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Regional and Model-Specific Response Types in A Global Gridded Crop Model Ensemble
Crop models are often employed to project crop yields under changing conditions such as global warming and associated management change for adaptation. Multi-model ensembles are promoted to enhance the robustness of projections, but questions remain on what causes often large differences between projections of individual models. Global Gridded Crop Models (GGCMs) are especially exposed to this question when applied for assessing climate change impacts, adaptation, environmental impacts of agricultural production, because their results are used in downstream analyses, such as in integrated assessment or economic modeling for projecting future land-use change. Even though global gridded crop models are often based on detailed field-scale models or have implemented similar modeling principles in other ecosystem models, global-scale models are subject to substantial uncertainties from both model structure and parametrization as well as from calibration and input data quality. AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) has thus set out to intercompare GGCMs in order to evaluate model performance, describe model uncertainties, identify inconsistencies within the ensemble and underlying reasons, and to ultimately improve models and modeling capacities. In phase 2 of the GGCMI activities, 12 modeling groups followed a modeling protocol that asked for up to 1404 31-year global simulations at 0.5 arc-degree spatial resolution to assess models’ sensitivities to changes in carbon dioxide (C; 4 different levels) temperature (T; 7 different offset levels), water supply (W; 9 levels), and nitrogen (N; 3 levels), the so-called CTWN experiment (Franke et al. 2020; http://dx.doi.org/10.5194/gmd-13-2315-2020). We here present analyses of model response types using impact response surfaces along the C, T, W, and N dimensions, respectively and collectively. Doing so, we can understand differences in simulated responses per driver rather than aggregated changes in yields. We find that models’ sensitivities to the individual driver dimensions are substantially different and often more different across models than across regions. A cluster analysis finds regional and model-specific patterns. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response type clusters across models suggests that models need to undergo further scrutiny. We suggest establishing standards in model process evaluation not only against historical dynamics but also against dedicated experiments across the CTWN dimensions.
InSAR Evaluation in Support of Louisiana Regional Geospatial Modeling
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Implementing and Improving CBMZ-MAM3 Chemistry and Aerosol Modules in the Regional Climate Model WRF-CAM5: An Evaluation over the Western US and Eastern North Pacific
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Tsunami Squares: Earthquake driven inundation mapping and validation by comparison to the Regional Ocean Modeling System
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Satellite-Based Evaluation of AeroCom Model Bias in Biomass Burning Regions
Global models are widely used to simulate biomass burning aerosol (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observations and Models (AeroCom) project model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase-III experiments (i.e., biomass burning emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning in boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances measurements with the Generalized Retrieval of Aerosol and Surface Properties algorithm (POLDER-GRASP), we found that the modeled AOD values were biased by −93 % to 152 %, with most models showing significant underestimations even for the state-of-the-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations in AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and the mass extinction coefficient (MEC). We found that in the AeroCom ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP-observed AE suggested that the models tended to overestimate the AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models contain biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.
Mesoscale simulations of the November 25-26 and December 5-6 cirrus cases using the RAMS model
The Regional Atmospheric Modeling System (RAMS), developed at Colorado State University, was used during the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment (FIRE) 2 (13 Nov. through 6 Dec. 1991) to provide real time forecasts of cirrus clouds. Forecasts were run once a day, initializing with the 0000 UTC dataset provided by NOAA (Forecast Systems Laboratory (FSL) Mesoscale Analysis and Prediction System (MAPS)). In order to obtain better agreement with observations, a second set of simulations were done for the FIRE 2 cases that occurred on 25-26 Nov. and 5-6 Dec. In this set of simulations, a more complex radiation scheme was used, the Chen/Cotton radiation scheme, along with the nucleation of ice occurring at ice supersaturations as opposed to nucleation occurring at water supersaturations that was done in the actual forecast version. The runs using these more complex schemes took longer wall clock time (7-9 hours for the actual forecasts as compared to 12-14 hrs for the runs using the more complex schemes) however, the final results of the simulations were definitely improved upon. Comparisons between these two sets of simulations are given. Now underway are simulations of these cases using a closed analytical solution for the auto-conversion of ice from a pristine ice class (sizes less than about 50 microns in effective diameter) to a snow class (effective diameters on the order of several hundred microns). This solution is employed along with a new scheme for the nucleation of ice crystals due to Meyers et al and Demott et al. The scheme is derived assuming complete gamma distributions for both the pristine and snow classes. The time rate of change of the number concentration and mass mixing-ratio of each distribution is found by calculating either the flux of crystals that grow beyond a certain critical diameter by vapor deposition in an ice supersaturated regime or by calculating the flux of crystals that evaporate to sizes below that same critical effective diameter.
Wind-US Simulations of the NASA 1507 Inlet Test Case
Steady-state, Reynolds-Averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) simulations were performed of the NASA 1507 Inlet Test Case from the 6th AIAA Propulsion Aerodynamics Workshop (PAW) held in January 2023 using the Wind-US CFD solver. The simulations were performed with bleed regions modeled as surface patches and the Slater bleed boundary condition models were applied within those regions. Both the constant-plenum-pressure and the fixed-plenum-exit bleed models where applied. The vortex generators were modeled using the Bender-Anderson-Yagle (BAY) vortex generator model. The CFD simulations were performed for inlet engine-face flow ratios varying from supercritical to subcritical inlet operation until inlet unstart. The results of the CFD simulations were compared to wind-tunnel data which included boundary layer profiles, axial static pressure distributions, bleed rates, total pressure recovery performance curves, and engine-face total pressure rake profiles. The CFD simulations were able to match most of the boundary layer rake data near the geometric throat, but the boundary layers on the centerbody were thinner than indicated by the wind-tunnel data. For the supercritical CFD simulations, the engine-face flow ratio was at least 2% to 2.5% lower than indicated by the wind-tunnel data, but still consistent with the level of supercritical bleed. The engine-face flow rates of the wind-tunnel tests had an uncertainty of ±2% which could explain the differences. The supercritical engine-face flow rates of the wind-tunnel data indicated a 5% supercritical bleed rate rather than the reported 7.18% bleed rate. The CFD simulations were able to calculate the maximum total pressure recovery of pt2/pt = 0.907 as observed for the wind-tunnel data. The CFD simulations with the constant-pressure bleed model were able to obtain a much lower engine-face total pressure ratio than indicated by both the wind-tunnel data and the CFD simulations using a fixed-plenum-exit bleed model. The CFD simulations indicated a sharp static pressure rise for the terminal shock rather than a more gradual rise indicated by the wind-tunnel data. The CFD simulations were not able to accurately compute the total pressure variation across the engine-face total pressure rake.
NASA Experimental Program to Stimulate Competitive Research: South Carolina
The use of an appropriate relationship model is critical for reliable prediction of future urban growth. Identification of proper variables and mathematic functions and determination of the weights or coefficients are the key tasks for building such a model. Although the conventional logistic regression model is appropriate for handing land use problems, it appears insufficient to address the issue of interdependency of the predictor variables. This study used an alternative approach to simulation and modeling urban growth using artificial neural networks. It developed an operational neural network model trained using a robust backpropagation method. The model was applied in the Myrtle Beach region of South Carolina, and tested with both global datasets and areal datasets to examine the strength of both regional models and areal models. The results indicate that the neural network model not only has many theoretic advantages over other conventional mathematic models in representing the complex urban systems, but also is practically superior to the logistic model in its capability to predict urban growth with better - accuracy and less variation. The neural network model is particularly effective in terms of successfully identifying urban patterns in the rural areas where the logistic model often falls short. It was also found from the area-based tests that there are significant intra-regional differentiations in urban growth with different rules and rates. This suggests that the global modeling approach, or one model for the entire region, may not be adequate for simulation of a urban growth at the regional scale. Future research should develop methods for identification and subdivision of these areas and use a set of area-based models to address the issues of multi-centered, intra- regionally differentiated urban growth.
Theoretical study of anomalously high F region peak altitudes in the polar ionosphere
It is pointed out that during the last solar maximum period several observations of anomalously high F region peak altitudes (hmF2) have been made by high latitude incoherent scatter radars. In the present investigation, a high latitude time-dependent F region model (HLTD) was used to study a range of differing plasma transport processes in order to identify possible source mechanisms for the anomalously high hmF2's. A description of model parameters is presented, and region density profiles are discussed. Attention is given to the solar maximum, the solar minimum, and the asymmetric wind. The results of the HLTD model study of transport effects for a wide range of solar cycle, season, magnetic activity, and neutral wind conditions show that high hmF2 values in the polar cap can be generated without the need for auroral particle precipitation.
Static and Impulsive Models of Solar Active Regions
The physical modeling of active regions (ARs) and of the global coronal is receiving increasing interest lately. Recent attempts to model ARs using static equilibrium models were quite successful in reproducing AR images of hot soft X-ray (SXR) loops. They however failed to predict the bright EUV warm loops permeating ARs: the synthetic images were dominated by intense footpoint emission. We demonstrate that this failure is due to the very weak dependence of loop temperature on loop length which cannot simultaneously account for both hot and warm loops in the same AR. We then consider time-dependent AR models based on nanoflare heating. We demonstrate that such models can simultaneously reproduce EUV and SXR loops in ARs. Moreover, they predict radial intensity variations consistent with the localized core and extended emissions in SXR and EUV AR observations respectively. We finally show how the AR morphology can be used as a gauge of the properties (duration, energy, spatial dependence, repetition time) of the impulsive heating.
Quantitative Measures of Immersion in Cloud and the Biogeography of Cloud Forests
Sites described as tropical montane cloud forests differ greatly, in part because observers tend to differ in their opinion as to what constitutes frequent and prolonged immersion in cloud. This definitional difficulty interferes with hydrologic analyses, assessments of environmental impacts on ecosystems, and biogeographical analyses of cloud forest communities and species. Quantitative measurements of cloud immersion can be obtained on site, but the observations are necessarily spatially limited, although well-placed observers can examine 10 50 km of a mountain range under rainless conditions. Regional analyses, however, require observations at a broader scale. This chapter discusses remote sensing and modeling approaches that can provide quantitative measures of the spatiotemporal patterns of cloud cover and cloud immersion in tropical mountain ranges. These approaches integrate remote sensing tools of various spatial resolutions and frequencies of observation, digital elevation models, regional atmospheric models, and ground-based observations to provide measures of cloud cover, cloud base height, and the intersection of cloud and terrain. This combined approach was applied to the Monteverde region of northern Costa Rica to illustrate how the proportion of time the forest is immersed in cloud may vary spatially and temporally. The observed spatial variation was largely due to patterns of airflow over the mountains. The temporal variation reflected the diurnal rise and fall of the orographic cloud base, which was influenced in turn by synoptic weather conditions, the seasonal movement of the Intertropical Convergence Zone and the north-easterly trade winds. Knowledge of the proportion of the time that sites are immersed in clouds should facilitate ecological comparisons and biogeographical analyses, as well as land use planning and hydrologic assessments in areas where intensive on-site work is not feasible.
Photoionization modeling of the LWS fine-structure lines in IR bright galaxies
The long wavelength spectrometer (LWS) fine structure line spectra from infrared luminous galaxies were modeled using stellar evolutionary synthesis models combined with photoionization and photodissociation region models. The calculations were carried out by using the computational code CLOUDY. Starburst and active galactic nuclei models are presented. The effects of dust in the ionized region are examined.
Propagation studies using a theoretical ionosphere model
The mid-latitude ionospheric and neutral atmospheric models are coupled with an advanced three dimensional ray tracing program to see what success would be obtained in predicting the wave propagation conditions and to study to what extent the use of theoretical ionospheric models is practical. The Penn State MK 1 ionospheric model, the Mitra-Rowe D region model, and the Groves' neutral atmospheric model are used throughout this work to represent the real electron densities and collision frequencies. The Faraday rotation and differential Doppler velocities from satellites, the propagation modes for long distance high frequency propagation, the group delays for each mode, the ionospheric absorption, and the spatial loss are all predicted.
A proposal to conduct a Caribbean plate project involving the application of space technology to the study of Caribbean geology
The Caribbean plate project is designed to improve current understanding of geological resources and geological hazards within the Caribbean region. Models of mineral occurrence and genesis (including energy resources) on a regional scale, which contribute to nonrenewable resource investigations. Models of lithospheric stress and strain on a regional scale, which contribute to forecasting geological hazards such as earthquakes and major volcanic eruptions are developed. Geological information is synthesize, and research tools provided by space technology the study of the Earth's crust are used. The project was organized in a thematic fashion, to focus on specific geological aspects of the Caribbean plate which are considered to be key factors in developing the types of models described. The project adopts a synoptic perspective in seeking to characterize the three dimensional structure, composition, state of stress, and evolution of the entire Caribbean plate. Geological information derived from analysis of space acquired data is combined with information provided by conventional methods to obtain insight into the structure, composition, and evolution of the Earth's crust. In addition, very long baseline interferometry and laser ranging techniques, which are also based upon the use of space technology, obtain information concerning crustal motion that, in turn, provides insight into the distribution and localization of crustal stress.
Airport Choice in Sao Paulo Metropolitan Area: An Application of the Conditional Logit Model
Using the conditional LOGIT model, this paper addresses the airport choice in the Sao Paulo Metropolitan Area. In this region, Guarulhos International Airport (GRU) and Congonhas Airport (CGH) compete for passengers flying to several domestic destinations. The airport choice is believed to be a result of the tradeoff passengers perform considering airport access characteristics, airline level of service characteristics and passenger experience with the analyzed airports. It was found that access time to the airports better explain the airport choice than access distance, whereas direct flight frequencies gives better explanation to the airport choice than the indirect (connections and stops) and total (direct plus indirect) flight frequencies. Out of 15 tested variables, passenger experience with the analyzed airports was the variable that best explained the airport choice in the region. Model specifications considering 1, 2 or 3 variables were tested. The model specification most adjusted to the observed data considered access time, direct flight frequencies in the travel period (morning or afternoon peak) and passenger experience with the analyzed airports. The influence of these variables was therefore analyzed across market segments according to departure airport and flight duration criteria. The choice of GRU (located neighboring Sao Paulo city) is not well explained by the rationality of access time economy and the increase of the supply of direct flight frequencies, while the choice of CGH (located inside Sao Paulo city) is. Access time was found to be more important to passengers flying shorter distances while direct flight frequencies in the travel period were more significant to those flying longer distances. Keywords: Airport choice, Multiple airport region, Conditional LOGIT model, Access time, Flight frequencies, Passenger experience with the analyzed airports, Transportation planning