHz channels] and by considering the vegetation single-scattering albedo ω [2.5% (DMRT-ML) and 4.8% (MEMLS) improvements compared to the cases neglecting ω]. The contribution of TB of the vegetation canopy to TB at the top of the atmosphere is better represented by considering ω in the RA system, and improvements in the resulting snow depth are evident for the forest land-cover type (about 5%–11%) and the taiga and alpine snow classes (about 5%–11% and 4%–8%, respectively), especially in the MEMLS case. Compared to the open-loop run (0.171-m snow depth RMSE), about 7% (DMRT-ML) and 10% (MEMLS) overall improvements of the RA performance are achieved.
Publisher: MDPI AG
Date: 31-08-2017
DOI: 10.3390/RS9090900
Publisher: Elsevier BV
Date: 07-2003
Publisher: American Geophysical Union (AGU)
Date: 27-10-2008
DOI: 10.1029/2008JD009820
Publisher: Elsevier BV
Date: 06-2016
Publisher: American Geophysical Union (AGU)
Date: 24-06-2011
DOI: 10.1029/2010JD015140
Publisher: Elsevier BV
Date: 08-2013
Publisher: American Geophysical Union (AGU)
Date: 24-06-2011
DOI: 10.1029/2010JD015139
Publisher: Elsevier BV
Date: 02-2017
Publisher: American Meteorological Society
Date: 09-2017
Abstract: Annual precipitation anomalies over eastern China are characterized by a north–south dipole pattern, referred to as the “southern flooding and northern drought” pattern (SF/ND), fluctuating on decadal time scales. Previous research has suggested possible links with oceanic forcing, but the underlying physical mechanisms by which sea surface temperature (SST) variability impacts the dipole pattern remains unclear. Idealized atmospheric general circulation model experiments conducted by the U.S. CLIVAR Drought Working Group are used to investigate the role of historical SST anomalies associated with Pacific El Niño–Southern Oscillation (ENSO)-like and the Atlantic multidecadal oscillation (AMO) patterns in this dipole pattern. The results show that the Pacific SST pattern plays a dominant role in driving the decadal variability of this dipole pattern and the associated atmospheric circulation anomalies, whereas the Atlantic SST pattern contributes to a much lesser degree. The direct atmospheric response to the Pacific SST pattern is a large-scale cyclonic or anticyclonic circulation anomaly in the lower troposphere occupying the entire northern North Pacific. During the warm phase of the Pacific SST pattern, it is cyclonic with northwesterly wind anomalies over northern China pushing the monsoon front to the south and consequently SF/ND. During the cold phase of the Pacific SST pattern, the circulation anomaly reverses with southeasterly winds over northern China allowing the monsoon front and the associated rainband to migrate northward, resulting in southern drought and northern flooding. The Atlantic SST pattern plays a supplementary role, enhancing the dipole pattern when the Pacific SST and Atlantic SST patterns are in opposite phases and weakening it when the phases are the same.
Publisher: Springer Science and Business Media LLC
Date: 24-02-2015
Publisher: Elsevier BV
Date: 06-2008
Publisher: Springer Science and Business Media LLC
Date: 29-03-2012
Publisher: American Geophysical Union (AGU)
Date: 18-10-0222
DOI: 10.1002/2015JD024424
Publisher: American Meteorological Society
Date: 06-2009
Abstract: The authors introduce and compare the performance of the unified Noah land surface model (LSM) and its augments with physically based, more conceptually realistic hydrologic parameterizations. Forty-five days of 30-min data collected over nine sites in transition zones are used to evaluate (i) their benchmark, the standard Noah LSM release 2.7 (STD) (ii) a version equipped with a short-term phenology module (DV) and (iii) one that couples a lumped, unconfined aquifer model to the model soil column (GW). Their model intercomparison, enhanced by multiobjective calibration and model sensitivity analysis, shows that, under the evaluation conditions, the current set of enhancements to Noah fails to yield significant improvement in the accuracy of simulated, high-frequency, warm-season turbulent fluxes, and near-surface states across these sites. Qualitatively, the versions of DV and GW implemented degrade model robustness, as defined by the sensitivity of model performance to uncertain parameters. Quantitatively, calibrated DV and GW show only slight improvement in the skill of the model over calibrated STD. Then, multiple model realizations are compared to explicitly account for parameter uncertainty. Model performance, robustness, and fitness are quantified for use across varied sites. The authors show that the least complex benchmark LSM (STD) remains as the most fit version of the model for broad application. Although GW typically performs best when simulating evaporative fraction (EF), 24-h change in soil wetness (ΔW30), and soil wetness, it is only about half as robust as STD, which also performs relatively well for all three criteria. GW’s superior performance results from bias correction, not from improved soil moisture dynamics. DV performs better than STD in simulating EF and ΔW30 at the wettest site, because DV tends to enhance transpiration and canopy evaporation at the expense of direct soil evaporation. This same model structure limits performance at the driest site, where STD performs best. This dichotomous performance suggests that the formulations that determine the partitioning of LE flux need to be modified for broader applicability. Thus, this work poses a caveat for simple “plug and play” of functional modules between LSMs and showcases the utility of rigorous testing during model development.
Publisher: Copernicus GmbH
Date: 25-04-2022
Publisher: American Geophysical Union (AGU)
Date: 2019
DOI: 10.1029/2017WR022236
Abstract: The precipitation partitioning between evapotranspiration (ET) and runoff (R) at the land surface is controlled by atmospheric boundary layer and terrestrial hydrological processes. These processes in land surface models are manifested primarily as stomatal conductance, soil moisture limitation factor to transpiration (β‐factor), turbulence, and runoff generation. What are the sensitivities of precipitation partitioning to the parameterizations of these processes? To address this overarching question, the annual and seasonal means of ET and R over the conterminous United States were simulated using 48 configurations of the Noah land surface model with multiparameterization options (Noah‐MP). The Sobol' total sensitive index was used to quantify the sensitivity of ET and R to the parameterizations of the four processes mentioned above. Results show that the sensitivities of the annual means depend on climatic conditions and the interplay between ET and R plays an important role. In humid regions, precipitation is mostly partitioned into R, whereas the simulations can be more sensitive to ET's parameterizations. In arid regions, ET accounts for the major partition, whereas the simulations can be more sensitive to the runoff parameterization. Seasonal means exhibit different sensitivities from the annual means. The seasonal mean ET is more sensitive to ET's parameterizations, and R is more sensitive to the runoff parameterization. The β‐factor, which is neglectable for the annual means, is important for summer‐time ET. Mediated by the terrestrial water storage memories, ET interplays R across seasons. The winter‐time R is still sensitive to the stomatal conductance that only modulates growing‐season ET.
Publisher: Elsevier BV
Date: 06-2014
Publisher: Copernicus GmbH
Date: 12-04-2023
DOI: 10.5194/EGUSPHERE-2023-675
Abstract: Abstract. The widely-used open-source community Noah-MP land surface model (LSM) is designed for applications ranging from uncoupled land-surface and ecohydrological process studies to coupled numerical weather prediction and decadal global/regional climate simulations. It has been used in many coupled community weather/climate/hydrology models. In this study, we modernize/refactor the Noah-MP LSM by adopting modern Fortran code and data structures and standards, which substantially enhances the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization and interoperability by re-organizing model physics into in idual process-level Fortran module files, (2) enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) enhanced code structure and calling workflow by leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standard, and (5) enhanced driver and interface structures to couple with host weather/climate/hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather/climate/hydrology models in the future. Overall, the modernized Noah-MP will allow a more efficient and convenient process for future model developments and applications.
Publisher: Wiley
Date: 13-12-2021
Publisher: Springer Science and Business Media LLC
Date: 14-04-2015
Publisher: Copernicus GmbH
Date: 08-09-2023
Publisher: American Geophysical Union (AGU)
Date: 03-2023
DOI: 10.1029/2022MS003136
Abstract: The Tibetan Plateau (TP) features unique and highly heterogeneous soils, terrains, vegetation, and climate. Accurately modeling complex freeze‐thaw processes and their hydrothermal impacts remains a great challenge. This study focused on deciphering the spatiotemporal variability of erse parameterization schemes in the soil hydrothermal simulations using the Noah‐MP land surface model. We first discussed the spin‐up time required by the model to reach the equilibrium state, and then performed a sensitivity analysis of these schemes. The Moderate Resolution Imaging Spectroradiometer land surface temperature and Soil Moisture Active Passive remote sensing products were used as benchmarks to evaluate the schemes' performance. Results show that longer spin‐up times are required in permafrost regions owing to water phase changes. Ground temperature and soil temperature are mainly sensitive to energy‐related schemes. Vegetation‐related schemes play an important role after the growing season begins on the southeastern TP. Soil water content shows strong sensitivity to schemes related to both water and energy transport. However, the sensitivity of these energy‐related schemes is weakened when simulating total soil moisture, including the total amount of water and ice, indicating that these schemes have marked impacts on soil freeze‐thaw processes. These results reveal the different spatial (both regional and depth‐related) and temporal effects of parameterization schemes we also provided a preliminary selection of these schemes at a regional scale that could facilitate the further improvement of the soil hydrothermal simulations on the TP.
Publisher: American Geophysical Union (AGU)
Date: 05-2016
DOI: 10.1002/2015EA000142
Publisher: American Geophysical Union (AGU)
Date: 09-2014
DOI: 10.1002/2014JF003095
Abstract: Mineral dust in the atmosphere has implications for Earth's radiation budget, biogeochemical cycles, hydrological cycles, human health, and visibility. Currently, the simulated vertical mass flux of dust differs greatly among the existing dust models. While most of the models utilize an erodibility factor to characterize dust sources, this factor is assumed to be static, without sufficient characterization of the highly heterogeneous and dynamic nature of dust source regions. We present a high‐resolution land cover map of the Middle East and North Africa (MENA) in which the terrain is classified by visually examining satellite images obtained from Google Earth Professional and Environmental Systems Research Institute Basemap. We show that the correlation between surface wind speed and Moderate Resolution Imaging Spectroradiometer deep blue aerosol optical depth (AOD) can be used as a proxy for erodibility, which satisfactorily represents the spatiotemporal distribution of soil‐derived dust sources. This method also identifies agricultural dust sources and eliminates the satellite‐observed dust component that arises from long‐range transport, pollution, and biomass burning. The erodible land cover of the MENA region is grouped into nine categories: (1) bedrock: with sediment, (2) sand deposit, (3) sand deposit: on bedrock, (4) sand deposit: stabilized, (5) agricultural and urban area, (6) fluvial system, (7) stony surface, (8) playa/sabkha, and (9) savanna/grassland. Our results indicate that erodibility is linked to the land cover type and has regional variation. An improved land cover map, which explicitly accounts for sediment supply, availability, and transport capacity, may be necessary to represent the highly dynamic nature of dust sources in climate models.
Publisher: Inter-Research Science Center
Date: 19-07-2012
DOI: 10.3354/CR01093
Publisher: Springer Science and Business Media LLC
Date: 19-07-2017
Publisher: American Geophysical Union (AGU)
Date: 22-12-2016
DOI: 10.1002/2016JD025187
Publisher: Research Square Platform LLC
Date: 10-03-2021
DOI: 10.21203/RS.3.RS-267288/V1
Abstract: Flash droughts can cause more serious environmental and agricultural impacts than traditional droughts because of the sudden onset and rapid intensification. However, it remains unclear how rapidly flash droughts develop and intensify worldwide. Here, we present for the first time a comprehensive assessment of the onset development phase of flash droughts on a global scale. We find that humid and semi-humid regions are more vulnerable to flash droughts. And 56.8% of flash droughts are extremely fast-developing (timescale ≤ 15 days) and intensifying (intensification rate ≥ 12.5th percentile) droughts during 2000–2019. More importantly, the evolution of flash droughts is accelerating with a significantly shorter timescale and a faster intensification, implying that less time is left for early warning and impact preparation. Our findings suggest that urgent action is needed to upgrade existing drought monitoring systems for better capturing the more rapid onset and evolution of flash droughts.
Publisher: American Geophysical Union (AGU)
Date: 03-2008
DOI: 10.1029/2007JG000562
Publisher: American Geophysical Union (AGU)
Date: 03-2008
DOI: 10.1029/2007JG000563
Publisher: Springer Science and Business Media LLC
Date: 28-07-2016
DOI: 10.1038/SREP30690
Abstract: The absorptive properties of dust aerosols largely determine the magnitude of their radiative impacts on the climate system. Currently, climate models use globally constant values of dust imaginary refractive index (IRI), a parameter describing the dust absorption efficiency of solar radiation, although it is highly variable. Here we show with model experiments that the dust-induced Indian summer monsoon (ISM) rainfall differences (with dust minus without dust) change from −9% to 23% of long-term climatology as the dust IRI is changed from zero to the highest values used in the current literature. A comparison of the model results with surface observations, satellite retrievals, and reanalysis data sets indicates that the dust IRI values used in most current climate models are too low, tending to significantly underestimate dust radiative impacts on the ISM system. This study highlights the necessity for developing a parameterization of dust IRI for climate studies.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-13345
Abstract: & & Multiple environmental factors influence the error structure of precipitation datasets. The conventional precipitation evaluation method over-simply analyzes how the statistical indicators vary with one or two factors via dimensionality reduction. As a result, the compound influences of multiple factors are superposed rather than disassembled. To overcome this deficiency, this study presents a novel approach to systematically and objectively analyze the error structure within precipitation products using decision trees. This data-driven method can analyze multiple factors simultaneously and extract the compound effects of various influencers. By interpreting the decision tree structures, the error characteristics of precipitation products are investigated. Three types of precipitation products (two satellite-based: & #8216 top-down& #8217 IMERG and & #8216 bottom-up& #8217 SM2RAIN-ASCAT, and one reanalysis: ERA5-Land) are evaluated across CONUS. The study period is from 2010 to 2019, and the ground-based Stage IV precipitation dataset is used as the ground truth. By data mining 60 binary decision trees, the spatiotemporal pattern of errors and the land surface influences are analyzed.& / & & & & & / & & & Results indicate that IMERG and ERA5-Land perform better than SM2RAIN-ASCAT with higher accuracy and more stable interannual patterns for the ten years of data analyzed. The conventional bias evaluation finds that ERA5-Land and SM2RAIN-ASCAT underestimate in summer and winter, respectively. The decision tree method cross-assesses three spatiotemporal factors and finds that underestimation of ERA5-Land occurs in the eastern part of the rocky mountains, and SM2RAIN-ASCAT underestimates precipitation over high latitudes, especially in winter. Additionally, the decision tree method ascribes system errors to nine physical variables, of which the distance to the coast, soil type, and DEM are the three dominant features. On the other hand, the land cover classification and the topography position index are two relatively weak factors.& / &
Publisher: Copernicus GmbH
Date: 27-05-2015
Abstract: Abstract. Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long-term Ecological Research site within the U.S. Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of the carbon and water cycles (e.g., net primary productivity and evapotranspiration). The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.
Publisher: Elsevier BV
Date: 04-2013
Publisher: Wiley
Date: 24-09-2015
Publisher: American Geophysical Union (AGU)
Date: 09-11-2016
DOI: 10.1002/2016GL070966
Abstract: We present the first systematic study to quantify the impact of land initialization on seasonal temperature prediction in the Northern Hemisphere, emphasizing the role of land snow data assimilation (DA). Three suites of ensemble seasonal integrations are conducted for coupled land‐atmosphere runs. The land component is initialized using datasets from (1) no DA, (2) assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF), and (3) assimilating both MODIS SCF and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage. Results show that snow DA improves temperature predictions especially in the Tibetan Plateau (by 5–20%) and high latitudes. Improvements at low latitudes are seen immediately and last up to 60 days, whereas improvements at high latitudes only appear later in transitional seasons. At high latitudes, assimilating GRACE data results in marked and prolonged improvements (by ~25%) due to large initial snow mass changes. This study has great implications for future land DA and seasonal climate prediction studies.
Publisher: Elsevier BV
Date: 2019
Publisher: American Geophysical Union (AGU)
Date: 18-06-2014
DOI: 10.1002/2013JD021329
Publisher: American Meteorological Society
Date: 22-08-2016
Abstract: The seasonal responses of the Indian summer monsoon (ISM) to dust aerosols in local (the Thar Desert) and remote (the Middle East and western China) regions are studied using the WRF Model coupled with online chemistry (WRF-Chem). Ensemble experiments are designed by perturbing model physical and chemical schemes to examine the uncertainties of model parameterizations. Model results show that the dust-induced increase in ISM total rainfall can be attributed to the remote dust in the Middle East, while the contributions from local and remote dust are very limited. Convective rainfall shows a spatially more homogeneous increase than stratiform rainfall, whose responses follow the topography. The magnitude of dust-induced increase in rainfall is comparable to that caused by anthropogenic aerosols. The Middle East dust aerosols tend to enhance the southwesterly monsoon flow, which can transport more water vapor to southern and northern India, while the anthropogenic aerosols tend to enhance the southeasterly monsoon flow, resulting in more water vapor and rainfall over northern India. Both dust and anthropogenic aerosol-induced rainfall responses can be attributed to their heating effect in the mid-to-upper troposphere, which enhances monsoon circulations. The heating effect of dust over the Iranian Plateau seems to play a bigger role than that over the Tibetan Plateau, while the heating of anthropogenic aerosols over the Tibetan Plateau is more important. Moreover, dust aerosols can decrease rainfall over the Arabian Sea through their indirect effect. This study addresses the relative roles of dust and anthropogenic aerosols in altering the ISM rainfall and provides insights into aerosol–ISM interactions.
Publisher: Elsevier BV
Date: 06-03-2009
Publisher: Elsevier BV
Date: 06-2005
DOI: 10.1016/J.JENVMAN.2004.11.024
Abstract: This paper develops a framework for regional scale flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-HMS/RAS). The San Antonio River Basin (about 4000 square miles, 10,000 km2) in Central Texas, USA, is the domain of the study because it is a region subject to frequent occurrences of severe flash flooding. A major flood in the summer of 2002 is chosen as a case to examine the modeling framework. The model consists of a rainfall-runoff model (HEC-HMS) that converts precipitation excess to overland flow and channel runoff, as well as a hydraulic model (HEC-RAS) that models unsteady state flow through the river channel network based on the HEC-HMS-derived hydrographs. HEC-HMS is run on a 4 x 4 km grid in the domain, a resolution consistent with the resolution of NEXRAD rainfall taken from the local river authority. Watershed parameters are calibrated manually to produce a good simulation of discharge at 12 subbasins. With the calibrated discharge, HEC-RAS is capable of producing floodplain polygons that are comparable to the satellite imagery. The modeling framework presented in this study incorporates a portion of the recently developed GIS tool named Map to Map that has been created on a local scale and extends it to a regional scale. The results of this research will benefit future modeling efforts by providing a tool for hydrological forecasts of flooding on a regional scale. While designed for the San Antonio River Basin, this regional scale model may be used as a prototype for model applications in other areas of the country.
Publisher: American Geophysical Union (AGU)
Date: 11-2007
DOI: 10.1029/2007JD008674
Publisher: American Geophysical Union (AGU)
Date: 17-04-2015
DOI: 10.1002/2014JD022958
Publisher: American Geophysical Union (AGU)
Date: 22-07-2016
DOI: 10.1002/2015JD024248
Publisher: Wiley
Date: 14-12-2021
Publisher: American Meteorological Society
Date: 10-2011
Abstract: The mapped rivers and streams of the contiguous United States are available in a geographic information system (GIS) dataset called National Hydrography Dataset Plus (NHDPlus). This hydrographic dataset has about 3 million river and water body reaches along with information on how they are connected into networks. The U.S. Geological Survey (USGS) National Water Information System (NWIS) provides streamflow observations at about 20 thousand gauges located on the NHDPlus river network. A river network model called Routing Application for Parallel Computation of Discharge (RAPID) is developed for the NHDPlus river network whose lateral inflow to the river network is calculated by a land surface model. A matrix-based version of the Muskingum method is developed herein, which RAPID uses to calculate flow and volume of water in all reaches of a river network with many thousands of reaches, including at ungauged locations. Gauges situated across river basins (not only at basin outlets) are used to automatically optimize the Muskingum parameters and to assess river flow computations, hence allowing the diagnosis of runoff computations provided by land surface models. RAPID is applied to the Guadalupe and San Antonio River basins in Texas, where flow wave celerities are estimated at multiple locations using 15-min data and can be reproduced reasonably with RAPID. This river model can be adapted for parallel computing and although the matrix method initially adds a large overhead, river flow results can be obtained faster than with the traditional Muskingum method when using a few processing cores, as demonstrated in a synthetic study using the upper Mississippi River basin.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: American Geophysical Union (AGU)
Date: 12-2020
DOI: 10.1029/2020MS002132
Abstract: We develop a novel framework for rigorously evaluating land surface models (LSMs) against observations by recognizing the asymmetry between verification‐ and falsification‐oriented approaches. The former approach cannot completely verify LSMs even though it exhausts every case of consistency between the model predictions and observations, whereas the latter only requires a single case of inconsistency to reveal that there must be something wrong. We argue that it is such an inconsistency that stimulates further development of the models and enhancement of the observations. We therefore propose a falsification‐oriented signature‐based evaluation framework to identify cases of inconsistency between model predictions and observations by extracting signatures based on a set of key assumptions. We apply this framework to evaluate an ensemble of simulations from the Noah‐MP LSM against observations over the continental United States under the three assumptions of water mass conservation, no lateral water flow, and a sufficiently long period of time. Regions showing inconsistencies between the Noah‐MP ensemble simulations and the observations are located in the western mountainous areas, the Yellowstone river basin, the lower Floridan aquifer, the Niobrara river basin at the north tip of the Ogallala aquifer, and the basins downstream of the Balcones fault zones in Texas. These regions coincide with the sites where both advances in theoretical modeling and new observational data (e.g., from the Critical Zone Observatories) have emerged.
Publisher: American Geophysical Union (AGU)
Date: 23-07-2013
DOI: 10.1002/JGRD.50576
Publisher: American Geophysical Union (AGU)
Date: 27-12-2014
DOI: 10.1002/2014JD022167
Publisher: American Geophysical Union (AGU)
Date: 04-2006
DOI: 10.1029/2005GL025555
Publisher: Copernicus GmbH
Date: 15-01-2016
DOI: 10.5194/GMD-9-1-2016
Abstract: Abstract. Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 04-2016
Publisher: American Geophysical Union (AGU)
Date: 08-2007
DOI: 10.1029/2007GL030413
Publisher: Wiley
Date: 23-10-2017
Publisher: American Geophysical Union (AGU)
Date: 07-2021
DOI: 10.1029/2020WR028846
Abstract: Multi‐physics ensembles have emerged as a promising approach to hydrological simulations. As multi‐physics ensembles are constructed by perturbing the model physics, the ensemble members share a substantial portion of the same physics and hence are not independent of each other. It is unknown whether and to what extent this nonindependence affects the skill gain of the ensemble method, especially compared with the multi‐model ensemble approach. This study compares a multi‐physics ensemble configured from the Noah land surface model with multi‐parameterization options (Noah‐MP) with the North American Land Data Assimilation System (NLDAS) multi‐model ensemble. The two ensembles are evaluated in terms of the annual cycle and interannual anomaly at 12 River Forecast Centers over the conterminous United States. The ensemble skill gain is measured by the difference between the performance of the ensemble mean and the average of the ensemble members' performance, and the inter‐member independence is measured by error correlations. Results show that, due to the improved model physics, the Noah‐MP configurations outperform, on average, the NLDAS models, especially in the snow‐dominated areas. The Noah‐MP ensemble almost always obtains an outstanding member that performs the best among the two ensembles, reflecting its dense s ling of the feasible model physics space. However, these two performance superiorities do not lead to a superiority of the ensemble mean. The Noah‐MP ensemble has a lower ensemble skill gain, which corresponds to the lower inter‐member independence. These results highlight the importance of inter‐member independence, particularly when most hydrological ensemble methods have overlooked it.
Publisher: Wiley
Date: 21-04-2011
DOI: 10.1002/HYP.8070
Publisher: Elsevier BV
Date: 02-2022
Publisher: Springer Science and Business Media LLC
Date: 13-09-2017
DOI: 10.1038/S41598-017-11579-1
Abstract: Characterization of river drainage networks has been a subject of research for many years. However, most previous studies have been limited to quantities which are loosely connected to the topological properties of these networks. In this work, through a graph-theoretic formulation of drainage river networks, we investigate the eigenvalue spectra of their adjacency matrix. First, we introduce a graph theory model for river networks and explore the properties of the network through its adjacency matrix. Next, we show that the eigenvalue spectra of such complex networks follow distinct patterns and exhibit striking features including a spectral gap in which no eigenvalue exists as well as a finite number of zero eigenvalues. We show that such spectral features are closely related to the branching topology of the associated river networks. In this regard, we find an empirical relation for the spectral gap and nullity in terms of the energy dissipation exponent of the drainage networks. In addition, the eigenvalue distribution is found to follow a finite-width probability density function with certain skewness which is related to the drainage pattern. Our results are based on optimal channel network simulations and validated through ex les obtained from physical experiments on landscape evolution. These results suggest the potential of the spectral graph techniques in characterizing and modeling river networks.
Publisher: Springer Science and Business Media LLC
Date: 15-08-2017
Publisher: Copernicus GmbH
Date: 02-09-2015
Abstract: Abstract. The response of the Indian summer monsoon (ISM) circulation and precipitation to Middle East dust aerosols on sub-seasonal timescales is studied using observations and the Weather Research and Forecasting model coupled with online chemistry (WRF-Chem). Satellite data show that the ISM rainfall in coastal southwest India, central and northern India, and Pakistan is closely associated with the Middle East dust aerosols. The physical mechanism behind this dust–ISM rainfall connection is examined through ensemble simulations with and without dust emissions. Each ensemble includes 16 members with various physical and chemical schemes to consider the model uncertainties in parameterizing short-wave radiation, the planetary boundary layer, and aerosol chemical mixing rules. Experiments show that dust aerosols increase rainfall by about 0.44 mm day−1 (~10 % of the climatology) in coastal southwest India, central and northern India, and north Pakistan, a pattern consistent with the observed relationship. The ensemble mean rainfall response over India shows a much stronger spatial correlation with the observed rainfall response than any other ensemble members. The largest modeling uncertainties are from the boundary layer schemes, followed by short-wave radiation schemes. In WRF-Chem, the dust aerosol optical depth (AOD) over the Middle East shows the strongest correlation with the ISM rainfall response when dust AOD leads rainfall response by about 11 days. Further analyses show that increased ISM rainfall is related to enhanced southwesterly monsoon flow and moisture transport from the Arabian Sea to the Indian subcontinent, which are associated with the development of an anomalous low-pressure system over the Arabian Sea, the southern Arabian Peninsula, and the Iranian Plateau due to dust-induced heating in the troposphere. The dust-induced heating in the mid-upper troposphere is mainly located in the Iranian Plateau rather than the Tibetan Plateau. This study demonstrates a thermodynamic mechanism that links remote desert dust emissions in the Middle East to ISM circulation and precipitation variability on sub-seasonal timescales, which may have implications for ISM rainfall forecasts.
Publisher: Springer Science and Business Media LLC
Date: 07-03-2015
Publisher: American Geophysical Union (AGU)
Date: 11-12-2004
DOI: 10.1029/2004JD004884
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-4101
Abstract: The Tibetan Plateau (TP) features unique and highly heterogeneous soils, terrains, vegetation, and climate. Accurately modeling complex freeze-thaw processes and their hydrothermal impacts remains a great challenge. This study focused on deciphering the spatiotemporal variability of erse parameterization schemes in the soil hydrothermal simulations using the Noah-MP land surface model. We first discussed the spin-up time required by the model to reach the equilibrium state, and then performed a sensitivity analysis of these schemes. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and Soil Moisture Active Passive (SMAP) remote sensing products were used as benchmarks to evaluate the schemes& #8217 performance. Results show that longer spin-up times are required in permafrost regions owing to water phase changes. Ground temperature and soil temperature are mainly sensitive to energy-related schemes. Vegetation-related schemes play an important role after the growing season begins on the southeastern TP. Soil water content shows strong sensitivity to schemes related to both water and energy transport. However, the sensitivity of these energy-related schemes is weakened when simulating total soil moisture, including the total amount of water and ice, indicating that these schemes have marked impacts on soil freeze-thaw processes. These results reveal the different spatial (both regional and depth-related) and temporal effects of parameterization schemes we also provided a preliminary selection of these schemes at a regional scale that could facilitate the further improvement of the soil hydrothermal simulations on the TP.
Publisher: American Geophysical Union (AGU)
Date: 28-06-2016
DOI: 10.1002/2016GL069386
Publisher: Wiley
Date: 22-12-2010
DOI: 10.1002/JOC.1995
Publisher: American Geophysical Union (AGU)
Date: 05-05-2007
DOI: 10.1029/2007GL029804
Publisher: American Geophysical Union (AGU)
Date: 08-11-2005
DOI: 10.1029/2005JD006111
Publisher: IOP Publishing
Date: 02-2018
Publisher: American Geophysical Union (AGU)
Date: 21-07-2007
DOI: 10.1029/2006JD008231
Publisher: American Geophysical Union (AGU)
Date: 19-05-2010
DOI: 10.1029/2009JD013035
Publisher: Wiley
Date: 24-11-2020
Publisher: Elsevier BV
Date: 03-2006
Publisher: Elsevier BV
Date: 07-2003
Publisher: American Geophysical Union (AGU)
Date: 31-10-2023
DOI: 10.1029/2023JD038813
Publisher: American Geophysical Union (AGU)
Date: 06-2008
DOI: 10.1029/2008GL033721
Publisher: American Meteorological Society
Date: 11-2016
Abstract: This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting TB based on their correlations with the prior TB (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171 m RMSE), the overall snow depth estimates are improved by 1.6% (0.168 m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177 m RMSE). Significant improvement of the snow depth estimates in the rule-based RA is observed for tundra snow class (11.5%, p & 0.05) and bare soil land-cover type (13.5%, p & 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.
Publisher: Copernicus GmbH
Date: 03-01-2019
DOI: 10.5194/GMD-2018-232
Abstract: Abstract. The leaf area index (LAI) is a crucial parameter for understanding the exchanges of momentum, carbon, energy, and water between terrestrial ecosystems and the atmosphere. To improve the ability to simulate land surface water and energy balances, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model (CLM) by assimilating global remotely sensed LAI data with explicit carbon and nitrogen components (CLM4CN). The purpose of this paper is to determine the best algorithm for LAI assimilation. Within this framework, four sequential assimilation algorithms, i.e., the Kalman Filter (KF), the Ensemble Kalman Filter (EnKF), the Ensemble Adjust Kalman Filter (EAKF), and the Particle Filter (PF), are applied, thoroughly analyzed and compared. The results show that assimilating remotely sensed LAI data into the CLM4CN is an effective method for improving model performance. In detail, the assimilation accuracies of the ensemble filter algorithms (EnKF and EAKF) are better than that of the KF algorithm because the KF is based on the linear model error assumption. The PF algorithm performs worse than the EAKF and EnKF algorithms because of the gradually reduced acceptance of observations with assimilation steps. In other words, the contribution of the observations to the posterior probability during the assimilation process is reduced. The EAKF algorithm is the best method because the matrix is adjusted at each time step during the assimilation procedure.
Publisher: Springer Science and Business Media LLC
Date: 04-11-2021
DOI: 10.1038/S41597-021-01079-3
Abstract: Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979–2014 and future scenarios (SSP245 and SSP585) for 2015–2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the in idual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth’s future climate, atmospheric environment, hydrology, agriculture, wind power, etc.
Publisher: Copernicus GmbH
Date: 25-04-2022
Abstract: Abstract. Rivers and river habitats around the world are under sustained pressure from anthropogenic activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. Vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNNs) approach for basin-scale river network learning and stream forecasting. GNN models are pretrained using a high-resolution vector-based river network model, and then fine-tuned with in situ streamflow observations, after which a post-processing data fusion step is proposed to propagate residuals over the entire network to correct predictions. The GNN-based framework is demonstrated over a snow-dominated watershed in the western U.S. consisting of 552 reaches. A series of experiments are performed to test different training and imputation strategies. Results show the trained GNN model can effectively serve as a surrogate model of the process-based model with high accuracies, with the median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 percent KGE improvement over cross-validation gages. Additionally we exploit and demonstrate a graph coarsening procedure that achieves comparable predicting skills at only a fraction of training cost, thus providing important insights on the degree of physical realism needed for developing large-scale GNN-based river network models.
Publisher: American Geophysical Union (AGU)
Date: 03-04-2004
DOI: 10.1029/2003JD004419
Publisher: Copernicus GmbH
Date: 11-06-2015
DOI: 10.5194/ACPD-15-15571-2015
Abstract: Abstract. The response of the Indian summer monsoon (ISM) circulation and precipitation to Middle East dust aerosols on sub-seasonal timescales is studied using observations and the Weather Research and Forecasting model with chemistry (WRF-Chem). Satellite data shows that the ISM rainfall in coastal southwest India, central and northern India, and Pakistan are closely associated with Middle East dust aerosols. The physical mechanism behind this dust–ISM rainfall connection is examined through ensemble simulations with and without dust emission. Each ensemble includes 16 members with various physical and chemical schemes to consider the model uncertainties in parameterizing shortwave radiation, the planetary boundary layer, and aerosol chemical mixing rules. Experiments show that dust aerosols increase rainfall by about 0.44 mm day−1 (~ 10%) in coastal southwest India, central and northern India, and northern Pakistan, a pattern consistent with the observed relationship. The ensemble mean rainfall response over India shows much stronger spatial correlation with the observed rainfall response than any of the ensemble members. The largest modeling uncertainties are from the boundary layer schemes, followed by shortwave radiation schemes. In WRF-Chem, the dust AOD over the Middle East shows the strongest correlation with the ISM rainfall response when dust AOD leads rainfall response by about 11 days. Further analyses show that the increased ISM rainfall is related to the enhanced southwesterly flow and moisture transport from the Arabian Sea to the Indian subcontinent, which are associated with the development of an anomalous low pressure system over the Arabian Sea, the southern Arabian Peninsula, and the Iranian Plateau due to dust-induced heating in the lower troposphere (800–500 hPa). This study demonstrates a thermodynamic mechanism that links remote desert dust emission in the Middle East to the ISM circulation and precipitation variability on sub-seasonal timescales, which may have implications for ISM rainfall forecasts.
Publisher: Copernicus GmbH
Date: 14-10-2022
DOI: 10.5194/HESS-26-5163-2022
Abstract: Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.
Publisher: American Geophysical Union (AGU)
Date: 12-11-2008
DOI: 10.1029/2008JD010180
Publisher: Copernicus GmbH
Date: 06-10-2022
Abstract: Abstract. Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS‑2) four-LSM ensemble, this study presents a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP). The 48 Noah‑MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The ensemble simulated the 1980–2015 monthly TWB over the conterminous United States (CONUS) at a 1/8° spatial resolution. Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). This dataset is available at 0.5281/zenodo.7109816 (Zheng et al., 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS estimations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilities the identification of model deficiencies. The parameterizations of shallow snow, lakes, and near-surface atmospheric turbulence should be improved in future model versions.
Publisher: American Meteorological Society
Date: 09-2016
Abstract: Very few frameworks exist that estimate global-scale soil moisture through microwave land data assimilation (DA). Toward this goal, such a framework has been developed by linking the Community Land Model, version 4 (CLM4), and a microwave radiative transfer model (RTM) with the Data Assimilation Research Testbed (DART). The deterministic ensemble adjustment Kalman filter (EAKF) within DART is utilized to estimate global multilayer soil moisture by assimilating brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). A 40-member ensemble of Community Atmosphere Model, version 4.0 (CAM4.0), reanalysis is adopted to drive CLM4 simulations. Space-specific, time-invariant microwave parameters are precalibrated to minimize uncertainties in RTM. Besides, various methods are designed to upscale AMSR-E observations for computational efficiency and time shift CAM4.0 forcing to facilitate global daily assimilations. A series of experiments are conducted to quantify the DA sensitivity to microwave parameters, choice of assimilated observations, and different CLM4 updating schemes. Evaluation results indicate that the newly established CLM4–RTM–DART framework improves the open-loop CLM4-simulated soil moisture. Precalibrated microwave parameters, rather than their default values, can ensure a more robust global-scale performance. In addition, updating near-surface soil moisture is capable of improving soil moisture in deeper layers (0–30 cm), while simultaneously updating multilayer soil moisture fails to obtain intended improvements. Future work is needed to address the systematic bias in CLM4 that cannot be fully covered through the ensemble spread in CAM4.0 reanalysis.
Publisher: Springer Science and Business Media LLC
Date: 18-06-2022
DOI: 10.1007/S43762-022-00046-X
Abstract: The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utilized a hybrid GIS- and remote sensing imagery-based framework to systematically compare and evaluate different machine and deep learning methods. The Convolution Neural Network (CNN) classifier outperforms in terms of accuracy, but it requires multi-pixel input, which reduces the output’s spatial resolution and creates a tradeoff between accuracy and spatial resolution. The Random Forest (RF) classifier performs best among the single-pixel classifiers. This study also shows that incorporating building height dataset improves the accuracy of the high- and mid-rise classes in the RF classifiers, whereas an imperviousness dataset improves the low-rise classes. The single-pass forward permutation test reveals that both auxiliary datasets dominate the classification accuracy in the RF classifier, while near-infrared and thermal infrared are the dominating features in the CNN classifier. These findings show that the conventional LCZ classification framework used in the World Urban Database and Access Portal Tools (WUDAPT) can be improved by adopting building height and imperviousness information. This framework can be easily applied to different cities to generate LCZ maps for urban models.
Publisher: Wiley
Date: 08-2009
Publisher: American Meteorological Society
Date: 12-2008
Abstract: A regional-scale weather model is used to determine the potential for flood forecasting based on model-predicted rainfall. Extreme precipitation and flooding events are a significant concern in central Texas, due to both the high occurrence and severity of flooding in the area. However, many current regional prediction models do not provide sufficient accuracy at the watershed scale necessary for flood mitigation efforts. The Weather Research and Forecasting (WRF) model, created with the purpose of improving upon the current fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), is specifically designed for regional grid spacings of 1–10 km. Previous research by the authors resulted in the development of a regional-scale prediction system over the San Antonio River basin, using a geographic information system (GIS) database, a hydrologic model, and a hydraulic model. Observed precipitation drives the prediction system the authors hypothesize that the WRF model has the potential to predict flooding, at a lead time of several days, with a level of accuracy near that of observed precipitation. Causes of model error are also investigated, to determine the relative errors caused by model physics, initialization interval, buffer zone and domain size, and small- litude random errors. Results show that the Betts–Miller–Janjić cumulus and Lin et al. microphysics schemes, 48-h initialization interval, and two-domain configuration covering minimal ocean and having a parent-to-nest area ratio of greater than 10 best simulates a recent (July 2002) large storm event over the San Antonio River basin. This particular storm was selected because it produced extreme rainfall volumes and intensities, and also because its meteorological characteristics are typical of central Texas storm events. Location errors in rainfall are most significant because of their typically nonlinear patterns (increasing location error does not linearly modify streamflow output). Errors in intensity and timing show a more predictable (linear) watershed response that may be useful in the estimation of streamflow ranges for flood forecasting.
Publisher: Wiley
Date: 04-2009
DOI: 10.1002/JOC.1740
Publisher: American Geophysical Union (AGU)
Date: 24-09-2018
DOI: 10.1029/2018JD028588
Location: China
No related grants have been discovered for Zong-Liang Yang.