Bounding the parameters of land-surface schemes using observational data

2001 ◽  
pp. 65-76 ◽  
Author(s):  
Luis A. Bastidas ◽  
Hoshin V. Gupta ◽  
Soroosh Sorooshian
Keyword(s):  
Observational Data ◽  
Land Surface

scholarly journals Land Surface Temperature Variation Following the 2017 Mw 7.3 Iran Earthquake

2019 ◽  
Vol 11 (20) ◽  
pp. 2411
Author(s):  
Zhu ◽  
Jiao ◽  
Shan ◽  
Zhang ◽  
Li
Keyword(s):  
Numerical Simulation ◽  
Surface Temperature ◽  
Temperature Variation ◽  
Observational Data ◽  
Land Surface Temperature ◽  
Crustal Deformation ◽  
Land Surface ◽  
Projection Area ◽  
Fluid Migration ◽  
Seismic Slip

During an earthquake, crustal deformation, fluid flow, and temperature variation are coupled; however, earthquake-related land surface temperature (LST) variations remain unclear. To determine whether post-seismic fluid migration can cause changes in LST, and taking the Mw 7.3 2017 Iran earthquake as an example, we modeled surface cooling (CA) and warming (WA) areas induced by co-seismic slip and fluid migration using a thermo-hydro-mechanical (THM) coupled numerical simulation. Moreover, using nighttime LST data with 15-min resolution, the daily attenuation coefficient k of nighttime LST was extracted by attenuation function fitting, and the trend of the k time series was analyzed using the Mann–Kendall and Sen’s methods. Based on the comparison of k trends between the post-seismic and 2010–2016 periods, we obtained cooling and warming trends for the modeled CA and WA. The numerical simulation and observational data show good consistency, and both indicate that fluid migration caused by crustal deformation can lead to changes in LST. The numerical simulations show that after the Iran earthquake, the surface projection area of co-seismic slip correlated with a cooling area (CA), while the surrounding area correlated with a warming area (WA). For the LST observational data, the post-seismic k trends of the calculated CA and WA are positive and negative, indicating sustained cooling and warming processes, respectively. This study provides evidence that LST variation is caused by co-seismic crustal deformation and fluid migration and reveals the coupled evolution of deformation, fluid, and temperature fields. The results provide new insights into the mechanisms of seismic thermal anomalies.

Evaluation of the WRF Physics Ensemble using Multivariable Integrated Evaluation Approach over Haihe river basin in north China

2020 ◽  
Author(s):  
Danqiong Dai
Keyword(s):  
Vector Field ◽  
Observational Data ◽  
River Basin ◽  
Land Surface ◽  
Regional Climate ◽  
Field Evaluation ◽  
Shortwave Radiation ◽  
Haihe River Basin ◽  
Spatial Average ◽  
Haihe River

<p>  A crucial step of the application of WRF in regional climate research is selection of the proper combinations of physical parameterizations. In this study, we performed experiments in WRF to assess the predict skill of various parametrization schemes sets in simulating precipitation, temperature over the Haihe river basin. The experiments driven by ERA-INTERIM reanalysis data are performed for a period of summer (1 June to 31 August, 2016) in this domain with 13 km grid spacing. Fifty-eight members of physics combinations thoroughly covering five types of physics options are assessed against the available observational data by utilizing the multivariable integrated evaluation (MVIE) method. It is deduced that the best performing setup consists of CAM5.1 microphysics, MRF PBL, BMJ Cumulus, CAM Longwave/Shortwave radiation, and Noah Land Surface schemes. To identify the robustness of the optimal scheme set, the vector field evaluation (VFE) diagram for displaying all simulations reveals that the optimal one is distinguished from others by higher vector field similarity coefficient(Rν), smaller root mean square vector deviation(RMSVD). The model deviations spatially for the precipitation show a promising tendency that a strong overestimation about 5 mm/day for the default configuration evolves small biases of the optimal setup with a range between -1 and 1 mm/day, and the surface temperature forecasts have improved to some extent although not significant as that of precipitation. The temporally analysis of the spatial average of all simulations exhibits that for temperature the optimal setup is more approaching to the observational data, but for precipitation no remarkable difference between all simulation and the observations. Further analysis of the sensitivities of model output to different types of physics option suggests that, microphysics, PBL, and Cumulus schemes have more significant impact on the model performances measured by a multivariable integrated evaluation index (MIEI) than radiation scheme and Land Surface schemes.</p>

scholarly journals A 16-year record (2002–2017) of permafrost, active layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River Delta, northern Siberia: an opportunity to validate remote sensing data and land surface, snow, and permafrost models

2018 ◽  
Author(s):  
Julia Boike ◽  
Jan Nitzbon ◽  
Katharina Anders ◽  
Mikhail Grigoriev ◽  
Dmitry Bolshiyanov ◽  
...  
Keyword(s):  
Energy Balance ◽  
Observational Data ◽  
Land Surface ◽  
Laser Scanning ◽  
Landscape Heterogeneity ◽  
The Arctic ◽  
Annual Rainfall ◽  
Data Set ◽  
Northern Siberia ◽  
Research Site

Abstract. Most of the world's permafrost is located in the Arctic, where its frozen organic carbon con-tent makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere is a positive feed-back mechanism that has the potential for climate warming. It is therefore particularly im-portant to understand the links between the energy balance, which can vary rapidly over hour-ly to annual time scales, and permafrost condition, which changes slowly on decadal to cen-tennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, pro-cessing and data quality control. Additional data include a high-resolution digital terrain mod-el (DTM) obtained from terrestrial LiDAR laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active lay-er soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon center, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modeling. For the record from 1998 to 2017, the average mean annual air temperature was − 12.3 °C, with mean monthly temperature of the warmest month (July) recorded as 9.5 °C and for the coldest month (February) − 32.7 °C. The average annual rainfall was 169 mm. The depth of zero annual amplitude niveau is at 20.8 m, and has warmed from − 9.1 °C in 2006 to − 7.7 °C in 2017. The presented data are available in the supplementary material of this paper and through the PANGAEA website (https://doi.org/10.1594/PANGAEA.891142).

scholarly journals A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models

2019 ◽  
Vol 11 (1) ◽  
pp. 261-299 ◽  
Author(s):  
Julia Boike ◽  
Jan Nitzbon ◽  
Katharina Anders ◽  
Mikhail Grigoriev ◽  
Dmitry Bolshiyanov ◽  
...  
Keyword(s):  
Energy Balance ◽  
Observational Data ◽  
Active Layer ◽  
Land Surface ◽  
The Arctic ◽  
Annual Rainfall ◽  
Organic Carbon Content ◽  
Data Set ◽  
Northern Siberia ◽  
Research Site

Abstract. Most of the world's permafrost is located in the Arctic, where its frozen organic carbon content makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere, as well as snow cover changes, are positive feedback mechanisms that have the potential for climate warming. It is therefore particularly important to understand the links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost conditions, which changes slowly on decadal to centennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, processing, and data quality control. Furthermore, we present a merged data set of the parameters, which were measured from 1998 onwards. Additional data include a high-resolution digital terrain model (DTM) obtained from terrestrial lidar laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active-layer soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon centre, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modelling. For the record from 1998 to 2017, the average mean annual air temperature was −12.3 ∘C, with mean monthly temperature of the warmest month (July) recorded as 9.5 ∘C and for the coldest month (February) −32.7 ∘C. The average annual rainfall was 169 mm. The depth of zero annual amplitude is at 20.75 m. At this depth, the temperature has increased from −9.1 ∘C in 2006 to −7.7 ∘C in 2017. The presented data are freely available through the PANGAEA (https://doi.org/10.1594/PANGAEA.891142) and Zenodo (https://zenodo.org/record/2223709, last access: 6 February 2019) websites.

Hydroclimatological evaluation of CLM5 simulations using multiple data sources for land-atmosphere interaction studies over Scandinavia

2020 ◽  
Author(s):  
Yeliz A. Yılmaz ◽  
Lena M. Tallaksen ◽  
Frode Stordal
Keyword(s):  
Observational Data ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Surface Model ◽  
Data Sets ◽  
Earth System ◽  
Data Set ◽  
Cold Environments ◽  
Community Land Model ◽  
Terrestrial Water

<p>Arctic amplification leads to rapid changes in the terrestrial water and energy balances at high northern latitudes. Advances in Earth System Models (ESMs) is improving our understanding of the underlying feedback mechanisms leading to these changes. The representation of the land surface in ESMs is essential to simulate and understand changes at the global and regional scales. The latest version of the land component of the Norwegian Earth System Model (NorESM), namely the Community Land Model (CLM5), has received substantial new implementations to help simulate the land surface processes in cold environments. At the same time, the behaviour of offline CLM5 simulations and new observational data sets have not been systematically compared over Scandinavian regions. In this study, we run the CLM5 model at relatively high resolution (0.25 degrees) over Scandinavia (including Svalbard) for 15 years between 2002 and 2016. We evaluate the water and energy budget components of CLM5 using several reanalyses and satellite-based observational data sets. In particular, we use monthly model outputs and compare with the satellite retrievals from GRACE, MODIS, AMSR2, and AMSR-E, and reanalysis data sets from ERA5, GLDAS, and MERRA-2. As an additional data source, we use the local‐scale measurements obtained from the Finse Eco-Hydrological Observatory (Finse EcHO) at 1200 m a.s.l, and the high-Arctic research site at Bayelva near Ny-Ålesund, Svalbard. Our investigation is focused on several variables including terrestrial water storage, snow water equivalent, turbulent fluxes, net radiation, and skin temperature. The results indicate that the perceived performance of the land surface model (CLM5) depends strongly on the reference observational data set. Regional discrepancies between data sets, particularly for Svalbard, prompts further investigation of the underlying sources of uncertainty. The results of this evaluation provide a valuable source of information for future studies in the region, particularly in the Land-ATmosphere Interactions in Cold Environments (LATICE) project, which focuses on cold region land surface dynamics, integrating across observational systems, laboratory experiments, field, and modeling efforts.</p><p>Acknowledgement : This study is conducted under the LATICE strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo, and the project EMERALD (294948) funded by the Research Council of Norway.</p>

scholarly journals Constraining Land Surface and Atmospheric Parameters of a Locally Coupled Model Using Observational Data

2005 ◽  
Vol 6 (2) ◽  
pp. 156-172 ◽  
Author(s):  
Yuqiong Liu ◽  
Hoshin V. Gupta ◽  
Soroosh Sorooshian ◽  
Luis A. Bastidas ◽  
William J. Shuttleworth
Keyword(s):  
Parameter Estimation ◽  
Observational Data ◽  
Land Surface ◽  
Climate Model ◽  
Model Performance ◽  
Computational Time ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Atmospheric Parameters ◽  
Estimation Process

Abstract In coupled land surface–atmosphere modeling, the possibility and benefits of constraining model parameters using observational data bear investigation. Using the locally coupled NCAR Single-column Community Climate Model (NCAR SCCM), this study demonstrates some feasible, effective approaches to constrain parameter estimates for coupled land–atmosphere models and explores the effects of including both land surface and atmospheric parameters and fluxes/variables in the parameter estimation process, as well as the value of conducting the process in a stepwise manner. The results indicate that the use of both land surface and atmospheric flux variables to construct error criteria can lead to better-constrained parameter sets. The model with “optimal” parameters generally performs better than when a priori parameters are used, especially when some atmospheric parameters are included in the parameter estimation process. The overall conclusion is that, to achieve balanced, reasonable model performance on all variables, it is desirable to optimize both land surface and atmospheric parameters and use both land surface and atmospheric fluxes/variables for error criteria in the optimization process. The results also show that, for a coupled land–atmosphere model, there are potential advantages to using a stepwise procedure in which the land surface parameters are first identified in offline mode, after which the atmospheric parameters are determined in coupled mode. This stepwise scheme appears to provide comparable solutions to a fully coupled approach, but with considerably reduced computational time. The trade-off in the ability of a model to satisfactorily simulate different processes simultaneously, as observed in most multicriteria studies, is most evident for sensible heat and precipitation in this study for the NCAR SCCM.

scholarly journals Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures

2016 ◽  
Vol 9 (6) ◽  
pp. 2077-2098 ◽  
Author(s):  
Duncan Ackerley ◽  
Dietmar Dommenget
Keyword(s):  
Surface Temperature ◽  
Observational Data ◽  
Land Surface ◽  
General Circulation ◽  
Global Ocean ◽  
Data Sets ◽  
Global Circulation ◽  
Sensitivity Experiments ◽  
Surface Temperatures ◽  
Land Surface Temperatures

Abstract. General circulation models (GCMs) are valuable tools for understanding how the global ocean–atmosphere–land surface system interacts and are routinely evaluated relative to observational data sets. Conversely, observational data sets can also be used to constrain GCMs in order to identify systematic errors in their simulated climates. One such example is to prescribe sea surface temperatures (SSTs) such that 70 % of the Earth's surface temperature field is observationally constrained (known as an Atmospheric Model Intercomparison Project, AMIP, simulation). Nevertheless, in such simulations, land surface temperatures are typically allowed to vary freely, and therefore any errors that develop over the land may affect the global circulation. In this study therefore, a method for prescribing the land surface temperatures within a GCM (the Australian Community Climate and Earth System Simulator, ACCESS) is presented. Simulations with this prescribed land surface temperature model produce a mean climate state that is comparable to a simulation with freely varying land temperatures; for example, the diurnal cycle of tropical convection is maintained. The model is then developed further to incorporate a selection of “proof of concept” sensitivity experiments where the land surface temperatures are changed globally and regionally. The resulting changes to the global circulation in these sensitivity experiments are found to be consistent with other idealized model experiments described in the wider scientific literature. Finally, a list of other potential applications is described at the end of the study to highlight the usefulness of such a model to the scientific community.

scholarly journals Comparison of Simulated Spatial Patterns Using Rain Gauge and Polarimetric-Radar-Based Precipitation Data in Catchment Hydrological Modeling

2018 ◽  
Vol 19 (8) ◽  
pp. 1273-1288 ◽  
Author(s):  
Xin He ◽  
Julian Koch ◽  
Chunmiao Zheng ◽  
Thomas Bøvith ◽  
Karsten H. Jensen
Keyword(s):  
Observational Data ◽  
Spatial Patterns ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Time Series Data ◽  
Function Analysis ◽  
Evaluation Criteria ◽  
Radar Data ◽  
Rain Gauge ◽  
Series Data

Abstract With the advance of the weather radar technology, dual-polarization (dual-pol) radar data are now available for hydrological studies, which go beyond the traditional rainfall products relying purely on rain gauge data. Previous studies have focused on the evaluation of rainfall products and their hydrological responses using point-based observational data; however, spatial patterns of simulated hydrological variables are equally important to be considered in order to fully address the distributed effect of the precipitation estimates. In the present study, we compare three rainfall estimations based on rain gauge, single-polarization, and dual-pol radar data. Special attention is given to the use of the two radar products and their corresponding hydrological simulations of both surface water and groundwater. Performance of the hydrological simulations is evaluated based first on traditional point-based observations of stream discharge and groundwater head, and second on remotely sensed land surface temperature data. For the latter, the empirical orthogonal function analysis, which quantifies spatial pattern similarities, is employed. The Skjern River catchment in western Denmark is selected as the study site, and the results show that all three models perform equally well in terms of the traditional aggregated evaluation criteria, such as Nash–Sutcliffe efficiency (NSE) and RMSE on time series data. It is found that the differences of simulated hydrological spatial patterns are sensitive to rainfall signal intensity, as well as the simulation scale in space (<100 km2) and time (subdaily). Our study suggests that the currently available observational data have limited capabilities to clearly differentiate the performance of the three applied models due to the low resolution.

scholarly journals Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

2016 ◽  
Vol 17 (4) ◽  
pp. 1049-1067 ◽  
Author(s):  
Paul A. Dirmeyer ◽  
Jiexia Wu ◽  
Holly E. Norton ◽  
Wouter A. Dorigo ◽  
Steven M. Quiring ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Observational Data ◽  
Temporal Variability ◽  
Land Surface ◽  
Large Scale ◽  
Heat Dissipation ◽  
Measurement Techniques ◽  
The United States ◽  
Spatial And Temporal Variability

Abstract Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

Current status of special planetary and lunar theories and ephemerides

1966 ◽  
Vol 25 ◽  
pp. 266-267
Author(s):  
R. L. Duncombe
Keyword(s):  
Observational Data ◽  
Current Status ◽  
Numerical Errors

An examination of some specialized lunar and planetary ephemerides has revealed inconsistencies in the adopted planetary masses, the presence of non-gravitational terms, and some outright numerical errors. They should be considered of temporary usefulness only, subject to subsequent amendment as required for the interpretation of observational data.

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