Climate Impacts on Water Security
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climate, water security↗
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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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Employing Tiny Helpers to Boost Biomass Productivity and Climate Resilience of Bioenergy and Food Crops.
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Algae, Plants, Climate, Biomass, Biofuels↗
Science Area 1: Standard Award: Model-Data Fusion to Examine Multiscale Dynamical Controls on Snow Cover and Critical Zone Moisture Inputs (Final Report)
In many mountain watersheds of the world, seasonal snowpacks play an important role as natural reservoirs of water. Seasonal snowpacks accumulate water during cold, wet winter months that subsequently melts. Downstream communities depend on water from melting seasonal snowpacks to support agricultural, industrial, and municipal water needs. Rapidly melting snowpacks can also present a flooding hazard, particularly if snowpacks melt at rates faster than anticipated and where adequate reservoir capacity is unavailable to buffer river flows associated with melt. The spatial and temporal dynamics of snow accumulation and melt also play an important role in supporting upland ecosystems in mountain landscapes. Snowmelt provides soil moisture that enable terrestrial ecosystem productivity and exert control on soil microorganisms that play important roles in global carbon cycles. Climate warming is gradually decreasing the amount of precipitation in mountain watersheds arriving as snow, presenting potentially profound disruptions to mountain ecosystems, as well as downstream delivery of water. The overarching goal of this project was to understand how interactions between the near-surface atmosphere and surface topography control the input, accumulation, retention, and release of water from mountain snowpacks. Over a 5-year period, this project pursued an approach combining high-resolution regional climate modeling, satellite and airborne remote sensing data, and ground-based observations to develop and analyze benchmark datasets to address overarching science questions and hypotheses. Key products include a continuous, long-term, high spatiotemporal resolution (1 km/1 hr) dataset characterizing key climate variables in the Upper Colorado River Basin. The dataset included historical estimates of precipitation, temperature, humidity, solar and longwave radiation, and wind speeds. Additionally, the project developed a 20+ year long, 30 m spatial, daily temporal multi-sensor dataset characterizing snow presence/absence in the East/Taylor River watersheds in the Central Rocky Mountains of Colorado. The project supported training of 1 postdoctoral scholar, 1 Ph.D. student, and 1 M.S. student.
54 ENVIRONMENTAL SCIENCES↗
Collaborative Research: Understanding air‐sea feedbacks to the MJO through process evaluation of observations and E3SM experiments (Final Technical Report)
This project explored the role of tropical oceans in regulating cloud populations within the Madden-Julian oscillation (MJO)–a large-scale tropical weather disturbance that influences extreme weather events across the globe by affecting the position of the jet stream and patterns of moisture transport. Our project sought to 1) document ocean feedback strength to the MJO in climate models, 2) understand how air-sea interactions affect the intensity, organization, and propagation of MJO convection, 3) utilize existing observations to identify sources of model bias for tropical air-sea interactions, and 4) better understand the effects of slow and fast ocean variability on MJO cloudiness. We found that transfers of heat and moisture across the air-sea interface (i.e., surface fluxes) in most climate models are over-supportive in maintaining MJO convection and under-supportive in propagating MJO convection. This can lead to misrepresentations in the frequency of extreme weather events across the globe through erroneous MJO feedbacks to jet stream position and moisture transport. Furthermore, using observations collected by ocean moorings across the tropical oceans, we showed that most models greatly overestimate surface evaporation in the subtropics, and that this bias is likely one contributor to a pervasive bias among climate models in their ability to simulate the mean state of rainfall over the tropical oceans (i.e., the “double ITCZ bias”). Replacing the default surface flux algorithm in two climate models with a state-of-the-art surface flux algorithm modestly reduced the double ITCZ bias in those models. Our work produced two model diagnostics packages that are now publicly available through DOE and NOAA model diagnostics archives. The first, the Analysis of Scales of Precipitation (ASoP) diagnoses scales of precipitation variability across space and time, with temporal resolution as fine as the model integration timestep. The second, the air-sea flux diagnostic diagnoses model surface fluxes and allows direct comparison of model fluxes to observations without having to recompute fluxes using model output and a state-of-the-art flux algorithm. These diagnostics, which were developed using climate models, can be readily applied to regional and global forecast model output to improve forecasts for the benefit of society.
54 ENVIRONMENTAL SCIENCES↗
The trailing edge is everywhere: tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts
Given the importance of climate in shaping species’ geographic distributions, climate change poses an existential threat to biodiversity. Climate envelope modeling, the predominant approach used to quantify this threat, presumes that individuals in populations respond to climate variability and change according to species-level responses inferred from spatial occurrence data—such that individuals at the cool edge of a species’ distribution should benefit from warming (the “leading edge”), whereas individuals at the warm edge should suffer (the “trailing edge”). Using 1,558 tree-ring time series of an aridland pine (Pinus edulis) collected at 977 locations across the species’ distribution, we found that trees everywhere grow less in warmer-than-average and drier-than-average years. Ubiquitous negative temperature sensitivity indicates that individuals across the entire distribution should suffer with warming—the entire distribution is a trailing edge. Species-level responses to spatial climate variation are opposite in sign to individual-scale responses to time-varying climate for approximately half the species’ distribution with respect to temperature and the majority of the species’ distribution with respect to precipitation. These findings, added to evidence from the literature for scale-dependent climate responses in hundreds of species, suggest that correlative, equilibrium-based range forecasts may fail to accurately represent how individuals in populations will be impacted by changing climate. A scale-dependent view of the impact of climate change on biodiversity highlights the transient risk of extinction hidden inside climate envelope forecasts and the importance of evolution in rescuing species from extinction whenever local climate variability and change exceeds individual-scale climate tolerances.
54 ENVIRONMENTAL SCIENCES↗
Tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts
Given the importance of climate in shaping species’ geographic distributions, climate change poses an existential threat to biodiversity. Climate envelope modeling, the predominant approach used to quantify this threat, presumes that individuals in populations respond to climate variability and change according to species-level responses inferred from spatial occurrence data—such that individuals at the cool edge of a species’ distribution should benefit from warming (the “leading edge”), whereas individuals at the warm edge should suffer (the “trailing edge”). Using 1,558 tree-ring time series of an aridland pine (Pinus edulis) collected at 977 locations across the species’ distribution, we found that trees everywhere grow less in warmer-than-average and drier-than-average years. Ubiquitous negative temperature sensitivity indicates that individuals across the entire distribution should suffer with warming—the entire distribution is a trailing edge. Species-level responses to spatial climate variation are opposite in sign to individual-scale responses to time-varying climate for approximately half the species’ distribution with respect to temperature and the majority of the species’ distribution with respect to precipitation. These findings, added to evidence from the literature for scale-dependent climate responses in hundreds of species, suggest that correlative, equilibrium-based range forecasts may fail to accurately represent how individuals in populations will be impacted by changing climate. A scale-dependent view of the impact of climate change on biodiversity highlights the transient risk of extinction hidden inside climate envelope forecasts and the importance of evolution in rescuing species from extinction whenever local climate variability and change exceeds individual-scale climate tolerances.
54 ENVIRONMENTAL SCIENCES↗
The Critical Role Of Conversion Cost And Comparative Advantage In Modeling Agricultural Land Use Change
The difference in land use modeling approaches is an important uncertain factor in evaluating future climate scenarios in global economic models. We compare five widely used land use modeling approaches: constrained optimization, constant elasticity of transformation (CET), the additive form of constant elasticity of transformation (ACET), logit, and Ricardian. We demonstrate that the approaches differ not only by the extent of parameter uses but also by the definition of conversion cost and the consideration of comparative advantage implied by land heterogeneity. We develop a generalized hybrid approach that incorporates ACET/logit and Ricardian to account for both conversion cost and comparative advantage. We use this hybrid approach to estimate future climate impacts on agriculture. We find a welfare loss of about 0.38–0.46% of the global GDP. We demonstrate that ignoring land heterogeneity or land conversion costs underestimates climate impacts on agricultural production and welfare.
54 ENVIRONMENTAL SCIENCES↗
Evolution of the Earth Observing System (EOS) Data and Information System (EOSDIS)
One of the strategic goals of the U.S. National Aeronautics and Space Administration (NASA) is to "Develop a balanced overall program of science, exploration, and aeronautics consistent with the redirection of the human spaceflight program to focus on exploration". An important sub-goal of this goal is to "Study Earth from space to advance scientific understanding and meet societal needs." NASA meets this subgoal in partnership with other U.S. agencies and international organizations through its Earth science program. A major component of NASA s Earth science program is the Earth Observing System (EOS). The EOS program was started in 1990 with the primary purpose of modeling global climate change. This program consists of a set of space-borne instruments, science teams, and a data system. The instruments are designed to obtain highly accurate, frequent and global measurements of geophysical properties of land, oceans and atmosphere. The science teams are responsible for designing the instruments as well as scientific algorithms to derive information from the instrument measurements. The data system, called the EOS Data and Information System (EOSDIS), produces data products using those algorithms as well as archives and distributes such products. The first of the EOS instruments were launched in November 1997 on the Japanese satellite called the Tropical Rainfall Measuring Mission (TRMM) and the last, on the U.S. satellite Aura, were launched in July 2004. The instrument science teams have been active since the inception of the program in 1990 and have participation from Brazil, Canada, France, Japan, Netherlands, United Kingdom and U.S. The development of EOSDIS was initiated in 1990, and this data system has been serving the user community since 1994. The purpose of this chapter is to discuss the history and evolution of EOSDIS since its beginnings to the present and indicate how it continues to evolve into the future. this chapter is organized as follows. Sect. 7.2 provides a discussion of EOSDIS, its elements and their functions. Sect. 7.3 provides details regarding the move towards more distributed systems for supporting both the core and community needs to be served by NASA Earth science data systems. Sect. 7.4 discusses the use of standards and interfaces and their importance in EOSDIS. Sect. 7.5 provides details about the EOSDIS Evolution Study. Sect. 7.6 presents the implementation of the EOSDIS Evolution plan. Sect. 7.7 briefly outlines the progress that the implementation has made towards the 2015 Vision, followed by a summary in Sect. 7.8.
Ramapriyan, Hampapuram K.↗
Soil Microbiomes Under Climate Change and Implications for Carbon Cycling
Communities of soil microorganisms (soil microbiomes) play a major role in biogeochemical cycles and support of plant growth. Here we focus primarily on the roles that the soil microbiome plays in cycling soil organic carbon and the impact of climate change on the soil carbon cycle. We first discuss current challenges in understanding the roles carried out by highly diverse and heterogeneous soil microbiomes and review existing knowledge gaps in understanding how climate change will impact soil carbon cycling by the soil microbiome. Because soil microbiome stability is a key metric to understand as the climate changes, we discuss different aspects of stability, including resistance, resilience, and functional redundancy. We then review recent research pertaining to the impact of major climate perturbations on the soil microbiome and the functions that they carry out. Finally, we review new experimental methodologies and modeling approaches under development that should facilitate our understanding of the complex nature of the soil microbiome to better predict its future responses to climate change.
54 ENVIRONMENTAL SCIENCES↗
Exploring ENSO-Induced Anomalies over North America in Historical and Future Climate Simulations That Use HadGEM2-ESM Output to Drive WRF
Projected changes to the El Niño Southern Oscillation (ENSO) climate mode have been explored using global Earth system models (ESMs). Regional expressions of such changes have yet to be fully advanced and may require the use of regional downscaling. Here, we employ regional climate modeling (RCM) using the Weather Research and Forecasting (WRF) model at convection-permitting resolution and nested in output from the HadGEM2 ESM. We quantify ENSO teleconnections to temperature and precipitation anomalies in historical and future climate scenarios over eastern North America. Two paired simulations are run, a strong El Niño (positive ENSO phase) and a weak La Niña (negative ENSO phase), for the historical and future years. The HadGEM2 direct output and HadGEM2-WRF simulation output are compared to the anomalies derived from the NOAA ENSO Climate Normals dataset. The near-surface temperature and precipitation differences by ENSO phase, as represented by the HadGEM2-WRF historical simulations, show a poor degree of association with the NOAA ENSO Climate Normals, in part because of the large biases in the HadGEM2 model. Downscaling with the WRF model does improve the agreement with the observations, and large discrepancies remain. The model chain HadGEM2-WRF reverses the sign of the ENSO phase response over eastern North America under simulations of the future climate with high greenhouse gas forcing, but due to the poor agreement with the NOAA ENSO Climate Normals it is difficult to assign confidence to this prediction.
54 ENVIRONMENTAL SCIENCES↗
Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin
The Nile River basin is one of the global hotspots vulnerable to climate change impacts because of a fast-growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from phase six of the Coupled Model Intercomparison Project (CMIP6) benchmarked against a high-spatial-resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). Next, a Bayesian model averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is underdispersive and is a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change vary considerably across the Nile basin. Specifically, it is found that projected changes in the two headwaters basins, namely, the Blue Nile and Upper White Nile, are 0.03% and -1.65%, respectively; both are statistically insignificant at α = 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the Upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Data Democratization: Challenges and Opportunities
Democratizing Earth data is one of the challenges many organizations around the world face in order to maximize the use of their Earth data for research, applications, education, and societal benefits. For example, at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC), over 1600 global and regional datasets in several NASA Earth science focus areas, including atmospheric composition, water and energy cycles, and climate variability, are archived and distributed to the public. Giovanni, the Geospatial Interactive Online Visualization and Analysis Infrastructure, was developed by GES DISC to facilitate data access and exploration, especially for novice users of Earth science. With Giovanni, users can analyze and visualize over 2000 Earth science variables (e.g., precipitation, aerosol, surface wind) without downloading data, software, the expert understanding of data formats and structures, and coding skills, lowering the barrier to data analysis/comparison by preprocessing and accessing to the data. Results of data analysis and visualization can be accessed in several popular formats (e.g., NetCDF, CSV). As a result of Giovanni's efforts, more than 3000 referral papers have been published in various fields. In spite of this, Giovanni is still difficult to use for some users. For instance, if one searches for "precipitation," it will return over 150 related variables. The question is, which one to use? Furthermore, variables from different data providers (e.g., satellites and models) are named differently with different units, further confusing users, especially those outside the communities. Data democratization is complex and multifaceted. Challenges include service and data discovery, user experiences, visualization, data quality, trustworthiness, and more. In this presentation, we will examine Giovanni as an example of challenges and opportunities in developing data democratization services.
data democratization↗
Tracking Aerosol Convective Interactions Experiment (TRACER) Science Plan
Convective clouds play an important role in the Earth’s climate system as a driver of large-scale circulations and a primary mechanism for the transport of heat, moisture, aerosols, and momentum throughout the troposphere. Despite their climatic importance, multi-scale models continue to have persistent biases produced by insufficient representation of convective clouds. This is the result of an incomplete understanding of key processes such as convective initiation, updraft and downdraft dynamics, cloud and precipitation microphysics, and aerosol-convection interactions. The Aerosol-Cloud-Precipitation-Climate Initiative, an international research group dedicated to advancing understanding of aerosol impacts on clouds relevant to climate, has identified the Houston, Texas region as an optimal location for targeted studies of aerosol-convection interactions within frequently developing isolated deep convection. Houston lies within a humid subtropical climate regime, where onshore flow and sea-breeze convection interact with a range of aerosol conditions associated with Houston’s urban and industrial emissions. Pilot studies have suggested that convective clouds in this region are potentially significantly impacted by the varying aerosol conditions. To increase our understanding of convective cloud life cycles and aerosol-convection interactions, the Tracking Aerosol Convection Interactions Experiment (TRACER) aims to collect a comprehensive data set focused on the evolution of convective clouds and the environment (including aerosol, cloud, thermodynamics, and lightning) in which the clouds initiate, grow, and decay. A unique component of TRACER is that a large number of individual, isolated convective cells will be tracked and measured in high spatial and temporal resolution for the purposes of: (i) Characterizing and linking convective cloud kinematic and microphysical life cycles, (ii) Quantifying environmental thermodynamic and kinematic controls on convective life cycle properties, (iii) Isolating and quantifying the impacts of aerosol properties on convective cloud kinematic and microphysical evolution. TRACER includes a one-year deployment of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s first ARM Mobile Facility (AMF1) and second-generation C-Band Scanning ARM Precipitation Radar (CSAPR2) aimed at collecting statistical data sets of cloud, precipitation, atmospheric state, and aerosol under varying aerosol loading and local circulation (sea breeze and urban heat island) conditions. A four-month intensive operational period (IOP) from June through September will include deployment of an ancillary site with aerosol, atmospheric state, and precipitation measurements at a location rarely impacted by Houston’s anthropogenic emissions, which will be commonplace near the AMF1 site. Convective cell tracking by the CSAPR2 will provide high-resolution polarimetric and velocity observations under a range of environmental (including aerosol loading) conditions. High-frequency radiosonde launches will capture quickly evolving thermodynamic and kinematic conditions near convective cells, a requirement for isolating aerosol effects on clouds. TRACER will additionally leverage a lightning mapping array, surface meteorological networks, and air pollution networks, and proposals will be entered to deploy additional mobile radar and radiosonde assets during the IOP. This unique combination of cloud, precipitation, lightning, aerosol, and atmospheric state measurements associated with tracked convective cells will ultimately improve our understanding of the convective cloud life cycle and its interaction with individual environmental factors such that improved, next-generation cumulus, microphysics, turbulence, and aerosol parameterizations can be designed.
Convective clouds, aerosols, cloud life cycle, atm↗
ESMs Latent Space Exploration for Uncertainty Quantification and Spatiotemporal Downscaling
This final report for DOE Award DE-SC0023044 presents advances in two key areas of climate modeling: (1) representative climate model selection and (2) Earth System Model (ESM) downscaling using hybrid AI methods. The first section introduces a reordered, three-stage workflow to select representative GCM runs that more effectively balance historical skill with ensemble spread, validated across Texas, Bihar, and New York. The second section introduces two novel super-resolution frameworks, ViSIR and ViFOR, that integrate Vision Transformers with sinusoidal and Fourier-based implicit neural representations. These models achieve state-of-the-art reconstruction accuracy for ESM variables including surface temperature and heat fluxes. The report includes detailed methodology, benchmarks, and results, demonstrating significant gains in uncertainty quantification, spatial fidelity, and scalability for climate-impact studies.
54 ENVIRONMENTAL SCIENCES↗
Nonlinear dynamics and predictability in the atmospheric sciences
Systematic applications of nonlinear dynamics to studies of the atmosphere and climate are reviewed for the period 1987-1990. Problems discussed include paleoclimatic applications, low-frequency atmospheric variability, and interannual variability of the ocean-atmosphere system. Emphasis is placed on applications of the successive bifurcation approach and the ergodic theory of dynamical systems to understanding and prediction of intraseasonal, interannual, and Quaternary climate changes.
Ghil, M.↗
Using TRMM Data To Understand Interannual Variations In the Tropical Water Balance
A significant element of the science rationale for TRMM centered on assembling rainfall data needed to validate climate models-- climatological estimates of precipitation, its spatial and temporal variability, and vertical modes of latent heat release. Since the launch of TRMM, a great interest in the science community has emerged for quantifying interannual variability (IAV) of precipitation and its relationship to sea-surface temperature (SST) changes. The fact that TRMM has sampled one strong warm/ cold ENSO couplet, together with the prospect for a mission lifetime approaching ten years, has bolstered this interest in these longer time scales. Variability on a regional basis as well as for the tropics as a whole is of concern. Our analysis of TRMM results so far has shown surprising lack of concordance between various algorithms in quantifying IAV of precipitation. The first objective of this talk is to quantify the sensitivity of tropical precipitation to changes in SSTs. We analyze performance of the 3A11, 3A25, and 3B31 algorithms and investigate their relationship to scattering-- based algorithms constructed from SSM/I and TRMM 85 kHz data. The physical basis for the differences (and similarities) in depicting tropical oceanic and land rainfall will be discussed. We argue that scattering-based estimates of variability constitute a useful upper bound for precipitation variations. These results lead to the second question addressed in this talk-- How do TRMM precipitation / SST sensitivities compare to estimates of oceanic evaporation and what are the implications of these uncertainties in determining interannual changes in large-scale moisture transport? We summarize results of an analysis performed using COADS data supplemented by SSM/I estimates of near-surface variables to assess evaporation sensitivity to SST. The response of near 5 W sq m/K is compared to various TRMM precipitation sensitivities. Implied moisture convergence over the tropics and its sensitivity to errors of these algorithms is discussed.
Robertson, Franklin R.↗
Tracking Aerosol Convective Interactions Experiment (TRACER) Science Plan
Convective clouds play an important role in the Earth's climate system as a driver of large-scale circulations and a primary mechanism for the transport of heat, moisture, aerosols, and momentum throughout the troposphere. Despite their climatic importance, multi-scale models continue to have persistent biases produced by insufficient representation of convective clouds. This is the result of an incomplete understanding of key processes such as convective initiation, updraft and downdraft dynamics, cloud and precipitation microphysics, and aerosol-convection interactions.The Aerosol-Cloud-Precipitation-Climate Initiative, an international research group dedicated to advancing understanding of aerosol impacts on clouds relevant to climate, has identified the Houston, Texas region as an optimal location for targeted studies of aerosol-convection interactions within frequently developing isolated deep convection. Houston lies within a humid subtropical climate regime, where onshore flow and sea-breeze convection interact with a range of aerosol conditions associated with Houston's urban and industrial emissions. Pilot studies have suggested that convective clouds in this region are potentially significantly impacted by the varying aerosol conditions.
Jensen, M.↗
Communicating the Needs of Climate Change Policy Makers to Scientists
This chapter will describe the challenges that earth scientists face in developing science data products relevant to decision maker and policy needs, and will describe strategies that can improve the two-way communication between the scientist and the policy maker. Climate change policy and decision making happens at a variety of scales - from local government implementing solar homes policies to international negotiations through the United Nations Framework Convention on Climate Change. Scientists can work to provide data at these different scales, but if they are not aware of the needs of decision makers or understand what challenges the policy maker is facing, they are likely to be less successful in influencing policy makers as they wished. This is because the science questions they are addressing may be compelling, but not relevant to the challenges that are at the forefront of policy concerns. In this chapter we examine case studies of science-policy partnerships, and the strategies each partnership uses to engage the scientist at a variety of scales. We examine three case studies: the global Carbon Monitoring System pilot project developed by NASA, a forest biomass mapping effort for Silvacarbon project, and a forest canopy cover project being conducted for forest management in Maryland. In each of these case studies, relationships between scientists and policy makers were critical for ensuring the focus of the science as well as the success of the decision-making.
Brown, Molly E.↗