Climate simulations and projections with a super-parameterized climate model
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58 GEOSCIENCES↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison
To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. In conclusion, correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.
54 ENVIRONMENTAL SCIENCES↗
Crop models: integrating systems from the molecular to global for agricultural productivity and sustainability
Mathematical models that simulate crop growth in response to environmental conditions and management practices are essential tools for exploring agriculture-based strategies to address food security and environmental sustainability challenges. Early applications of crop models focused on supporting farmers in making management decisions. Applications have since expanded to estimating future impacts on local and global food production from changing climates. Emerging applications of crop models aim to leverage how these models integrate plant processes across biological scales to identify engineering or breeding strategies that account for environmentally-responsive dynamics at field scales and for exploring solutions to improve sustainability. In this review, we highlight recent studies across these four broad application areas and highlight potential future directions for the crop modeling field.
Piao, Ximin [Univ. of Illinois at Urbana-Champaign↗
A multi-algorithm approach for modeling coastal wetland eco-geomorphology
Coastal wetlands play an important role in the global water and biogeochemical cycles. Climate change makes it more difficult for these ecosystems to adapt to the fluctuation in sea levels and other environmental changes. Given the importance of eco-geomorphological processes for coastal wetland resilience, many eco-geomorphology models differing in complexity and numerical schemes have been developed in recent decades. However, their divergent estimates of the response of coastal wetlands to climate change indicate that substantial structural uncertainties exist in these models. To investigate the structural uncertainty of coastal wetland eco-geomorphology models, we developed a multi-algorithm model framework of eco-geomorphological processes, such as mineral accretion and organic matter accretion, within a single hydrodynamics model. The framework is designed to explore possible ways to represent coastal wetland eco-geomorphology in Earth system models and reduce the related uncertainties in global applications. We tested this model framework at three representative coastal wetland sites: two saltmarsh wetlands (Venice Lagoon and Plum Island Estuary) and a mangrove wetland (Hunter Estuary). Through the model–data comparison, we showed the importance of using a multi-algorithm ensemble approach for more robust predictions of the evolution of coastal wetlands. We also found that more observations of mineral and organic matter accretion at different elevations of coastal wetlands and evaluation of the coastal wetland models at different sites in diverse environments can help reduce the model uncertainty.
58 GEOSCIENCES↗
Avoiding climate change uncertainties in Strategic Environmental Assessment
This article is concerned with how Strategic Environmental Assessment (SEA) practice handles climate change uncertainties within the Danish planning system. First, a hypothetical model is set up for how uncertainty is handled and not handled in decision-making. The model incorporates the strategies ‘reduction’ and ‘resilience’, ‘denying’, ‘ignoring’ and ‘postponing’. Second, 151 Danish SEAs are analysed with a focus on the extent to which climate change uncertainties are acknowledged and presented, and the empirical findings are discussed in relation to the model. The findings indicate that despite incentives to do so, climate change uncertainties were systematically avoided or downplayed in all but 5 of the 151 SEAs that were reviewed. Finally, two possible explanatory mechanisms are proposed to explain this: conflict avoidance and a need to quantify uncertainty.
54 ENVIRONMENTAL SCIENCES↗
The future of wind energy in California: Future projections with the Variable-Resolution CESM
Shifting wind patterns are an expected consequence of global climate change, with direct implications for wind energy production. However, wind is notoriously difficult to predict, and significant uncertainty remains in our understanding of climate change impacts on existing wind generation capacity. Historical and future wind climatology and associated capacity factors at five wind turbine sites in California are examined. Historical (1980–2000) and mid-century (2030–2050) simulations were produced using the Variable-Resolution Community Earth System Model (VR-CESM) to understand how these wind generation sites are expected to be impacted by climate change. A high-resolution statistically downscaled WRF product provided by DNV GL, reanalysis datasets MERRA-2, CFSR, NARR, and observational data were used for model validation and comparison. These projections suggest that wind power generation capacity throughout the state is expected to increase during the summer, and decrease during fall and winter, based on significant changes at several wind farm sites. Finally, this study improves the characterization of uncertainty around the magnitude and variability in space and time of California's wind resources in the near future, and also enhances our understanding of the physical mechanisms related to the trends in wind resource variability.
17 WIND ENERGY↗
Projected Impacts of Climate, Urbanization, Water Management, and Wetland Restoration on Waterbird Habitat in California’s Central Valley
The Central Valley of California is one of the most important regions for wintering waterbirds in North America despite extensive anthropogenic landscape modification and decline of historical wetlands there. Like many other mediterranean-climate ecosystems across the globe, the Central Valley has been subject to a burgeoning human population and expansion and intensification of agricultural and urban development that have impacted wildlife habitats. Future effects of urban development, changes in water supply management, and precipitation and air temperature related to global climate change on area of waterbird habitat in the Central Valley are uncertain, yet potentially substantial. Therefore, we modeled area of waterbird habitats for 17 climate, urbanization, water supply management, and wetland restoration scenarios for years 2006–2099 using a water resources and scenario modeling framework. Planned wetland restoration largely compensated for adverse effects of climate, urbanization, and water supply management changes on habitat areas through 2065, but fell short thereafter for all except one scenario. Projected habitat reductions due to climate models were more frequent and greater than under the recent historical climate and their magnitude increased through time. After 2065, area of waterbird habitat in all scenarios that included severe warmer, drier climate was projected to be >15% less than in the “existing” landscape most years. The greatest reduction in waterbird habitat occurred in scenarios that combined warmer, drier climate and plausible water supply management options affecting priority and delivery of water available for waterbird habitats. This scenario modeling addresses the complexity and uncertainties in the Central Valley landscape, use and management of related water supplies, and climate to inform waterbird habitat conservation and other resource management planning. Results indicate that increased wetland restoration and additional conservation and climate change adaptation strategies may be warranted to maintain habitat adequate to support waterbirds in the Central Valley.
54 ENVIRONMENTAL SCIENCES↗
Rising Temperatures Reduce Global Wheat Production
Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32◦ degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time.
agriculture↗
Mean precipitation change from a deepening troposphere
Global climate models robustly predict that global mean precipitation should increase at roughly 2–3% K - 1 , but the origin of these values is not well understood. Here we develop a simple theory to help explain these values. This theory combines the well-known radiative constraint on precipitation, which says that condensation heating from precipitation is balanced by the net radiative cooling of the free troposphere, with an invariance of radiative cooling profiles when expressed in temperature coordinates. These two constraints yield a picture in which mean precipitation is controlled primarily by the depth of the troposphere, when measured in temperature coordinates. We develop this theory in idealized simulations of radiative–convective equilibrium and also demonstrate its applicability to global climate models.
54 ENVIRONMENTAL SCIENCES↗
A Modeling Framework for Evaluating the Drought Resilience of a Surface Water Supply System under Non-stationarity
The future resilience of water supply systems is unprecedentedly challenged by non-stationary processes, such as fast population growth and a changing climate. A thorough understanding of how these non-stationarities impact water supply resilience is vital to support sustainable decision making, particularly for large cities in arid and/or semi-arid regions. In this study, a novel modeling framework, which integrates hydrological processes and water management, was established over a representative water limited metropolitan area to evaluate the impacts of water availability and water demand on reservoir storage and water supply reliability. In this framework, climate change induced drought events were selected from statistically downscaled Coupled Model Intercomparison Project Phase 5 outputs under the Representative Concentration Pathway 8.5 scenario, while future water demand was estimated by the product of projected future population and per capita water use. Compared with the first half of the 21st century (2000–2049), reservoir storage and water supply reliability during the second half century (2050–2099) are projected to reduce by 16.1% and 14.2%, respectively. While both future multi-year droughts and population growth will lower water supply resilience, the uncertainty associated with future climate projection is larger than that associated with urbanization. To reduce the drought risks, a combination of mitigation strategies (e.g., additional conservation, integrating new water sources, and water use redistribution) was found to be the most efficient approach and can significantly improve water supply reliability by as much as 15.9%.
Zhao, Gang (ORCID:0000000327370530)↗
Continued development and validation of the AER two-dimensional interactive model
Results from two-dimensional chemistry-transport models have been used to predict the future behavior of ozone in the stratosphere. Since the transport circulation, temperature, and aerosol surface area are fixed in these models, they cannot account for the effects of changes in these quantities, which could be modified because of ozone redistribution and/or other changes in the troposphere associated with climate changes. Interactive two-dimensional models, which calculate the transport circulation and temperature along with concentrations of the chemical species, could provide answers to complement the results from three-dimension model calculations. In this project, we performed the following tasks in pursuit of the respective goals: (1) We continued to refine the 2-D chemistry-transport model; (2) We developed a microphysics model to calculate the aerosol loading and its size distribution; (3) The treatment of physics in the AER 2-D interactive model were refined in the following areas--the heating rate in the troposphere, and wave-forcing from propagation of planetary waves.
Ko, M. K. W.↗
Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5
Abstract Earth system models (ESMs) have been rapidly developed in recent decades to advance our understanding of climate change‐carbon cycle feedback. However, those models are massive in coding, require expensive computational resources, and have difficulty in diagnosing their performance. It is highly desirable to develop ESMs with modularity and effective diagnostics. Toward these goals, we implemented a matrix approach to the Community Land Model version 5 (CLM5) to represent carbon and nitrogen cycles. Specifically, we reorganized 18 balance equations each for carbon and nitrogen cycles among the 18 vegetation pools in the original CLM5 into two matrix equations. Similarly, 140 balance equations each for carbon and nitrogen cycles among the 140 soil pools were reorganized into two additional matrix equations. The vegetation carbon and nitrogen matrix equations are connected to soil matrix equations via litterfall. The matrix equations fully reproduce simulations of carbon and nitrogen dynamics by the original model. The computational cost for forwarding simulation of the CLM5 matrix model was 26% more expensive than the original model, largely due to calculation of additional diagnostic variables, but the spin‐up computational cost was significantly saved. We showed a case study on modeled soil carbon storage under two forcing data sets to illustrate the diagnostic capability that the matrix approach uniquely offers to understand simulation results of global carbon and nitrogen dynamics. The successful implementation of the matrix approach to CLM5, one of the most complex land models, demonstrates that most, if not all, the biogeochemical models can be reorganized into the matrix form to gain high modularity, effective diagnostics, and accelerated spin‐up.
58 GEOSCIENCES↗
Current developments in soil organic matter modeling and the expansion of model applications. A review
It is important to note that Soil organic matter (SOM) is a great natural resource. It is fundamental to soil and ecosystem functions across a wide range of scales, from site-specific soil fertility and water holding capacity to global biogeochemical cycling. It is also a highly complex material that is sensitive to direct and indirect human impacts. In our SOM research, simulation models play an important role by providing a mathematical framework to integrate, examine, and test the understanding of SOM dynamics. Simulation models of SOM are also increasingly used in more ‘applied’ settings to evaluate human impacts on ecosystem function, and to manage SOM for greenhouse gas mitigation, improved soil health, and sustainable use as a natural resource. Within this context, there is a need to maintain a robust connection between scientific developments in SOM modeling approaches and SOM model applications. This need forms the basis of this review. In this review we first provide an overview of SOM modeling, focusing on SOM theory, data-model integration, and model development as evidenced by a quantitative review of SOM literature. Second, we present the landscape of SOM model applications, focusing on examples in climate change policy. Finally, we conclude by discussing five areas of recent developments in SOM modeling including: (1) microbial roles in SOM stabilization; (2) modeling SOM saturation kinetics; (3) temperature controls on decomposition; (4)SOM dynamics in deep soil layers; and (5)SOM representation in earth system models. Our aim is to comprehensively connect SOM model development to its applications, revealing knowledge gaps in need of focused interdisciplinary attention and exposing pitfalls that, if avoided, can lead to best use of SOM models to support policy initiatives and sustainable land management solutions.
54 ENVIRONMENTAL SCIENCES↗
Estimating heterotrophic respiration at large scales: challenges, approaches, and next steps
Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of "Decomposition Functional Types" (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing models to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present and discuss an example clustering analysis to show how model-produced annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from already-existing PFTs. A similar analysis, incorporating observational data, could form a basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with high-performance computing; rigorous testing of analytical results; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR at large scales.
carbon cycle↗
Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes
Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099
Li, Bonan↗
Midwestern Regional Center of the Doe National Institute for Climatic Change Research
The goal of NICCR (National Institute for Climatic Change Research) was to mobilize university researchers, from all regions of the country, in support of the climatic change research objectives of DOE/BER. The NICCR Midwestern Regional Center (MRC) supported work in the following states: North Dakota, South Dakota, Nebraska, Kansas, Oklahoma, Minnesota, Iowa, Missouri, Wisconsin, Illinois, Michigan, Indiana, and Ohio. The MRC of NICCR was able to support nearly $\$ $8 million in climatic change research, including $\$ $6,671,303 for twenty projects solicited and selected by the MRC over five requests for proposals (RFPs) and $\$ $1,051,666 for the final year of ten projects from the discontinued DOE NIGEC (National Institute for Global Environmental Change) program. The projects selected and funded by the MRC resulted in 135 peer-reviewed publications and supported the training of 25 PhD students and 23 Masters students. Another 36 publications were generated by the final year of continuing NIGEC projects supported by the MRC. The projects funded by the MRC used a variety of approaches to answer questions relevant to the DOE’s climate change research program. These included experiments that manipulated temperature, moisture and other global change factors; studies that sought to understand how the distribution of species and ecosystems might change under future climates; studies that used measurements and modeling to examine current ecosystem fluxes of energy and mass and those that would exist under future conditions; and studies that synthesized existing data sets to improve our understanding of the effects of climatic change on terrestrial ecosystems. In all of these efforts, the MRC specifically sought to identify and quantify responses of terrestrial ecosystems that were not well understood or not well modeled by current efforts. The MRC also sought to better understand and model important feedbacks between terrestrial ecosystems, atmospheric chemistry, and regional and global climate systems. The broad variety of projects the MRC has supported gave us a unique opportunity to greatly improve our ability to predict the future health, composition and function of important agricultural and natural terrestrial ecosystems within the Midwestern Region.
54 ENVIRONMENTAL SCIENCES↗
Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020
Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates is largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields (> 250 m). Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000-2020 across a 12 state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015-2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forests model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008-2014. We also evaluated the full 2000-2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R 2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R 2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000-2020, each standard deviation increase in rainfall delayed planting by ~ 2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.
54 ENVIRONMENTAL SCIENCES↗
Change in terrestrial ecosystem water‐use efficiency over the last three decades
Abstract Defined as the ratio between gross primary productivity ( GPP ) and evapotranspiration ( ET ), ecosystem‐scale water‐use efficiency ( EWUE ) is an indicator of the adjustment of vegetation photosynthesis to water loss. The processes controlling EWUE are complex and reflect both a slow evolution of plants and plant communities as well as fast adjustments of ecosystem functioning to changes of limiting resources. In this study, we investigated EWUE trends from 1982 to 2008 using data‐driven models derived from satellite observations and process‐oriented carbon cycle models. Our findings suggest positive EWUE trends of 0.0056, 0.0007 and 0.0001 g C m −2 mm −1 yr −1 under the single effect of rising CO 2 (‘ CO 2 ’), climate change (‘ CLIM ’) and nitrogen deposition (‘ NDEP ’), respectively. Global patterns of EWUE trends under different scenarios suggest that (i) EWUE ‐ CO 2 shows global increases, (ii) EWUE ‐ CLIM increases in mainly high latitudes and decreases at middle and low latitudes, (iii) EWUE ‐ NDEP displays slight increasing trends except in west Siberia, eastern Europe, parts of North America and central Amazonia. The data‐driven MTE model, however, shows a slight decline of EWUE during the same period (−0.0005 g C m −2 mm −1 yr −1 ), which differs from process‐model (0.0064 g C m −2 mm −1 yr −1 ) simulations with all drivers taken into account. We attribute this discrepancy to the fact that the nonmodeled physiological effects of elevated CO 2 reducing stomatal conductance and transpiration ( TR ) in the MTE model. Partial correlation analysis between EWUE and climate drivers shows similar responses to climatic variables with the data‐driven model and the process‐oriented models across different ecosystems. Change in water‐use efficiency defined from transpiration‐based WUE t ( GPP / TR ) and inherent water‐use efficiency ( IWUE t , GPP × VPD / TR ) in response to rising CO 2 , climate change, and nitrogen deposition are also discussed. Our analyses will facilitate mechanistic understanding of the carbon–water interactions over terrestrial ecosystems under global change.
Huang, Mengtian↗