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DOE OSTI2024

Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics

A large and ever-growing body of geophysical information is measured in campaigns and at specialized observatories as a part of scientific expeditions and experiments. These collections of observed data include many essential climate variables (as defined by the Global Climate Observing System) but are often distinguished by a wide range of additional non-routine measurements that are designed to not only document the state of the environment but also the drivers that contribute to that state. These field data are used not only to further understand environmental processes through observation-based studies but also to provide baseline data to test model performance and to codify understanding to improve predictive capabilities. To address the considerable barriers and difficulty in utilizing these diverse and complex data for observation–model research, the Merged Observatory Data File (MODF) concept has been developed. A MODF combines measurements from multiple instruments into a single file that complies with well-established data format and metadata practices and has been designed to parallel the development of corresponding Merged Model Data Files (MMDFs). Using the MODF and MMDF protocols will facilitate the evolution of model intercomparison projects into model intercomparison and improvement projects by putting observation and model data “on the same page” in a timely manner. The MODF concept was developed especially for weather forecast model studies in the Arctic. The surprisingly complex process of implementing MODFs in that context refined the concept itself. Thus, this article explains the concept of MODFs by providing details on the issues that were revealed and resolved during that first specific implementation. Detailed instructions are provided on how to make MODFs, and this article can be considered a MODF creation manual.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1991

Satellite orbit considerations for a global change technology architecture trade study

A study was conducted to determine satellite orbits for Earth observation missions aimed at obtaining data for assessing global climate change. A multisatellite system is required to meet the scientific requirements for temporal coverage over the globe. The best system consists of four Sun-synchronous satellites equally spaced in local time of equatorial crossing. This system can obtain data every three hours for all regions. Several other satellite systems consisting of combinations of Sun-synchronous orbits and either the Space Station Freedom or a mid-latitude equatorial satellite can provide three to six hour temporal coverage, which is sufficient for measuring many of the parameters required for the global change monitoring mission. Geosynchronous satellites are required to study atmospheric and surface processes involving variations on the order of a few minutes to an hour. Two or more geosynchronous satellites can be relocated in longitude to study processes over selected regions of Earth.

Harrison, Edwin F.↗

NASA NTRS1998

NASA Ocean Altimeter Pathfinder Project: Data Processing Handbook - Report 1

The NOAA/NASA Pathfinder program was created by the Earth Observing System (EOS) Program Office to determine how satellite-based data sets can be processed and used to study global change. The data sets are designed to be long time-sedes data processed with stable calibration and community consensus algorithms to better assist the research community. The Ocean Altimeter Pathfinder Project involves the reprocessing of all altimeter observations with a consistent set of improved algorithms, based on the results from TOPEX/POSEIDON (T/P), into easy-to-use data sets for the oceanographic community for climate research. This report describes the processing schemes used to produce a consistent data set and two of the products derived f rom these data. Other reports have been produced that: a) describe the validation of these data sets against tide gauge measurements and b) evaluate the statistical properties of the data that are relevant to climate change. The use of satellite altimetry for earth observations was proposed in the early 1960s. The first successful space based radar altimeter experiment was flown on SkyLab in 1974. The first successful satellite radar altimeter was flown aboard the Geos-3 spacecraft between 1975 and 1978. While a useful data set was collected from this mission for geophysical studies, the noise in the radar measured and incomplete global coverage precluded ft from inclusion in the Ocean Altimeter Pathfinder program. This program initiated its analysis with the Seasat mission, which was the first satellite radar altimeter flown for oceanography.

Koblinsky, C. J.↗

NASA NTRS2023

Sensitivity of the MAR Regional Climate Model Snowpack to the Parameterization of the Assimilation of Satellite-Derived Wet-Snow Masks on the Antarctic Peninsula

Both regional climate models (RCMs) and remote sensing (RS) data are essential tools in understanding the response of polar regions to climate change. RCMs can simulate how certain climate variables, such as surface melt, runoff and snowfall, are likely to change in response to different climate scenarios but are subject to biases and errors. RS data can assist in reducing and quantifying model uncertainties by providing indirect observations of the modeled variables on the present climate. In this work, we improve on an existing scheme to assimilate RS wet snow occurrence data with the “Modèle Atmosphérique Régional” (MAR) RCM and investigate the sensitivity of the RCM to the parameters of the scheme. The assimilation is performed by nudging the MAR snowpack temperature to match the presence of liquid water observed by satellites. The sensitivity of the assimilation method is tested by modifying parameters such as the depth to which the MAR snowpack is warmed or cooled, the quantity of water required to qualify a MAR pixel as “wet” (0.1 % or 0.2 % of the snowpack mass being water), and assimilating different RS datasets. Data assimilation is carried out on the Antarctic Peninsula for the 2019–2021 period. The results show an increase in meltwater production (+66.7 % on average, or +95 Gt), along with a small decrease in surface mass balance (SMB) (−4.5 % on average, or −20 Gt) for the 2019–2020 melt season after assimilation. The model is sensitive to the tested parameters, albeit with varying orders of magnitude. The prescribed warming depth has a larger impact on the resulting surface melt production than the liquid water content (LWC) threshold due to strong refreezing occurring within the top layers of the snowpack. The values tested for the LWC threshold are lower than the LWC for typical melt days (approximately 1.2 %) and impact results mainly at the beginning and end of the melting period. The assimilation method will allow for the estimation of uncertainty in MAR meltwater production and will enable the identification of potential issues in modeling near-surface snowpack processes, paving the way for more accurate simulations of snow processes in model projections.

regional climate models↗

NASA NTRS1998

GCM Studies on the Interactions Between Photosynthesis and Climate at Diurnal to Decadal Time Scales

Transpiration, a major component of total evaporation from vegetated surfaces, is an unavoidable consequence of photosynthetic carbon fixation. Because of limiting soil moisture and competition for solar radiation plants invest most of their fixed carbon into structural and hydraulic functions (roots and stems) and solar radiation absorption (leaves). These investments permit individuals to overshadow competitors and provide for transport of water from the soil to the leaves where photosynthesis and transpiration occur. Often low soil moisture or high evaporative demand limit the supply of water to leaves reducing photosynthesis and thus transpiration. The absorption of solar radiation for photosynthesis and dissipation of this energy via radiation, heat, mass and momentum fluxes represents the link between photosynthesis and climate. Recognition of these relationships has led to the development of hydro/energy balance models that are based on the physiological ecology of photosynthesis. We discuss an approach to study vegetation-climate interactions using photosynthesis-centric models embedded in a GCM. The rate at which a vegetated area transpires and photosynthesizes is determined by the physiological state of the vegetation, its amount and its type. The latter two are specified from global satellite data collected since 1982. Climate simulations have been carried out to study how this simulated climate system responds to changes in radiative forcing, physiological capacity, atmospheric CO2, vegetation type and variable vegetation cover observed from satellites during the 1980's. Results from these studies reveal significant feedbacks between the vegetation activity and climate. For example, vegetation cover and physiological activity increases cause the total latent heat flux and precipitation to increase while mean and maximum air temperatures decrease. The reverse occurs if cover or activity'decreases. In general climate response of a particular region was dominated by local processes but we also find evidence that plausible climate-vegetation scenarios lead to changes in global atmospheric circulation and strong non-local influences in some cases.

Collatz, G. James↗

DOE OSTI2022

New tree-ring data from Canadian boreal and hemi-boreal forests provide insight for improving the climate sensitivity of terrestrial biosphere models

Understanding boreal/hemi-boreal forest growth sensitivity to seasonal variations in temperature and water availability provides important basis for projecting the potential impacts of climate change on the productivity of these ecosystems. Our best available information currently comes from a limited number of field experiments and terrestrial biosphere model (TBM) simulations of varying predictive accuracy. Here, we assessed the sensitivity of annual boreal/hemi-boreal forest growth in Canada to yearly fluctuations in seasonal climate variables using a large tree-ring dataset and compared this to the climate sensitivity of annual net primary productivity (NPP) estimates obtained from fourteen TBMs. We found that boreal/hemi-boreal forest growth sensitivity to fluctuations in seasonal temperature and precipitation variables changed along a southwestern to northeastern gradient, with growth limited almost entirely by temperature in the northeast and west and by water availability in the southwest. We also found a lag in growth climate sensitivity, with growth largely determined by the climate during the summer prior to ring formation. Analyses of NPP sensitivity to the same climate variables produced a similar southwest to northeast gradient in growth climate sensitivity for NPP estimates from all but three TBMs. However, analyses of growth from tree-ring data and analyses of NPP from TBMs produced contrasting evidence concerning the key climate variables limiting growth. While analyses of NPP primarily indicated a positive relationship between growth and seasonal temperature, tree-ring analyses indicated negative growth relationships to temperature. Also, the positive effect of precipitation on NPP derived from most TBMs was weaker than the positive effect of precipitation on tree-ring based growth: temperature had a more important limiting effect on NPP than tree-ring data indicated. These mismatches regarding the key climate variables limiting growth suggested that characterization of tree growth in TBMs might need revision, particularly regarding the effects of stomatal conductance and carbohydrate reserve dynamics.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2024

Climate Extremes and Risks: Links Between Climate Science and Decision-Making

The World Climate Research Programme (WCRP) envisions a future where actionable climate information is universally accessible, supporting decision makers in preparing for and responding to climate change. In this perspective, we advocate for enhancing links between climate science and decision-making through a better and more decision-relevant understanding of climate impacts. The proposed framework comprises three pillars: climate science, impact science, and decision-making, focusing on generating seamless climate information from sub-seasonal, seasonal, decadal to century timescales informed by observed climate events and their impacts. The link between climate science and decision-making has strengthened in recent years, partly owing to undeniable impacts arising from disastrous weather extremes. Enhancing decision-relevant understanding involves utilizing lessons from past extreme events and implementing impact-based early warning systems to improve resilience. Integrated risk assessment and management require a comprehensive approach that encompasses good knowledge about possible impacts, hazard identification, monitoring, and communication of risks while acknowledging uncertainties inherent in climate predictions and projections, but not letting the uncertainty lead to decision paralysis. The importance of data accessibility, especially in the Global South, underscores the need for better coordination and resource allocation. Strategic frameworks should aim to enhance impact-related and open-access climate services around the world. Continuous improvements in predictive modeling and observational data are critical, as is ensuring that climate science remains relevant to decision makers locally and globally. Ultimately, fostering stronger collaborations and dedicated investments to process and tailor climate data will enhance societal preparedness, enabling communities to navigate the complexities of a changing climate effectively.

climate extremes↗

NASA NTRS2006

Antarctic Circumpolar Current Transport Variability during 2003-05 from GRACE

Gravity Recovery and Climate Experiment (GRACE) gravity data spanning January 2003 - November 2005 are used as proxies for ocean bottom pressure (BP) averaged over 1 month, spherical Gaussian caps 500 km in radius, and along paths bracketing the Antarctic Circumpolar Current's various fronts. The GRACE BP signals are compared with those derived from the Estimating the Circulation and Climate of the Ocean (ECCO) ocean modeling-assimilation system, and to a non-Boussinesq version of the Regional Ocean Model System (ROMS). The discrepancy found between GRACE and the models is 1.7 cm(sub H2O) (1 cm(sub H2O) similar to 1 hPa), slightly lower than the 1.9 cm(sub H2O) estimated by the authors independently from propagation of GRACE errors. The northern signals are weak and uncorrelated among basins. The southern signals are strong, with a common seasonality. The seasonal cycle GRACE data observed in the Pacific and Indian Ocean sectors of the ACC are consistent, with annual and semiannual amplitudes of 3.6 and 0.6 cm(sub H2O) (1.1 and 0.6 cm(sub H2O) with ECCO), the average over the full southern path peaks (stronger ACC) in the southern winter, on days of year 197 and 97 for the annual and semiannual components, respectively; the Atlantic Ocean annual peak is 20 days earlier. An approximate conversion factor of 3.1 Sv ( Sv equivalent to 10(exp 6) m(exp 3) s(exp -1)) of barotropic transport variability per cm(sub H2O) of BP change is estimated. Wind stress data time series from the Quick Scatterometer (QuikSCAT), averaged monthly, zonally, and over the latitude band 40 de - 65 deg S, are also constructed and subsampled at the same months as with the GRACE data. The annual and semiannual harmonics of the wind stress peak on days 198 and 82, respectively. A decreasing trend over the 3 yr is observed in the three data types.

oceanographic regions↗

DOE OSTI2016

Potential Energy Cost Savings from Increased Commercial Energy Code Compliance

An important question for commercial energy code compliance is: “How much energy cost savings can better compliance achieve?” This question is in sharp contrast to prior efforts that used a checklist of code requirements, each of which was graded pass or fail. Percent compliance for any given building was simply the percent of individual requirements that passed. A field investigation method is being developed that goes beyond the binary approach to determine how much energy cost savings is not realized. Prototype building simulations were used to estimate the energy cost impact of varying levels of non-compliance for newly constructed office buildings in climate zone 4C. Field data collected from actual buildings on specific conditions relative to code requirements was then applied to the simulation results to find the potential lost energy savings for a single building or for a sample of buildings. This new methodology was tested on nine office buildings in climate zone 4C. The amount of additional energy cost savings they could have achieved had they complied fully with the 2012 International Energy Conservation Code is determined. This paper will present the results of the test, lessons learned, describe follow-on research that is needed to verify that the methodology is both accurate and practical, and discuss the benefits that might accrue if the method were widely adopted.

code compliance↗

DOE OSTI2016

Potential Energy Cost Savings from Increased Commercial Energy Code Compliance

code compliance, commercial energy code, energy co↗

DOE OSTI2021

Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM)

Traditionally, watershed-scale hydrology is simulated by distributed hydrological models with offline meteorological forcing data, or by regional regional climate models that link atmospheric and land hydrology interactions. Global climate models (GCMs) are rarely used to study watershed-scale hydrology due to the relatively coarse grid resolution, computationally expensive downscaling, and simplified physical processes. Recently, however, watershed-scale hydrology analysis has become possible in GCMs due to the development of variable-resolution GCMs that dynamically couple the hydrological processes between atmosphere and land systems at fine resolutions in selected regions and coarse resolution elsewhere. In this study, we used the variable-resolution Community Earth System Model (VR-CESM) with refined-resolution (14 km) in the western U.S. and eastern China to evaluate watershed-scale hydrology. Furthermore, we compared the historical VR-CESM outputs with gauge measurements and other hydrological models (e.g., National Water Model in the U.S.) and calibrated the subsurface runoff capacities in four mountainous watersheds. An RCP8.5 projection from 2007 to 2050 is used to estimate the impact of changing precipitation and snow climatology on watershed hydrology. We also analyzed the long-term runoff variability and the possibility of extreme runoff events as simulated by the VR-CESM. Although calibration is not possible in larger-scale watersheds, VR-CESM simulates the long-term annual variability of watersheds and provides insights on climate change impacts on hydrology. We conclude that refined-resolution VR-CESM can be used for watershed-scale hydrology analysis to understand water resources and water balance, in addition to traditional watershed-scale hydrological models. It enables hydrological analysis at multiple watersheds in one simulation and can help understand the two-way dynamics between land surface hydrology and atmospheric processes, and is especially practical for projecting climate change impacts. However, it is challenging to apply VR-CESM for hydrologic analysis in regulated watersheds as human factors (e.g., pumping, irrigation, water diversion) have not been fully addressed in VR-CESM.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

FACE-IT. A Science Gateway for Food Security Research

Progress in sustainability science is hindered by challenges in creating and managing complex data acquisition, processing, simulation, post-processing, and intercomparison pipelines. To address these challenges, we developed the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT) for crop and climate impact assessments. This integrated data processing and simulation framework enables data ingest from geospatial archives; data regridding, aggregation, and other processing prior to simulation; large-scale climate impact simulations with agricultural and other models, leveraging high-performance and cloud computing; and post-processing to produce aggregated yields and ensemble variables needed for statistics, for model intercomparison, and to connect biophysical models to global and regional economic models. FACE-IT leverages the capabilities of the Globus Galaxies platform to enable the capture of workflows and outputs in well-defined, reusable, and comparable forms. We describe FACE-IT and applications within the Agricultural Model Intercomparison and Improvement Project and the Center for Robust Decision-making on Climate and Energy Policy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

AmeriFlux US-SCs Southern California Climate Gradient - Coastal Sage

This is the AmeriFlux version of the carbon flux data for the site US-SCs Southern California Climate Gradient - Coastal Sage. Site Description - Half hourly data are available at https://www.ess.uci.edu/~california/. This site is one of six Southern California Climate Gradient flux towers operated along an elevation gradient (sites are US-SCg, US-SCs, US-SCf, US-SCw, US-SCc, US-SCd). This site is a coastal sage shrubland. Coastal sage is a small stature, closed canopy vegetation dominated by drought deciduous shrubs. The site has historically burned every 10-20 years, with the wild fire in October 2007. The tower data sets includes this recovery process.

Goulden, Mike↗

DOE OSTI2014

Scanning ARM Cloud Radars. Part II: Data Quality Control and Processing

The Scanning ARM Cloud Radars (SACR’s) are the primary instruments for documenting the four-dimensional structure and evolution of clouds within a 20-30 km radius from the ARM fixed and mobile sites. Here, the post-processing of the calibrated SACR measurements is discussed. First, a feature mask algorithm that objectively determines the presence of significant radar returns is described. The feature mask algorithm is based on the statistical properties of radar receiver noise. It accounts for atmospheric emission and is applicable even for SACR profiles with few or no signal-free range gates. Using the nearest-in-time atmospheric sounding, the SACR radar reflectivities are corrected for gaseous attenuation (water vapor and oxygen) using a line-by-line absorption model. Despite having a high pulse repetition frequency, the SACR has a narrow Nyquist velocity limit and thus Doppler velocity folding is commonly observed. An unfolding algorithm that makes use of a first guess for the true Doppler velocity using horizontal wind measurements from the nearest sounding is described. The retrieval of the horizontal wind profile from the HS-RHI SACR scan observations and/or nearest sounding is described. The retrieved horizontal wind profile can be used to adaptively configure SACR scan strategies that depend on wind direction. Several remaining challenges are discussed, including the removal of insect and second-trip echoes. The described algorithms significantly enhance SACR data quality and constitute an important step towards the utilization of SACR measurements for cloud research.

climate↗

DOE OSTI2022

Median bed-material sediment particle size across rivers in the contiguous US

Abstract. Bed-material sediment particle size data, particularly the median sediment particle size (D50), are critical for understanding and modeling riverine sediment transport. However, sediment particle size observations are primarily available at individual sites. Large-scale modeling and assessment of riverine sediment transport are limited by the lack of continuous regional maps of bed-material sediment particle size. We hence present a map of D50 over the contiguous US in a vector format that corresponds to approximately 2.7 million river segments (i.e., flowlines) in the National Hydrography Dataset Plus (NHDPlus) dataset. We develop the map in four steps: (1) collect and process the observed D50 data from 2577 U.S. Geological Survey stations or U.S. Army Corps of Engineers sampling locations; (2) collocate these data with the NHDPlus flowlines based on their geographic locations, resulting in 1691 flowlines with collocated D50 values; (3) develop a predictive model using the eXtreme Gradient Boosting (XGBoost) machine learning method based on the observed D50 data and the corresponding climate, hydrology, geology, and other attributes retrieved from the NHDPlus dataset; and (4) estimate the D50 values for flowlines without observations using the XGBoost predictive model. We expect this map to be useful for various purposes, such as research in large-scale river sediment transport using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc. The map is available at https://doi.org/10.5281/zenodo.4921987 (Li et al., 2021a).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Evaluation of the Relative Contribution of Observing Systems in Reanalyses: Aircraft Temperature Bias and Analysis Innovations

Reanalyses have become important sources of data in weather and climate research. While observations are the most crucial component of the systems, few research projects consider carefully the multitudes of assimilated observations and their impact on the results. This is partly due to the diversity of observations and their individual complexity, but also due to the unfriendly nature of the data formats. Here, we discuss the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) and a companion dataset, the Gridded Innovations and Observations (GIO). GIO is simply a post-processing of the assimilated observations and their innovations (forecast error and analysis error) to a common spatio-temporal grid, following that of the MERRA analysis fields. This data includes in situ, retrieved and radiance observations that are assimilated and used in the reanalysis. While all these disparate observations and statistics are in a uniform easily accessible format, there are some limitations. Similar observations are binned to the grid, so that multiple observations are combined in the gridding process. The data is then implicitly thinned. Some details in the meta data may also be lost (e.g. aircraft or station ID). Nonetheless, the gridded observations should provide easy access to all the observations input to the reanalysis. To provide an example of the GIO data, a case study evaluating observing systems over the United States and statistics is presented, and demonstrates the evaluation of the observations and the data assimilation. The GIO data is used to collocate 200mb Radiosonde and Aircraft temperature measurements from 1979-2009. A known warm bias of the aircraft measurements is apparent compared to the radiosonde data. However, when larger quantities of aircraft data are available, they dominate the analysis and the radiosonde data become biased against the forecast. When AMSU radiances become available the radiosonde and aircraft analysis and forecast error take on an annual cycle. While this supports results of previous work that recommend bias corrections for the aircraft measurements, the interactions with AMSU radiances will also require further investigation. This also provides an example for reanalysis users in examining the available observations and their impact on the analysis. GIO data is presently available alongside the MERRA reanalysis.

Bosilovich, Michael G.↗

NASA NTRSDate not supplied

Applied Uses of MERRA Reanalyses: Current and Future Prospects

During the initial planning of the Modern Era Retrospective-analysis for Research and Applications (MERRA), the project was envisioned to become a tool for applied sciences and decision makers, as well as weather and climate research. MERRA was the first reanalysis to provide data at 1 hourly frequencies. After discussions with wind energy users, 50m above the surface winds were included in the data collection to begin to represent the environment at the turbine height. MERRA-2 was the first reanalysis to include interactive and assimilated aerosols. From these, PM 1, 2.5 and 10 records have been computed and contribute to the climate and health sector. The US Center for Disease Control is accessing MERRA-2 data for comparison along with their health data. MERRA-2 Extreme Indices have been computed from the high frequency data (e.g. precipitation maxima, heat waves and fire weather). These have been used to characterize the changing extremes in the United States and around the globe as well as the weather associated with the extremes. The next-generation, MERRA-21C, includes increased resolution (25km) and continues to provide innovative Earth system data. For example, a boundary layer collection will provide constant height level data starting at 100m, continuing up to 4000m. This will provide detailed information about the boundary layer processes that greatly affect the biosphere. Chemical tracers for constituents are included in the system. The ensemble data assimilation is run with 32 members, and the variance for critical variables are stored. We will explore the use of this variance as one measure of uncertainty relevant for applied uses, which has not been included in any MERRA reanalysis. We will discuss use case scenarios developed to engage with the energy and financial sectors, to better understand their needs for climate data. Prospects for further downscaling of reanalyses will also be explored.

Michael Bosilovich↗

DOE OSTI2016

Multi-timescale data assimilation for atmosphere–ocean state estimates

Paleoclimate proxy data span seasonal to millennial timescales, and Earth's climate system has both high- and low-frequency components. Yet it is currently unclear how best to incorporate multiple timescales of proxy data into a single reconstruction framework and to also capture both high- and low-frequency components of reconstructed variables. Here we present a data assimilation approach that can explicitly incorporate proxy data at arbitrary timescales. The principal advantage of using such an approach is that it allows much more proxy data to inform a climate reconstruction, though there can be additional benefits. Through a series of offline data-assimilation-based pseudoproxy experiments, we find that atmosphere–ocean states are most skillfully reconstructed by incorporating proxies across multiple timescales compared to using proxies at short (annual) or long (~ decadal) timescales alone. Additionally, reconstructions that incorporate long-timescale pseudoproxies improve the low-frequency components of the reconstructions relative to using only high-resolution pseudoproxies. We argue that this is because time averaging high-resolution observations improves their covariance relationship with the slowly varying components of the coupled-climate system, which the data assimilation algorithm can exploit. These results are consistent across the climate models considered, despite the model variables having very different spectral characteristics. Furthermore, our results also suggest that it may be possible to reconstruct features of the oceanic meridional overturning circulation based on atmospheric surface temperature proxies, though here we find such reconstructions lack spectral power over a broad range of frequencies.

54 ENVIRONMENTAL SCIENCES↗