scholarly journals Simulating carbon and water cycles of larch forests in East Asia by the BIOME-BGC model with AsiaFlux data

2010 ◽  
Vol 7 (3) ◽  
pp. 959-977 ◽  
Author(s):  
M. Ueyama ◽  
K. Ichii ◽  
R. Hirata ◽  
K. Takagi ◽  
J. Asanuma ◽  
...  
Keyword(s):  
Air Temperature ◽  
Ecosystem Respiration ◽  
Spatial Scales ◽  
Model Performance ◽  
Limiting Factors ◽  
Model Parameters ◽  
Total Precipitation ◽  
Water Conditions ◽  
Larch Forests ◽  
Water Cycles

Abstract. Larch forests are widely distributed across many cool-temperate and boreal regions, and they are expected to play an important role in global carbon and water cycles. Model parameterizations for larch forests still contain large uncertainties owing to a lack of validation. In this study, a process-based terrestrial biosphere model, BIOME-BGC, was tested for larch forests at six AsiaFlux sites and used to identify important environmental factors that affect the carbon and water cycles at both temporal and spatial scales. The model simulation performed with the default deciduous conifer parameters produced results that had large differences from the observed net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (RE), and evapotranspiration (ET). Therefore, we adjusted several model parameters in order to reproduce the observed rates of carbon and water cycle processes. This model calibration, performed using the AsiaFlux data, substantially improved the model performance. The simulated annual GPP, RE, NEE, and ET from the calibrated model were highly consistent with observed values. The observed and simulated GPP and RE across the six sites were positively correlated with the annual mean air temperature and annual total precipitation. On the other hand, the simulated carbon budget was partly explained by the stand disturbance history in addition to the climate. The sensitivity study indicated that spring warming enhanced the carbon sink, whereas summer warming decreased it across the larch forests. The summer radiation was the most important factor that controlled the carbon fluxes in the temperate site, but the VPD and water conditions were the limiting factors in the boreal sites. One model parameter, the allocation ratio of carbon between belowground and aboveground, was site-specific, and it was negatively correlated with the annual climate of annual mean air temperature and total precipitation. Although this study substantially improved the model performance, the uncertainties that remained in terms of the sensitivity to water conditions should be examined in ongoing and long-term observations.

scholarly journals Simulating carbon and water cycles of larch forests in East Asia by the BIOME-BGC model with AsiaFlux data

2009 ◽  
Vol 6 (4) ◽  
pp. 8311-8357 ◽  
Author(s):  
M. Ueyama ◽  
K. Ichii ◽  
R. Hirata ◽  
K. Takagi ◽  
J. Asanuma ◽  
...  
Keyword(s):  
Air Temperature ◽  
Ecosystem Respiration ◽  
Spatial Scales ◽  
Model Performance ◽  
Limiting Factors ◽  
Model Parameters ◽  
Total Precipitation ◽  
Water Conditions ◽  
Larch Forests ◽  
Water Cycles

Abstract. Larch forests are widely distributed across many cool-temperate and boreal regions, and they are expected to play an important role in global carbon and water cycles. Model parameterizations for larch forests still contain large uncertainties owing to a lack of validation. In this study, a process-based terrestrial biosphere model, BIOME-BGC, was tested for larch forests at six AsiaFlux sites and used to identify important environmental factors that affect the carbon and water cycles at both temporal and spatial scales. The model simulation performed with the default deciduous conifer parameters produced results that had large differences from the observed net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (RE), and evapotranspiration (ET). Therefore, we adjusted several model parameters in order to reproduce the observed rates of carbon and water cycle processes. This model calibration, performed using the AsiaFlux data, significantly improved the model performance. The simulated annual GPP, RE, NEE, and ET from the calibrated model were highly consistent with observed values. The observed and simulated GPP and RE across the six sites are positively correlated with the annual mean air temperature and annual total precipitation. On the other hand, the simulated carbon budget is partly explained by the stand disturbance history in addition to the climate. The sensitivity study indicates that spring warming enhances the carbon sink, whereas summer warming decreases it across the larch forests. The summer radiation is the most important factor that controls the carbon fluxes in the temperate site, but the VPD and water conditions are the limiting factors in the boreal sites. One model parameter, the allocation ratio of carbon between aboveground and belowground, is site-specific, and it is negatively correlated with the annual climate of annual mean air temperature and total precipitation. Although this study significantly improves the model performance, the uncertainties that remain in terms of the sensitivity to water conditions should be examined in ongoing and long-term observations.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

Uncertainty of evapotranspiration fluxes measured with eddy covariance

2020 ◽  
Author(s):  
Felix Pohl ◽  
Anke Hildebrandt ◽  
Ulrike Werban ◽  
Corinna Rebmann
Keyword(s):  
Eddy Covariance ◽  
Ecosystem Respiration ◽  
Spatial Scales ◽  
Net Ecosystem Exchange ◽  
Weather Conditions ◽  
Lower Atmosphere ◽  
Special Focus ◽  
Eddy Covariance Method ◽  
Rain Events ◽  
Water Cycles

&lt;p&gt;The project MOMENT (Model Monitoring EveNTs) investigates the interplay between carbon and water cycles with special focus on the impacts of drought and heatwaves as well as their long-term trends. This project aims to investigate new monitoring and modeling methods to explain the interplay between carbon and water cycles of ecosystems on different time and spatial scales.&lt;/p&gt;&lt;p&gt;To achieve this goal, we need reliable information about the ecosystem and its drivers. We measure, for example, mass and energy exchange between the ecosystem and the lower atmosphere with the eddy covariance method, which allows us to obtain data on a half-hourly scale. Nevertheless, unfavorable weather conditions, as well as malfunctions of the instruments, can lead to a serious amount of data gaps. Different gap-filling methods are available, with the Marginal Distribution Sampling (MDS) by Reichstein et al. (2005) being the most common one. Here, we investigate, how different filling approaches influence the uncertainty of evapotranspiration (ET) data for a German forest. We especially focus on the imputation of evaporation from intercepted canopy water, because open-path EC systems rarely work correctly during and after rain events.&lt;/p&gt;&lt;p&gt;Even though the EC technique is a well-established method to measure ET at the ecosystem level, many approaches require rather the share of transpiration, such as the validation of some ecosystem models. Partitioning ET into its components is difficult due to the manifold drivers involved, and measuring ecosystem transpiration is challenging due to measurement limitations and assumptions, that have to be made. Therefore, we examine the possibility to retrieve information about the share of transpiration by using EC data only without additional measurement campaigns.&lt;/p&gt;&lt;p&gt;Reference: &lt;br&gt;&lt;span&gt;Reichstein&lt;/span&gt;, M. et al.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and improved algorithm, Glob. Chang. Biol., 11(9), 1424&amp;#8211;1439, doi:10.1111/j.1365-2486.2005.001002.x, 2005.&lt;/p&gt;

scholarly journals Crowdsourcing Urban Air Temperatures through Smartphone Battery Temperatures in São Paulo, Brazil

2017 ◽  
Vol 34 (9) ◽  
pp. 1853-1866 ◽  
Author(s):  
A. M. Droste ◽  
J. J. Pape ◽  
A. Overeem ◽  
H. Leijnse ◽  
G. J. Steeneveld ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Air Temperature ◽  
Spatial Scales ◽  
Sao Paulo ◽  
Heat Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
São Paulo ◽  
Air Temperatures ◽  
Local Climate Zones

AbstractCrowdsourcing as a method to obtain and apply vast datasets is rapidly becoming prominent in meteorology, especially for urban areas where routine weather observations are scarce. Previous studies showed that smartphone battery temperature readings can be used to estimate the daily and citywide air temperature via a direct heat transfer model. This work extends model estimates by studying smaller temporal and spatial scales. The study finds the number of battery readings influences the accuracy of temperature retrievals. Optimal results are achieved for 700 or more retrievals. An extensive dataset of over 10 million battery temperature readings for estimating hourly and daily air temperatures is available for São Paulo, Brazil. The air temperature estimates are validated with measurements from a WMO station, an Urban Flux Network site, and data from seven citizen weather stations. Daily temperature estimates are good (coefficient of determination ρ2 of 86%), and the study shows they improve by optimizing model parameters for neighborhood scales (<1 km2) as categorized in local climate zones (LCZs). Temperature differences between LCZs can be distinguished from smartphone battery temperatures. When validating the model for hourly temperature estimates, the model requires a diurnally varying parameter function in the heat transfer model rather than one fixed value for the entire day. The results show the potential of large crowdsourced datasets in meteorological studies, and the value of smartphones as a measuring platform when routine observations are lacking.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

scholarly journals Sequential modelling of the Earth’s core magnetic field

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Guillaume Ropp ◽  
Vincent Lesur ◽  
Julien Baerenzung ◽  
Matthias Holschneider
Keyword(s):  
Magnetic Field ◽  
Prior Information ◽  
Spatial Scales ◽  
Model Parameters ◽  
International Geomagnetic Reference Field ◽  
The Earth ◽  
Temporal And Spatial ◽  
Different Sources ◽  
Original Approach ◽  
Modelling Approach

Abstract We describe a new, original approach to the modelling of the Earth’s magnetic field. The overall objective of this study is to reliably render fast variations of the core field and its secular variation. This method combines a sequential modelling approach, a Kalman filter, and a correlation-based modelling step. Sources that most significantly contribute to the field measured at the surface of the Earth are modelled. Their separation is based on strong prior information on their spatial and temporal behaviours. We obtain a time series of model distributions which display behaviours similar to those of recent models based on more classic approaches, particularly at large temporal and spatial scales. Interesting new features and periodicities are visible in our models at smaller time and spatial scales. An important aspect of our method is to yield reliable error bars for all model parameters. These errors, however, are only as reliable as the description of the different sources and the prior information used are realistic. Finally, we used a slightly different version of our method to produce candidate models for the thirteenth edition of the International Geomagnetic Reference Field.

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 Transmission of solar radiation through clouds on melting glaciers: a comparison of parameterizations and their impact on melt modelling

2011 ◽  
Vol 57 (202) ◽  
pp. 367-381 ◽  
Author(s):  
Francesca Pellicciotti ◽  
Thomas Raschle ◽  
Thomas Huerlimann ◽  
Marco Carenzo ◽  
Paolo Burlando
Keyword(s):  
Solar Radiation ◽  
Air Temperature ◽  
Radiative Forcing ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Swiss Alps ◽  
Cloud Radiative Forcing ◽  
Correct Definition ◽  
Diurnal Range ◽  
The Impact

AbstractWe explore the robustness and transferability of parameterizations of cloud radiative forcing used in glacier melt models at two sites in the Swiss Alps. We also look at the rationale behind some of the most commonly used approaches, and explore the relationship between cloud transmittance and several standard meteorological variables. The 2 m air-temperature diurnal range is the best predictor of variations in cloud transmittance. However, linear and exponential parameterizations can only explain 30–50% of the observed variance in computed cloud transmittance factors. We examine the impact of modelled cloud transmittance factors on both solar radiation and ablation rates computed with an enhanced temperature-index model. The melt model performance decreases when modelled radiation is used, the reduction being due to an underestimation of incoming solar radiation on clear-sky days. The model works well under overcast conditions. We also seek alternatives to the use of in situ ground data. However, outputs from an atmospheric model (2.2 km horizontal resolution) do not seem to provide an alternative to the parameterizations of cloud radiative forcing based on observations of air temperature at glacier automatic weather stations. Conversely, the correct definition of overcast conditions is important.

Development of an Inverse Plume Model for Mass Eruption Rate in Unsteady Conditions

2021 ◽  
Author(s):  
Stephen A Solovitz
Keyword(s):  
Laboratory Data ◽  
Model Performance ◽  
Volcanic Eruptions ◽  
Reynolds Numbers ◽  
Model Parameters ◽  
Plume Height ◽  
Eruption Rate ◽  
Mass Eruption Rate ◽  
Downstream Effects ◽  
Order Of Magnitude

Abstract Following volcanic eruptions, forecasters need accurate estimates of mass eruption rate (MER) to appropriately predict the downstream effects. Most analyses use simple correlations or models based on large eruptions at steady conditions, even though many volcanoes feature significant unsteadiness. To address this, a superposition model is developed based on a technique used for spray injection applications, which predicts plume height as a function of the time-varying exit velocity. This model can be inverted, providing estimates of MER using field observations of a plume. The model parameters are optimized using laboratory data for plumes with physically-relevant exit profiles and Reynolds numbers, resulting in predictions that agree to within 10% of measured exit velocities. The model performance is examined using a historic eruption from Stromboli with well-documented unsteadiness, again providing MER estimates of the correct order of magnitude. This method can provide a rapid alternative for real-time forecasting of small, unsteady eruptions.

RETROSPECTIVE ASSESSMENT OF UNREGULATED LATERAL INFLOWS OF THE KUIBYSHEVSKY (NOW ZHIGULYOVSKAYA HYDROPOWER PLANT) HYDRAULIC UNIT OF THE VOLGA RIVER BASIN

2021 ◽  
pp. 87-99
Author(s):  
G. KH. ISMAIYLOV ◽  
◽  
N. V. MURASCHENKOVA ◽  
I. G. ISMAIYLOVA
Keyword(s):  
River Basin ◽  
Air Temperature ◽  
Atmospheric Precipitation ◽  
Total Precipitation ◽  
Volga River ◽  
Hydraulic Unit ◽  
Seasonal Characteristics ◽  
Lateral Inflow ◽  
Increasing Trend

The results of the analysis and assessment of changes in annual and seasonal characteristics of hydrometeorological processes in a private catchment area of the Kuibyshev hydroelectric complex of the Volga river are presented. To analyze the temporal dynamics of the variability of the annual and seasonal characteristics of the hydrometeorological processes in the considered territory of the river basin we used more than 100 years of observations of annual and seasonal fluctuations of lateral inflow, total atmospheric precipitation and air temperature regimes on the Volgariver. Relationship equations for annual and seasonal changes in hydrometeorological characteristics in time are obtained. It was found that long-term fluctuations of hydrometeorological processes (lateral inflow, total atmospheric precipitation and air temperature) are characterized by tendencies (trends). The analysis of these trends showed that the non-standard climatic situation, starting from the 70s of the last century, had a very significant impact on the distribution of annual and especially on the seasonal (low-water and winter) characteristics of hydrometeorological processes. It has been established that non-standard unidirectional changes are found in the fluctuations in the total atmospheric precipitation. If the winter total precipitation is characterized over the 100-year period in question by a continuously decreasing trend,the summer-autumn period is an increasing trend. This leads to the fact that long-term fluctuations in total precipitation during the period of low water are formed as a stationary process. At the same time, the total precipitation of the spring flood and inflowing to the Kuibyshev hydroelectric unit is characterized by a constantly increasing trend.

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