Parameterization Of Processes Research Articles

Towards Precision Biocatalysis ‐ Leveraging Inline NMR for Autonomous Experimentation in Flow Reactors

AbstractReactor automation is a transformative force for chemical processes, but the potential of reaction monitoring for machine‐assisted autonomous biocatalytic reaction optimization is still largely unexplored. To address this gap, we report on automated reactor optimization for biocatalytic flow‐through microreactors. For this purpose, the inline NMR analysis of an enzymatically catalyzed stereoselective reduction of a prochiral diketone was combined with a self‐developed open‐source analysis and control software. The algorithm is continuously fed with spectra from a benchtop NMR instrument acquired from a reaction solution from a microreactor filled with biocatalytically active materials and adjusts the flow rate of the pumps to achieve predetermined target concentrations of the product. We show that through this automated coupling of data analysis and process parameterization, for example, maximum conversion efficiency can be achieved for a given bioreactor. This work illustrates the potential of inline NMR reaction monitoring for biocatalytic processes and provides a starting point for innovation to develop automated processes for precision biocatalysis through integrated data analysis.

Spatiotemporal Moisture Field

For monitoring capillary moisture conduction, the most important parameter is the moisture conductivity coefficient, which is a material characteristic; however, its use in practical calculations is not very common. For further development in the field of liquid moisture propagation, an automated measuring apparatus has been developed and granted a European patent. Its essence lies in detecting the liquid water content based on a well-known physical phenomenon: electromagnetic radiation in the microwave range. The determination of the spatiotemporal moisture field is the first and fundamental step for describing transportation phenomena. The moisture field thus created allows for the viewing of the moisture conductivity coefficient, which is one of the most important parameters in describing transportation phenomena as a function of moisture. The presence of water in building materials can significantly affect their physical properties, such as mechanical or thermal–technical characteristics. This may lead to unacceptable consequences, which might only manifest after a certain period of time. In the case of multi-layered structures, moisture can transfer from one material to another. Therefore, it is essential to address this process. The advantage of the software solution described by the methodology is the use of an open communication protocol in the form of a synchronized array, which is not common in typical applications of this type. The principle of separating hardware modules is also unusual for devices of this type, as it requires the independent communication of each module with the control software. Mutual communication is handled exclusively at the software level, making it possible to modify, optimize, or parameterize the procedures as needed. Upon closer examination of the wetting curves of various materials, anomalies were revealed in some of their structures. This can be advantageously utilized in the research of newly developed composite materials. The assembled system of measuring instruments, their software integration, and control provide a foundation for the practical application of the described procedures and methods for determining the moisture field of building materials. The parameterization of individual processes, as well as the open access to data, allows for the optimization of the methodology, as materials of entirely different characteristics may require an individual approach, which will certainly contribute to the advancement of science and research in this area. Currently, this work is being followed by further extensive studies, not yet published by the authors, focusing on the application of the described moisture field to evaluate the moisture conductivity coefficient as a function dependent on the material’s mass moisture content. Their application requires specific mathematical and programming approaches due to the significant volume of data involved.

Methods for Quantifying Source-Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice.

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

Assessment of a hydrologic-land surface model to simulate thermo-hydrologic evolution of permafrost regions

Hydrologic-land surface models (H-LSMs) offer a physically-based framework for representing and predicting the present and future states of the extensive high-latitude permafrost areas worldwide. Their primary challenge, however, is that soil temperature data are severely limited, and traditional model validation, based only on streamflow, can show the right fit to these data for the wrong reasons. Here, we address this challenge by (1) collecting existing data in various forms including in-situ borehole data and different large-scale permafrost maps in addition to streamflow data, (2) comprehensively evaluating the performance of an H-LSM with a wide range of possible process parametrizations and initializations, and (3) assessing possible trade-offs in model performance in concurrently representing hydrologic and permafrost dynamics, thereby pointing to the possible model deficiencies that require improvement. As a case study, we focus on the sub-arctic Liard River Basin in Canada, which typifies vast northern sporadic and discontinuous permafrost regions. Our findings reveal that different process parameterizations tend to align with different data sources or variables, which largely exhibit inconsistencies among themselves. We further observe that a model may fail to represent permafrost occurrence yet seemingly fit streamflows adequately. Nonetheless, we demonstrate that accurately representing essential permafrost dynamics, including the active soil layer and insulation effects from snow cover and soil organic matter, is crucial for developing high-fidelity models in these regions. Given the complexity of processes and the incompatibility among different data sources/variables, we conclude that employing an ensemble of carefully designed model parameterizations is essential to provide a reliable picture of the current conditions and future spatio-temporal co-evolution of hydrology and permafrost.

A Deep Learning-Enhanced Compartmental Model and Its Application in Modeling Omicron in China.

The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced dependency on data particularity and with a built-in temporal-spatial-mobile process that models the geographic distribution and physical mobility of the population. In particular, we apply DNN (Deep Neural Network) and LSTM (Long-Short Term Memory) techniques to estimate the transmission parameters in a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemic in China over the 28 days for the period between 4 June and 1 July 2022. The average levels of predication accuracy of the model are 98% and 92% for the number of infections and deaths, respectively. We establish that deep learning techniques provide an alternative to the prevalent compartmental modes and demonstrate the efficacy and potential of applying deep learning methodologies in predicting the dynamics of infectious diseases.

Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity

Abstract North Atlantic tropical cyclone (TC) activity under a high-emission scenario is projected using a statistical synthetic storm model coupled with nine phase 6 of the Coupled Model Intercomparison Project (CMIP6) climate models. The ensemble projection shows that the annual frequency of TCs generated in the basin will decrease from 15.91 (1979–2014) to 12.16 (2075–2100), and TC activity will shift poleward and coastward. The mean of lifetime maximum intensity will increase from 66.50 to 75.04 kt (1 kt ≈ 0.51 m s−1). Large discrepancies in TC frequency and intensity projections are found among the nine CMIP6 climate models. The uncertainty in the projection of wind shear is the leading cause of the discrepancies in the TC climatology projection, dominating the uncertainties in the projection of thermodynamic parameters such as potential intensity and saturation deficit. The uncertainty in the projection of wind shear may be related to the different projections of horizontal gradient of vertically integrated temperature in the climate models, which can be induced by different parameterizations of physical processes including surface process, sea ice, and cloud feedback. Informed by the uncertainty analysis, a surrogate model is developed to provide the first-order estimation of TC activity in climate models based on large-scale environmental features.

How dependent are quantitative volcanic ash concentration and along‐flight dosage forecasts to model structural choices?

AbstractProducing quantitative volcanic ash forecasts is challenging due to multiple sources of uncertainty. Careful consideration of this uncertainty is required to produce timely and robust hazard warnings. Structural uncertainty occurs when a model fails to produce accurate forecasts, despite good knowledge of the eruption source parameters, meteorological conditions and suitable parameterizations of transport and deposition processes. This uncertainty is frequently overlooked in forecasting practices. Using a Lagrangian particle dispersion model, simulations with varied output spatial resolution, temporal averaging period and particle release rate are performed to quantify the impact of these structural choices. This experiment reveals that, for the 2019 Raikoke eruption, structural choices give measurements of peak ash concentration spanning an order of magnitude, significantly impacting decision‐relevant thresholds used in aviation flight planning. Conversely, along‐flight dosage estimates exhibit less sensitivity to structural choices, suggesting it is a more robust metric to use in flight planning. Uncertainty can be reduced by eliminating structural choices that do not result in a favourable level of agreement with a high‐resolution reference simulation. Reliable forecasts require output spatial resolution 80 km, temporal averaging periods 3 h and particle release rates 5000 particles/h. This suggests that simulations with relatively small numbers of particles could be used to produce a large ensemble of simulations without significant loss of accuracy. Comparison with previous Raikoke simulations indicates that the uncertainty associated with these constrained structural choices is smaller than those associated with satellite constrained eruption source parameter and internal model parameter uncertainties. Thus, given suitable structural choices, other epistemic sources of uncertainty are likely to dominate. This insight is useful for the design of ensemble methodologies which are required to enable a shift from deterministic to probabilistic forecasting. The results are applicable to other long‐range dispersion problems and to Eulerian dispersion models.

Combined use of physically based hydrological model and empirical models to improve parameterisation of erosion processes in a flash flood prone catchment

AbstractThis study assesses the effectiveness of a distributed physically based hydrological model (MARINE) to investigate erosion estimation during flash floods compared with other widely used empirical models derived from the Universal Soil Loss Equation (USLE) like Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE). It is carried out on a small catchment in south‐eastern France, the Claduègne catchment. To compare the erosion volumes simulated by the three models, MARINE, MUSLE and RUSLE, a sensitivity analysis on the model parameters is carried out. According to physics‐based simulations, flood events fall into two categories: those dominated by raindrop erosion and those dominated by shear stress erosion. The results show that the erosion simulated by the three methods are comparable, except for events dominated by raindrop erosion suggesting that further research is needed to improve raindrop erosion within MARINE. Simulations from the MARINE model provide access to the spatio‐temporal variability of erosion dynamics during the event and can also be used to produce erosion/deposition maps, which are useful for environmental decision‐makers and planners in identifying areas at risk from erosion and deposition hazards.

Electrical Resistivity Changes During Heating Experiments Unravel Heterogeneous Thermal‐Hydrological‐Mechanical Processes in Salt Formations

AbstractRock salt is considered a suitable medium for the permanent disposal of heat‐generating radioactive waste due to its isolation properties. However, excavation damage and heating induce complex and heterogeneous thermal‐hydrological‐mechanical (THM) processes across different zones. Quantifying this heterogeneity is crucial for accurate long‐term performance assessment models, but traditional methods lack the necessary resolution. This study employs 4D electrical resistivity tomography (ERT) monitoring during controlled heating experiments in a salt formation to unravel the spatiotemporal dynamics of THM processes. Advanced time‐lapse inversion and clustering analysis quantify subsurface properties and map the heterogeneity of THM dynamics. The ERT results can estimate subsurface properties and delineate the damaged and intact zones, enabling appropriate parameterization and representation of processes for long‐term modeling. This approach may be used in further improving the predictive models and ensuring the safe long‐term disposal of radioactive waste in rock salt.

Observer-based consensus control for a class of nonlinear singular multi-agent systems

This paper studies the bounded consensus control problem for nonlinear singular multi-intelligence systems (NSMASs) based on observers, where the structure of nonlinear dynamics is completely unknown. Firstly, the unknown nonlinear terms are estimated by the linear parameterization method. However, since the system state is not directly available, the observers are used for state estimations, which leads to a double estimation problem in the parameterization process. Moreover, the distributed adaptive control protocols and the adaptive laws of unknown parameter estimations are developed in the leader-following and leaderless frameworks. Furthermore, the proposed control protocols achieve bounded consensus for the NSMASs. And the proposed nonlinear estimation methods guarantee bounded estimation performance of the unknown nonlinear terms in the double estimation problem. Finally, two examples are given to illustrate the effectiveness of the proposed methods.

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from Climate Models

Abstract Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias-correction approaches usually focus on correcting lower-order statistics (mean and standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.

Improved soil moisture estimation and detection of irrigation signal by incorporating SMAP soil moisture into the Indian Land Data Assimilation System (ILDAS)

Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).

Influence of saturation property of cold nuclear matter on the parameterization process of fusion barriers

Influence of saturation property of cold nuclear matter on the parameterization process of fusion barriers

Fast parameterization of planar domains for isogeometric analysis via generalization of deep neural network

One prominent step in isogeometric analysis (IGA) is known as domain parameterization, that is, finding a parametric spline representation for a computational domain. Typically, domain parameterization is divided into two separate steps: identifying an appropriate boundary correspondence and then parameterizing the interior region. However, this separation significantly degrades the quality of the parameterization. To attain high-quality parameterization, it is necessary to optimize both the boundary correspondence and the interior mapping simultaneously, referred to as integral parameterization. In a prior research, an integral parameterization approach for planar domains based on neural networks was introduced. One limitation of this approach is that the neural network has no ability of generalization, that is, a network has to be trained to obtain a parameterization for each specific computational domain. In this article, we propose an efficient enhancement over this work, and we train a network which has the capacity of generalization—once the network is trained, a parameterization can be immediately obtained for each specific computational via evaluating the network. The new network greatly speeds up the parameterization process by two orders of magnitudes. We evaluate the performance of the new network on the MPEG data set and a self-design data set, and experimental results demonstrate the superiority of our algorithm compared to state-of-the-art parameterization methods.

Estimating adaptation effort in industry 4.0-enabled systems: Introducing two complexity indices with an evolvable network graph approach

One of the key aims of Industry 4.0 is to create more responsive systems. Responsiveness enables coping with new market requirements or introducing new products, as demonstrated by the COVID-19 challenges. However, there are currently no effective methods for measuring the responsiveness or reconfigurability of a system, or for quantifying the effort required to adapt it from one state to another. Adapting a production cell from its current state to a new adapted state requires a significant amount of information about dismantling, reintegrating, and handling physical equipment, as well as updating the software controller. Practitioners often only consider adaptation options for simple process parametrization or at the end of a system's life cycle, overlooking many potential adaptation opportunities. This paper proposes an evolvable network graph approach for supporting reconfiguration decisions by estimating the effort required to adapt the physical structure. Two complexity indexes have been developed to quantify the adaptation activities. An estimation algorithm infers the effort from the difference in the adaptation graphs that represent alternative options. The approach is illustrated in a laboratory-scale cell and applied in two industrial-sized cells, quantifying adaptation times of approximately 58, 7, and 122 h, respectively. This is equivalent to £3129.6, £356.04, and £6118.8, utilizing average hourly rates for system integrators and equipment handlers. The results show that the approach can effectively quantify the adaptation effort for different equipment sizes and connections, estimating the adaptation cost and time from the graph change quickly at around a millisecond and with minimal computational resources.

Midsummer precipitation prediction over eastern China by the dynamic downscaling method

AbstractThis study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction.

Fetch‐Limited, Strongly Forced Wind Waves in Waters With Frazil and Grease Ice – Spectral Modeling and Satellite Observations in an Antarctic Coastal Polynya

AbstractSea ice–wave interactions have been widely studied in the marginal ice zone, at relatively low wind speeds and wave frequencies. Here, we focus on very different conditions typical of coastal polynyas: extremely high wind speeds and locally generated, short, steep waves. We overview available parameterizations of relevant physical processes (nonlinear wave–wave interactions, energy input by wind, whitecapping and ice‐related dissipation) and discuss modifications necessary to adjust them to polynya conditions. We use satellite‐derived data and spectral modeling to analyze waves in 10 polynya events in the Terra Nova Bay, Antarctica. We estimate the wind‐input reduction factor over frazil/grease ice in the wave‐energy balance equation at 0.56. By calibrating the model to satellite observations we show that exact treatment of quadruplet wave–wave interactions (as opposed to the default Discrete Interaction Approximation) is necessary to fit the model to data, and that the power n > 4 in the sea‐ice source term Sice ∼ fn (where f denotes wave frequency) is required to reproduce the strong attenuation of high‐frequency waves observed for frazil streaks. We use a very‐high resolution satellite image of a fragment of one of the polynyas to determine whitecap fraction. We show that there are more than twofold differences in whitecap fraction over ice‐free and ice‐covered regions, and that the model produces realistic whitecap fractions without any tuning of the whitecapping source term. Finally, we estimate the polynya‐area‐integrated wind input, energy dissipation due to whitecapping, and whitecap fraction to be on average below 25%, 10% and 30%, respectively, of the corresponding open‐water values.

A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion

This work focuses on an inversion problem derived from parametric partial differential equations (PDEs) with an infinite-dimensional parameter, represented as a coefficient function. The objective is to estimate this coefficient function accurately despite having only noisy measurements of the PDE solution at sparse input points. Conventional methods for inversion require numerous calls to a refined PDE solver, resulting in significant computational complexity, especially for challenging PDE problems, making the inference of the coefficient function practically infeasible. To address this issue, we propose a MCMC method that combines an deep learning-based surrogate and Gaussian process parameterization to efficiently infer the posterior of the coefficient function. The surrogate model is a combination of a cost-effective coarse PDE solver and a neural network-based transformation which provides an approximate solution derived from the refined PDE solver but based on the coarse PDE solution. The coefficient function is represented by Gaussian process with finite number of spatially dependent parameters and this parametric representation will be beneficial for the preconditioned Crank-Nicolson (pCN) Markov chain Monte Carlo method to sample efficiently from the posterior distribution. Approximate Bayesian computation method is used for constructing an informative dataset for the transformation learning. Our numerical examples demonstrate the effectiveness of this approach in accurately estimating the coefficient function while significantly reducing the computational burden associated with traditional inversion techniques.

Parametric detailing of structural walls in reinforced concrete using revit software

The use of BIM has become increasingly important in the development of civil construction and architectural projects. The methodology integrates various design stages and allows for compatibility between different software. The parameterization process in Revit, a software used for architectural design, was researched to assist professionals in creating projects using reinforced concrete structural walls. These walls are flat elements with welded screens as reinforcements to resist structural stresses. The objective of the research was to model and parameterize a wall with an opening and a monolithic model. Firstly, data about the norms and characteristics of structural walls used in Brazil was collected, and technical drawings were produced using AutoCAD. This was done to recognize the parameters that would be used in Revit while modeling. Finally, the parameterization process was initiated using Revit functionalities to model the concrete wall and insert the necessary reinforcement using the "rebar" tool that the software offers. Technical drawings such as plans and cuts were produced from these models, which showed the sections of concrete and steel elements. Automated quantitative data was also obtained.

A new process-based and scale-aware desert dust emission scheme for global climate models – Part II: Evaluation in the Community Earth System Model version 2 (CESM2)

Abstract. Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes.