Meteorological Impacts of Forest Mortality due to Insect Infestation in Colorado

2012 ◽  
Vol 16 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Christine Wiedinmyer ◽  
Michael Barlage ◽  
Mukul Tewari ◽  
Fei Chen
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Sensible Heat Flux ◽  
Surface Model ◽  
Multiple Model ◽  
Insect Infestation ◽  
Meteorological Model ◽  
Near Surface ◽  
Forest Mortality ◽  
Forest Dieback

Abstract Physical characteristics of forests and other ecosystems control land–atmosphere exchanges of water and energy and partly dictate local and regional meteorology. Insect infestation and resulting forest dieback can alter these characteristics and, further, modify land–atmosphere exchanges. In the past decade, insect infestation has led to large-scale forest mortality in western North America. This study uses a high-resolution mesoscale meteorological model coupled with a detailed land surface model to investigate the sensitivity of near-surface variables to insect-related forest mortality. The inclusion of this land surface disturbance in the model increased in simulated skin temperature by as much as 2.1 K. The modeled 2-m temperature increased an average of 1 K relative to the default simulations. A latent to sensible heat flux shift with a magnitude of 10%–15% of the available energy in the forested ecosystem was predicted after the inclusion of insect infestation and forest dieback. Although results were consistent across multiple model configurations, the characteristics of forests affected by insect infestations must be better constrained to more accurately predict their impacts. Despite the limited duration of the simulations (one week), these initial results suggest the importance of including large-scale forest mortality due to insect infestation in meteorological models and highlight the need for better observations of the characteristics and exchanges of these disturbed landscapes.

scholarly journals Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

2019 ◽  
Author(s):  
Renaud Hostache ◽  
Dominik Rains ◽  
Kaniska Mallick ◽  
Marco Chini ◽  
Ramona Pelich ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Model ◽  
Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

Evaluating Enhanced Hydrological Representations in Noah LSM over Transition Zones: Implications for Model Development

2009 ◽  
Vol 10 (3) ◽  
pp. 600-622 ◽  
Author(s):  
Enrique Rosero ◽  
Zong-Liang Yang ◽  
Lindsey E. Gulden ◽  
Guo-Yue Niu ◽  
David J. Gochis
Keyword(s):  
Land Surface ◽  
Soil Column ◽  
Model Development ◽  
Model Performance ◽  
Superior Performance ◽  
Surface Model ◽  
Multiple Model ◽  
Near Surface ◽  
Transition Zones ◽  
Soil Wetness

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.

scholarly journals Sensitivity of the regional European boreal climate to changes in surface properties resulting from structural vegetation perturbations

2015 ◽  
Vol 12 (10) ◽  
pp. 3071-3087 ◽  
Author(s):  
J. H. Rydsaa ◽  
F. Stordal ◽  
L. M. Tallaksen
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Structural Changes ◽  
Mixed Forest ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Energy Partitioning ◽  
The Arctic ◽  
Surface Model ◽  
Near Surface

Abstract. Amplified warming at high latitudes over the past few decades has led to changes in the boreal and Arctic climate system such as structural changes in high-latitude ecosystems and soil moisture properties. These changes trigger land–atmosphere feedbacks through altered energy partitioning in response to changes in albedo and surface water fluxes. Local-scale changes in the Arctic and boreal zones may propagate to affect large-scale climatic features. In this study, MODIS land surface data are used with the Weather Research and Forecasting model (WRF V3.5.1) and Noah land surface model (LSM), in a series of experiments to investigate the sensitivity of the overlying atmosphere to perturbations in the structural vegetation in the northern European boreal ecosystem. Emphasis is placed on surface energy partitioning and near-surface atmospheric variables, and their response to observed and anticipated land cover changes. We find that perturbations simulating northward migration of evergreen needleleaf forest into tundra regions cause an increase in latent rather than sensible heat fluxes during the summer season. Shrub expansion in tundra areas has only small effects on surface fluxes. Perturbations simulating the northward migration of mixed forest across the present southern border of the boreal forest, have largely opposite effects on the summer latent heat flux, i.e., they lead to a decrease and act to moderate the overall mean regional effects of structural vegetation changes on the near-surface atmosphere.

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

Development of stochastically perturbed parameterization scheme for the Noah Land Surface Model with the optimized random forcing parameters using the micro-genetic algorithm

2021 ◽  
Author(s):  
Sujeong Lim ◽  
Claudio Cassardo ◽  
Seon Ki Park
Keyword(s):  
Genetic Algorithm ◽  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Surface Model ◽  
Ensemble Spread ◽  
Near Surface ◽  
Random Forcing ◽  
Micro Genetic Algorithm ◽  
Stochastically Perturbed

<p>The ensemble data assimilation system is beneficial to represent the initial uncertainties and flow-dependent background error covariance (BEC). In particular, the inevitable model uncertainties can be expressed by ensemble spread, that is the standard deviation of ensemble BEC. However, the ensemble spread generally suffers from under-estimated problems. To alleviate this problem, recent studies employed stochastic perturbation schemes to increases the ensemble spreads by adding the random forcing in the model tendencies (i.e., physical or dynamical tendencies) or parameterization schemes (i.e., PBL, convective scheme, etc.). In this study, we focus on the near-surface uncertainties which are affected by the interactions between the land and atmosphere process. The land surface model (LSM) provides various fluxes as the lower boundary condition to the atmosphere, influencing the accuracy of hourly-to-seasonal scale weather forecasting, but the surface uncertainties were not much addressed yet. In this study, we developed the stochastically perturbed parameterization (SPP) scheme for the Noah LSM. The Weather Research and Forecasting (WRF) ensemble system is used for regional weather forecasting over East Asia, especially over the Korean Peninsula. As a testbed experiment with the newly-developed Noah LSM-SPP system, we first perturbed the soil temperature — a crucial variable for the near-surface forecasts by affecting sensible heat fluxes, land surface skin temperature and surface air temperature, and hence lower-tropospheric temperature. Here, the random forcing used in perturbation is made by the tuning parameters for amplitude, length scale, and time scales: they are commonly determined empirically by trial and error. In order to find optimal tuning parameter values, we applied a global optimization algorithm — the micro-genetic algorithm (micro-GA) — to achieve the smallest root-mean-squared errors. Our results indicate that optimization of the random forcing parameters contributes to an increase in the ensemble spread and a decrease in the ensemble mean errors in the near-surface and lower-troposphere uncertainties. Further experiments will be conducted by including soil moisture in the testbed.</p>

scholarly journals Continuous high-resolution midlatitude-belt simulations for July–August 2013 with WRF

2017 ◽  
Vol 10 (5) ◽  
pp. 2031-2055 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Kirsten Warrach-Sagi
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Temperature Fields ◽  
Surface Model ◽  
Limited Area ◽  
Data Set ◽  
Latitude Belt ◽  
Simulated Precipitation ◽  
The Impact

Abstract. Increasing computational resources and the demands of impact modelers, stake holders, and society envision seasonal and climate simulations with the convection-permitting resolution. So far such a resolution is only achieved with a limited-area model whose results are impacted by zonal and meridional boundaries. Here, we present the setup of a latitude-belt domain that reduces disturbances originating from the western and eastern boundaries and therefore allows for studying the impact of model resolution and physical parameterization. The Weather Research and Forecasting (WRF) model coupled to the NOAH land–surface model was operated during July and August 2013 at two different horizontal resolutions, namely 0.03 (HIRES) and 0.12° (LOWRES). Both simulations were forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data at the northern and southern domain boundaries, and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface.The simulations are compared to the operational ECMWF analysis for the representation of large-scale features. To analyze the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used as references.Analyzing pressure, geopotential height, wind, and temperature fields as well as precipitation revealed (1) a benefit from the higher resolution concerning the reduction of monthly biases, root mean square error, and an improved Pearson skill score, and (2) deficiencies in the physical parameterizations leading to notable biases in distinct regions like the polar Atlantic for the LOWRES simulation, the North Pacific, and Inner Mongolia for both resolutions.In summary, the application of a latitude belt on a convection-permitting resolution shows promising results that are beneficial for future seasonal forecasting.

scholarly journals High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

2020 ◽  
Author(s):  
Benjamin Fersch ◽  
Alfonso Senatore ◽  
Bianca Adler ◽  
Joël Arnault ◽  
Matthias Mauder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Weather Prediction ◽  
Surface Model ◽  
Near Surface ◽  
Ground Heat Flux ◽  
The Impact ◽  
Fully Coupled ◽  
Regional Water

<p>The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. We examine the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assess the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the pre-Alpine Terrestrial Environmental Observatory (TERENO Pre-Alpine) for the Ammer (600 km²) and Rott (55 km²) river catchments in southern Germany, covering a five month period (Jun–Oct 2016).</p><p>The sensitivity of 7 land surface parameters is tested using the <em>Latin-Hypercube One-factor-At-a-Time</em> (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent <em>Parameter Estimation and Uncertainty Analysis software</em> (PEST).</p><p>The calibration of the offline WRF-Hydro leads to Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro shows only tiny alterations for radiation and precipitation but considerable changes for moisture- and energy fluxes. By comparison with TERENO Pre-Alpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.</p>

scholarly journals Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

2011 ◽  
Vol 8 (2) ◽  
pp. 2555-2608 ◽  
Author(s):  
E. H. Sutanudjaja ◽  
L. P. H. van Beek ◽  
S. M. de Jong ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Large Scale ◽  
Groundwater Modeling ◽  
Land Surface Model ◽  
Water Levels ◽  
Surface Model ◽  
Groundwater Model ◽  
Model Output ◽  
Global Datasets

Abstract. Large-scale groundwater models involving aquifers and basins of multiple countries are still rare due to a lack of hydrogeological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Although the method that we used to couple the land surface and MODFLOW groundwater model is considered as an offline-coupling procedure (i.e. the simulations of both models were performed separately), results are promising. The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydrogeological parameter settings, we observe that the model can reproduce the observed groundwater head time series reasonably well. However, we note that there are still some limitations in the current approach, specifically because the current offline-coupling technique simplifies dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

scholarly journals Simulating the Effects of Land Surface Characteristics on Planetary Boundary Layer Parameters for a Modeled Landfalling Tropical Cyclone

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 138
Author(s):  
Yu Wang ◽  
Corene J. Matyas
Keyword(s):  
Tropical Cyclone ◽  
Land Surface ◽  
Friction Velocity ◽  
Inner Core ◽  
Sensible Heat Flux ◽  
Surface Characteristics ◽  
Near Surface ◽  
Land Surfaces ◽  
Rain Rates ◽  
Land Surface Characteristics

This study examined whether varying moisture availability and roughness length for the land surface under a simulated Tropical Cyclone (TC) could affect its production of precipitation. The TC moved over the heterogeneous land surface of the southeastern U.S. in the control simulation, while the other simulations featured homogeneous land surfaces that were wet rough, wet smooth, dry rough, and dry smooth. Results suggest that the near-surface atmosphere was modified by the changes to the land surface, where the wet cases have higher latent and lower sensible heat flux values, and rough cases exhibit higher values of friction velocity. The analysis of areal-averaged rain rates and the area receiving low and high rain rates shows that simulations having a moist land surface produce higher rain rates and larger areas of low rain rates in the TC’s inner core. The dry and rough land surfaces produced a higher coverage of high rain rates in the outer regions. Key differences among the simulations happened as the TC core moved over land, while the outer rainbands produced more rain when moving over the coastline. These findings support the assertion that the modifications of the land surface can influence precipitation production within a landfalling TC.

scholarly journals Presentation and discussion of the high resolution atmosphere-land surface subsurface simulation dataset of the virtual Neckar catchment for the period 2007–2015

2020 ◽  
Author(s):  
Bernd Schalge ◽  
Gabriele Baroni ◽  
Barbara Haese ◽  
Daniel Erdal ◽  
Gernot Geppert ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Overland Flow ◽  
Water Cycle ◽  
Numerical Models ◽  
Model Performance ◽  
Surface Model ◽  
Meteorological Model ◽  
The Real ◽  
Terrestrial System

Abstract. Coupled numerical models, which simulate water and energy fluxes in the subsurface-land surface-atmosphere system in a physically consistent way are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a virtual reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform TerrSysMP, which couples the meteorological model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to generate a virtual catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany. Simulations for this catchment are made for the period 2007–2015, and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modelling challenges, the model performance is evaluated based on real observations covering several variables of the water cycle. We find that the simulated (virtual) catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g. concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement such as overland flow, and in the realism of some observation operators like the satellite based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar_VCS_v1.

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