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.

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 The ERA5-Land soil temperature bias in permafrost regions

2020 ◽  
Vol 14 (8) ◽  
pp. 2581-2595 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Donghai Zheng ◽  
Xin Li
Keyword(s):  
Soil Temperature ◽  
Land Surface ◽  
Soil Column ◽  
Land Surface Model ◽  
Surface Model ◽  
Active Layer Thickness ◽  
Near Surface ◽  
Warm Bias ◽  
Temperature Bias ◽  
Permafrost Regions

Abstract. ERA5-Land (ERA5L) is a reanalysis product derived by running the land component of ERA5 at increased resolution. This study evaluates ERA5L soil temperature in permafrost regions based on observations and published permafrost products. We find that ERA5L overestimates soil temperature in northern Canada and Alaska but underestimates it in mid–low latitudes, leading to an average bias of −0.08 ∘C. The warm bias of ERA5L soil is stronger in winter than in other seasons. As calculated from its soil temperature, ERA5L overestimates active-layer thickness and underestimates near-surface (<1.89 m) permafrost area. This is thought to be due in part to the shallow soil column and coarse vertical discretization of the land surface model and to warmer simulated soil. The soil temperature bias in permafrost regions correlates well with the bias in air temperature and with maximum snow height. A review of the ERA5L snow parameterization and a simulation example both point to a low bias in ERA5L snow density as a possible cause for the warm bias in soil temperature. The apparent disagreement of station-based and areal evaluation techniques highlights challenges in our ability to test permafrost simulation models. While global reanalyses are important drivers for permafrost simulation, we conclude that ERA5L soil data are not well suited for informing permafrost research and decision making directly. To address this, future soil temperature products in reanalyses will require permafrost-specific alterations to their land surface models.

scholarly journals Representation of dissolved organic carbon in the JULES land surface model (vn4.4_JULES-DOCM)

2017 ◽  
Author(s):  
Mahdi Nakhavali ◽  
Pierre Friedlingstein ◽  
Ronny Lauerwald ◽  
Jing Tang ◽  
Sarah Chadburn ◽  
...  
Keyword(s):  
Land Surface ◽  
Soil Column ◽  
Model Performance ◽  
Land Surface Model ◽  
Terrestrial Ecosystems ◽  
Surface Model ◽  
Organic C ◽  
Run Off ◽  
C Cycle ◽  
Doc Concentration

Abstract. Current global models of the carbon (C) cycle consider only vertical gas exchanges between terrestrial or oceanic reservoirs and the atmosphere, thus not considering lateral transport of carbon from the continents to the oceans. Therefore, those models implicitly consider that all the C which is not respired to the atmosphere is stored on land, hence overestimating the land C sink capability. A model that represents the whole continuum from atmosphere to land and into the ocean would provide better understanding of the Earth's C cycle and hence more reliable historical or future projections. We present an original representation of Dissolved Organic C (DOC) processes in the Joint UK Land Environment Simulator (JULES-DOCM). The standard version of JULES represents energy, water and carbon dynamics between vegetation, soil and atmosphere, while lateral fluxes only account for water run-off. Here we integrate a representation of DOC production in terrestrial ecosystems based on incomplete decomposition of organic matter, DOC decomposition within the soil column, and DOC export to the river network via leaching. The model performance is evaluated in five specific sites for which observations of soil DOC concentration are available. Results show that the model is able to reproduce the DOC concentration and controlling processes including leaching to the riverine system which is fundamental for integrating terrestrial and aquatic ecosystems.

scholarly journals Evaluation and Transferability of the Noah Land Surface Model in Semiarid Environments

2005 ◽  
Vol 6 (1) ◽  
pp. 68-84 ◽  
Author(s):  
Terri S. Hogue ◽  
Luis Bastidas ◽  
Hoshin Gupta ◽  
Soroosh Sorooshian ◽  
Ken Mitchell ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Development ◽  
Model Performance ◽  
Land Surface Model ◽  
Time Frame ◽  
Climatic Conditions ◽  
Systematic Evaluation ◽  
Surface Model ◽  
Monsoon Period ◽  
Noah Land Surface Model

Abstract This paper investigates the performance of the National Centers for Environmental Prediction (NCEP) Noah land surface model at two semiarid sites in southern Arizona. The goal is to evaluate the transferability of calibrated parameters (i.e., direct application of a parameter set to a “similar” site) between the sites and to analyze model performance under the various climatic conditions that can occur in this region. A multicriteria, systematic evaluation scheme is developed to meet these goals. Results indicate that the Noah model is able to simulate sensible heat, ground heat, and ground temperature observations with a high degree of accuracy, using the optimized parameter sets. However, there is a large influx of moist air into Arizona during the monsoon period, and significant latent heat flux errors are observed in model simulations during these periods. The use of proxy site parameters (transferred parameter set), as well as traditional default parameters, results in diminished model performance when compared to a set of parameters calibrated specifically to the flux sites. Also, using a parameter set obtained from a longer-time-frame calibration (i.e., a 4-yr period) results in decreased model performance during nonstationary, short-term climatic events, such as a monsoon or El Niño. Although these results are specific to the sites in Arizona, it is hypothesized that these results may hold true for other case studies. In general, there is still the opportunity for improvement in the representation of physical processes in land surface models for semiarid regions. The hope is that rigorous model evaluation, such as that put forth in this analysis, and studies such as the Project for the Intercomparison of Land-Surface Processes (PILPS) San Pedro–Sevilleta, will lead to advances in model development, as well as parameter estimation and transferability, for use in long-term climate and regional environmental studies.

scholarly journals Does predictability of fluxes vary between FLUXNET sites?

2018 ◽  
Author(s):  
Ned Haughton ◽  
Gab Abramowitz ◽  
Martin G. De Kauwe ◽  
Andy J. Pitman
Keyword(s):  
Land Surface ◽  
Performance Metrics ◽  
A Priori ◽  
Model Development ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Individual Site ◽  
Site Characteristics ◽  
Development Evaluation

Abstract. The FLUXNET dataset contains eddy covariance measurements from across the globe, and represents an invaluable estimate of the fluxes of energy, water and carbon between the land surface and the atmosphere. While there is an expectation that the broad range of site characteristics in FLUXNET result in a diversity of flux behaviour, there has been little exploration of how predictable site behaviour is across the network. Aside from intrinsic interest in this fundamental question, understanding site predictability would be useful for land surface model (LSM) evaluation in setting a priori expectations of model performance. It would also provide a clear rationale for selecting particular FLUXNET sites for model development, evaluation and benchmarking. Here, 155 datasets with 30 minute temporal resolution from the Tier 1 of FLUXNET2015 were analysed in a first attempt to assess individual site predictability. Predictability was defined using the disparity between the ability to simulate fluxes at a site given specific knowledge of the site, and the ability to simulate fluxes given general land surface specifications. We then examined predictability using performance metrics including RMSE, correlation, and probability density overlap, and defined site uniqueness as the disparity between multiple empirical models trained globally and locally for each site. A number of hypotheses potentially explaining site predictability were then tested, including climatology, data quality and site characteristics. We found very few clear predictors of uniqueness across different sites including little evidence that flux behaviour is well discretised by vegetation types. While this result might relate to our definition of uniqueness, we argue that our approach is sound and provides a useful basis for site selection in LSM evaluation.

Underestimated snow density in ERA5-Land as a cause for warm bias of soil temperature over permafrost regions

2021 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Donghai Zheng ◽  
Xin Li
Keyword(s):  
Soil Temperature ◽  
Land Surface ◽  
Soil Column ◽  
Land Surface Model ◽  
Surface Model ◽  
Active Layer Thickness ◽  
Snow Density ◽  
Near Surface ◽  
Warm Bias ◽  
Permafrost Regions

&lt;div&gt; &lt;p&gt;ERA5 is the latest generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5-Land (ERA5L) is derived by running the land component of ERA5, Tiled ECMWF Scheme for Surface Exchanges over Land with a revised land surface hydrology (HTESSEL), at an increased resolution of 0.1&amp;#176;. This study evaluates ERA5L soil temperature in permafrost regions based on observations and published permafrost products. We find that ERA5L overestimates soil temperature in northern Canada and Alaska but underestimates it in mid&amp;#8211;low latitudes, leading to a near-zero overall bias (&amp;#8722;0.08 &amp;#730;C). The warm bias of ERA5L soil is more pronounced in winter than in other seasons. As calculated from its soil temperature, ERA5L overestimates active-layer thickness and underestimates near-surface (&lt; 1.89 m) permafrost area.This is thought to be due in part to the shallow soil column and coarse vertical discretization of the land surface model and to warmer simulated soil.&lt;/p&gt; &lt;p&gt;The soil temperature bias in permafrost regions correlates well with the bias in air temperature and with snow height. A review of the ERA5L snow parameterization in the code and a simulation example comparison with permafrost-specific processes rich model (GEOtop) both point to an error in snow metamorphism of HTESSEL leading to a low bias in ERA5L snow density as a possible cause for the warm bias in soil temperature. The apparent disagreement of station-based and areal evaluation techniques highlights challenges in our ability to test permafrost simulation models. While global reanalyses are important drivers for permafrost simulation, we conclude that ERA5L soil data are not well suited for informing permafrost research and decision making directly. To address this, future soil temperature products in reanalyses will require permafrost-specific alterations to their land surface models.&lt;/p&gt; &lt;p&gt;&lt;/p&gt;&lt;/div&gt;

Modifications to the Rapid Update Cycle Land Surface Model (RUC LSM) Available in the Weather Research and Forecasting (WRF) Model

2016 ◽  
Vol 144 (5) ◽  
pp. 1851-1865 ◽  
Author(s):  
Tatiana G. Smirnova ◽  
John M. Brown ◽  
Stanley G. Benjamin ◽  
Jaymes S. Kenyon
Keyword(s):  
Land Surface ◽  
Model Development ◽  
Lower Boundary ◽  
Wrf Model ◽  
Land Surface Model ◽  
Arctic Sea Ice ◽  
Surface Model ◽  
Development Effort ◽  
Near Surface ◽  
Environmental Prediction

The land surface model (LSM) described in this manuscript was originally developed as part of the NOAA Rapid Update Cycle (RUC) model development effort; with ongoing modifications, it is now used as an option for the WRF community model. The RUC model and its WRF-based NOAA successor, the Rapid Refresh (RAP), are hourly updated and have an emphasis on short-range, near-surface forecasts including aviation-impact variables and preconvective environment. Therefore, coupling to this LSM (hereafter the RUC LSM) has been critical to provide more accurate lower boundary conditions. This paper describes changes made to the RUC LSM since earlier descriptions, including extension from six to nine levels, improved snow treatment, and new land-use data from MODIS. The RUC LSM became operational at the NOAA/National Centers for Environmental Prediction (NCEP) as part of the RUC from 1998–2012 and as part of the RAP from 2012 through the present. The simple treatments of basic land surface processes in the RUC LSM have proven to be physically robust and capable of realistically representing the evolution of soil moisture, soil temperature, and snow in cycled models. Extension of the RAP domain to encompass all of North America and adjacent high-latitude ocean areas necessitated further development of the RUC LSM for application in the tundra permafrost regions and over Arctic sea ice. Other modifications include refinements in the snow model and a more accurate specification of albedo, roughness length, and other surface properties. These recent modifications in the RUC LSM are described and evaluated in this paper.

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

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

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

scholarly journals 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

&lt;p&gt;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 &amp;#8212; 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 &amp;#8212; the micro-genetic algorithm (micro-GA) &amp;#8212; 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.&lt;/p&gt;

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