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All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

A Decade of Salt Marsh Elevation Change in New York City’s Coastal Urban Parks

Coastal salt marshes of the eastern United States are particularly vulnerable to accelerated sea level rise, and urban marshes are at greater risk of erosion, inundation, and conversion to mudflat if left unmanaged. To guide New York City (NYC) salt marsh restoration strategies, NYC Parks collected up to 10 years of salt marsh elevation change data through 2020 at six salt marsh sites using the Surface Elevation Table-Marker Horizon (SET-MH) method, conducted a salt marsh trends analysis to determine shoreline change from 1974 to 2012, and conducted a salt marsh conditions assessment. We found that the citywide average surface elevation trend of 3.31 mm yr −1 was not significantly different from the 30-year (1990–2020) Relative Sea Level Rise of 4.23 mm yr −1 at The Battery, NY, tide station, probably due to high variability across and within sites. We also found that accretion rates differed across sites and watersheds, and sites situated lower in the tidal zone had higher accretion rates. Notably, Jamaica Bay’s Idlewild salt marsh, long suspected of being sediment-starved and ranking lowest in our conditions assessment, had the highest accretion rate at 9.5 mm yr −1 . Our salt marsh trends analysis also showed marsh loss at the shoreline edge, bare ground cover, and other indicators of marsh degradation. In mitigating marsh loss, the design grades for our recent wetland restoration projects enlarge the upper elevation ranges of the low- and high-marsh zones and incorporate wider and more gradual slopes in upland transition zones to enable inland marsh migration.

Salt marsh elevation change↗
DOE OSTI2024

Increasing phosphorus loss despite widespread concentration decline in US rivers

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.

Science & Technology - Other Topics↗
DOE OSTI2026

An evaluation of air quality in major urban areas of India

Rapid economic growth and burgeoning population have contributed to enhanced levels of PM 2.5 concentrations in urban regions of India. Evaluation of ambient air quality facilitates the assessment of effectiveness of emission control measures and early identification of new sources. This study provides a comprehensive statistical analysis of PM 2.5 concentrations in key urban areas across India, including Delhi, Kolkata, Mumbai, Chennai, Hyderabad, and several regional centers. Data from 2017 to 2023 was analyzed using trend analysis, cluster analysis, principal component analysis, and geostatistical interpolation to understand spatiotemporal variations and sources. The analysis reveals significant differences in spatial distribution of PM 2.5 concentrations with high annual averages in urban regions in Indo-Gangetic plain (82–123 μg m −3 ) and relatively lower concentrations (29–46 μg m −3 ) in southern urban areas of Kerala, Tamil Nadu and Andhra Pradesh. Delhi state had the highest 24-averaged PM 2.5 concentrations (112 μg m −3 ) followed by urban regions in Uttar Pradesh, Bihar and West Bengal (94 μg m −3 ). Trend analysis from 2017 to 2023 revealed an overall 2.5% decline in site-wide PM2.5 concentrations, with the exception of Ludhiana, which exhibited a consistent annual increase of 10%. Principal component analysis (PCA) attributes 30% of the variance to wintertime emissions, 13% to biomass burning, and 18% to the regional haze in the northern Indo-Gangetic Plain. Different analyses clearly demonstrates the contribution of biomass burning to pollution in Delhi and surrounding cities. Transboundary pollution to Kolkata is likely from the highly polluted region in Indo-Gangetic Plain. Coastal cities of Mumbai and Chennai has relatively lower pollution attributed to the influence of sea breeze dilution, with mostly local contribution and some potential transport from upwind industry clusters. Hyderabad also has local contribution due to high density of vehicular traffic and local small industries. This study shows that mitigation efforts targeting clusters of regions should be undertaken to curb the high PM2.5 pollution. Policy measures should be implemented both at local and the intra-state level to address shared sources and transport of pollution.

Hysplitbacktrajectories↗
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
DOE OSTI2016

DART 7.0 User Guide

The Desktop Analysis Reporting Tool (DART) is a software package that allows users to easily view and analyze daily files that span long periods. DART gives users the capability to quickly determine the state of health of a radiation portal monitor (RPM), troubleshoot and diagnose problems, and view data in various time frames to perform trend analysis. In short, it converts the data strings written in the daily files into meaningful tables and plots. The standalone version of DART (“soloDART”) utilizes a database engine that is included with the application; no additional installations are necessary. There is also a networked version of DART (“polyDART”) that is designed to maximize the benefit of a centralized data repository while distributing the workload to individual desktop machines. This networked approach requires a more complex database manager Structured Query Language (SQL) Server; however, SQL Server is not currently provided with DART. Regardless of which version is used, DART will import daily files from RPMs, store the relevant data in its database, and it can produce reports for status, trend analysis, and reporting purposes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2003

Weight and the Future of Space Flight Hardware Cost Modeling

Weight has been used as the primary input variable for cost estimating almost as long as there have been parametric cost models. While there are good reasons for using weight, serious limitations exist. These limitations have been addressed by multi-variable equations and trend analysis in models such as NAFCOM, PRICE, and SEER; however, these models have not be able to address the significant time lags that can occur between the development of similar space flight hardware systems. These time lags make the cost analyst's job difficult because insufficient data exists to perform trend analysis, and the current set of parametric models are not well suited to accommodating process improvements in space flight hardware design, development, build and test. As a result, people of good faith can have serious disagreement over the cost for new systems. To address these shortcomings, new cost modeling approaches are needed. The most promising approach is process based (sometimes called activity) costing. Developing process based models will require a detailed understanding of the functions required to produce space flight hardware combined with innovative approaches to estimating the necessary resources. Particularly challenging will be the lack of data at the process level. One method for developing a model is to combine notional algorithms with a discrete event simulation and model changes to the total cost as perturbations to the program are introduced. Despite these challenges, the potential benefits are such that efforts should be focused on developing process based cost models.

Prince, Frank A.↗
NASA NTRS2019

Trend Quality Ozone from NPP OMPS: The Version 2 Processing

A version 2 processing of data from two ozone monitoring instruments on Suomi NPP, the OMPS nadir ozone mapper and the OMPS nadir ozone profiler, has now been completed. The previously released data were useful for many purposes but were not suitable for use in ozone trend analysis. In this processing, instrument artifacts have been identified and corrected, an improved scattered light correction and wavelength registration have been applied, and soft calibration techniques were implemented to produce a calibration consistent with data from the series of SBUV/2 instruments. The result is a high-quality ozone time series suitable for trend analysis. Total column ozone data from the OMPS nadir mapper now agree with data from the SBUV/2 instrument on NOAA 19 with a zonal average bias of −0.2 % over the 60° S to 60° N latitude zone. Differences are somewhat larger between OMPS nadir profiler and N19 total column ozone, with an average difference of −1.1 % over the 60° S to 60° N latitude zone and a residual seasonal variation of about 2 % at latitudes higher than about 50°. For the profile retrieval, zonal average ozone in the upper stratosphere (between 2.5 and 4 hPa) agrees with that from NOAA 19 within ±3 % and an average bias of −1.1 %. In the lower stratosphere (between 25 and 40 hPa) agreement is within ±3 % with an average bias of +1.1 %. Tropospheric ozone produced by subtracting stratospheric ozone measured by the OMPS limb profiler from total column ozone measured by the nadir mapper is consistent with tropospheric ozone produced by subtracting stratospheric ozone from MLS from total ozone from the OMI instrument on Aura. The agreement of tropospheric ozone is within 10 % in most locations.

OMPS nadir ozone mapper↗
NASA NTRS2021

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better Than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R^(2) of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R^(2) results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

crop yield↗
DOE OSTI2018

Percolator: Scalable Pattern Discovery in Dynamic Graphs

We demonstrate Percolator, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of Percolator is to dynamically decide and verify a small fraction of patterns and their in- stances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a) the feasibility of incremental pattern mining by walking through each component of Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic graphs, and c) how the user-friendly GUI of Percolator inter- acts with users to support keyword-based queries that detect, browse and inspect trending patterns. We also demonstrate two user cases of Percolator, in social media trend analysis and academic collaboration analysis, respectively.

Choudhury, Sutanay↗
DOE OSTI2018

Analysis of trends in experimental observables: Reconstruction of the implosion dynamics and implications for fusion yield extrapolation for direct-drive cryogenic targets on OMEGA

This paper describes a technique for identifying trends in performance degradation for inertial con finement fusion implosion experiments. It is based on reconstruction of the implosion core with a combination of low- and mid-mode asymmetries. This technique was applied to an ensemble of hydro-equivalent deuterium-tritium implosions on OMEGA that achieved inferred hot-spot pressures ≈56 ± 7 Gbar [S. Regan et al., Phys. Rev. Lett. 117, 025001 (2016)]. All the experimental observables pertaining to the core could be reconstructed simultaneously with the same combination of low and mid modes. This suggests that in addition to low modes, that can cause a degradation of the stagnation pressure, mid modes are present that reduce the size of the neutron and x-ray producing volume. The systematic analysis shows that asymmetries can cause an overestimation of the total areal density in these implosions. Finally, it is also found that an improvement in implosion symmetry resulting from correction of either the systematic mid or low modes would result in an increase of the hot-spot pressure from 56 Gbar to ≈ 80 Gbar and could produce a burning plasma when the implosion core is extrapolated to an equivalent 1.9 MJ symmetric direct illumination [A. Bose et al., Phys. Rev. E 94, 011201(R) (2016)].

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Comparison of Vaisala radiosondes RS41 and RS92 at the ARM Southern Great Plains site

In the fall of 2013, the Vaisala RS41 (fourth generation) radiosonde was introduced as a replacement for the RS92-SGP radiosonde with improvements in measurement accuracy of profiles of atmospheric temperature, humidity, and pressure. In order to help characterize these improvements, an intercomparison campaign was undertaken at the US Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility site in north-central Oklahoma, USA. During 3–8 June 2014, a total of 20 twin-radiosonde flights were performed in a variety of atmospheric conditions representing typical midlatitude continental summertime conditions. The results show that for most of the observed conditions the RS92 and RS41 measurements agree much better than the manufacturer-specified combined uncertainties with notable exceptions when exiting liquid cloud layers where the “wet-bulbing” effect appears to be mitigated for several cases in the RS41 observations. The RS41 measurements of temperature and humidity, with applied correction algorithms, also appear to show less sensitivity to solar heating. In addition, these results suggest that the RS41 does provide important improvements, particularly in cloudy conditions. For many science applications – such as atmospheric process studies, retrieval development, and weather forecasting and climate modeling – the differences between the RS92 and RS41 measurements should have little impact. However, for long-term trend analysis and other climate applications, additional characterization of the RS41 measurements and their relation to the long-term observational records will be required.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2010

An analysis of trends in restructuring of electricity markets

The transition from traditional rate-of-return regulation to competition in generation in the electricity industry has been a bumpy one. While the outcome has been largely criticized, there is evidence that ratepayers in the majority of restructured states have in fact benefited, albeit less dramatically than was originally predicted, from their state's restructuring initiatives. Nonetheless, as regulated and restructured states move forward, there are lessons from the experience that should inform future restructuring efforts. (author)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Recent streamflow trends across permafrost basins of North America

Introduction Climate change impacts, including changing temperatures, precipitation, and vegetation, are widely anticipated to cause major shifts to the permafrost with resulting impacts to hydro-ecosystems across the high latitudes of the globe. However, it is challenging to examine streamflow shifts in these regions owing to a paucity of data, discontinuity of records, and other issues related to data consistency and accuracy. Methods Recent trends for long-term periods (1990–2021, 1976–2021) in observed minimum, mean, and maximum seasonal and annual streamflow were analyzed for a range of watersheds across North America affected by varying degrees of permafrost coverage. Results Streamflow trend analysis revealed that areas affected by permafrost are changing variably over the periods in terms of maximum, mean, and minimum seasonal and annual streamflow. These changes indicate a significant shift occurring in the most recent 46 years towards increasing mean streamflow for the dominant (> 50%) permafrost systems. Meanwhile, minimum streamflow increases for all permafrost-dominant systems and many of the other permafrost-affected systems across the seasons and annual periods considered, with the greatest number of significant changes in streamflow over other metrics. Maximum streamflow is shifting variably with significant increases in the permafrost-dominant systems in winter and fall over longer time periods of analysis. Our analysis suggests that streamflow trends are driven by climate (precipitation, followed by temperature), while variables such as permafrost coverage only appear important in the most recent 32-year period. Discussion The increases in streamflow trends observed in this study are reflective of deepening active layers and thawing permafrost, indicating that the entire hydrograph is undergoing change within permafrost-dominant streamflow systems as the Arctic moves towards a warmer future under climate change. Despite the many challenges to understanding changing streamflow in cold regions, there are new products and datasets in development that are increasingly allowing researchers to better understand the patterns of change in Arctic and subarctic systems affected by permafrost, offering a range of new tools, which, along with continued observational records, may help in improved understanding of changing Arctic streamflow patterns.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Total Ozone Trends from 1979 to 2016 Derived from Five Merged Observational Datasets - The Emergence into Ozone Recovery

We report on updated trends using different merged datasets from satellite and ground-based observations for the period from 1979 to 2016. Trends were determined by applying a multiple linear regression (MLR) to annual mean zonal mean data. Merged datasets used here include NASA MOD v8.6 and National Oceanic and Atmospheric Administration (NOAA) merge v8.6, both based on data from the series of Solar Backscatter UltraViolet (SBUV) and SBUV-2 satellite instruments (1978–present) as well as the Global Ozone Monitoring Experiment (GOME)-type Total Ozone (GTO) and GOME-SCIAMACHY-GOME-2 (GSG) merged datasets (1995-present), mainly comprising satellite data from GOME, the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and GOME-2A. The fifth dataset consists of the monthly mean zonal mean data from ground-based measurements collected at World Ozone and UV Data Center (WOUDC). The addition of four more years of data since the last World Meteorological Organization (WMO) ozone assessment (2013-2016) shows that for most datasets and regions the trends since the stratospheric halogen reached its maximum (approximately 1996 globally and approximately 2000 in polar regions) are mostly not significantly different from zero. However, for some latitudes, in particular the Southern Hemisphere extratropics and Northern Hemisphere subtropics, several datasets show small positive trends of slightly below +1 percent decade(exp. -1) that are barely statistically significant at the 2 Sigma uncertainty level. In the tropics, only two datasets show significant trends of +0.5 to +0.8 percent(exp.-1), while the others show near-zero trends. Positive trends since 2000 have been observed over Antarctica in September, but near-zero trends are found in October as well as in March over the Arctic. Uncertainties due to possible drifts between the datasets, from the merging procedure used to combine satellite datasets and related to the low sampling of ground-based data, are not accounted for in the trend analysis. Consequently, the retrieved trends can be only considered to be at the brink of becoming significant, but there are indications that we are about to emerge into the expected recovery phase. However, the recent trends are still considerably masked by the observed large year-to-year dynamical variability in total ozone.

column ozone trends↗
NASA NTRS2006

Real Time On-line Space Research Laboratory Environment Monitoring with Off-line Trend and Prediction Analysis

One of the responsibilities of the NASA Glenn Principal Investigator Microgravity Services is to support NASA sponsored investigators in the area of reduced-gravity acceleration data analysis, interpretation and the monitoring of the reduced-gravity environment on-board various carriers. With the International Space Station currently operational, a significant amount of acceleration data is being down-linked and processed on ground for both the space station onboard environment characterization (and verification) and scientific experiments. Therefore, to help principal investigator teams monitor the acceleration level on-board the International Space Station to avoid undesirable impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services developed an artificial intelligence monitoring system, which detects in near real time any change in the environment susceptible to affect onboard experiments. The main objective of the monitoring system is to help research teams identify the vibratory disturbances that are active at any instant of time onboard the International Space Station that might impact the environment in which their experiment is being conducted. The monitoring system allows any space research scientist, at any location and at any time, to see the current acceleration level on-board the Space Station via the World Wide Web. From the NASA Glenn s Exploration Systems Division web site, research scientists can see in near real time the active disturbances, such as pumps, fans, compressor, crew exercise, re-boost, extra-vehicular activity, etc., and decide whether or not to continue operating or stopping (or making note of such activity for later correlation with science results) their experiments based on the g-level associated with that specific event. A dynamic graphical display accessible via the World Wide Web shows the status of all the vibratory disturbance activities with their degree of confidence as well as their g-level contribution to the environment. The system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many Increments of the space station for selected disturbance activities. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential system failure as well as for use by research scientists during their science results analysis. Examples of both real time on-line vibratory disturbance detection and off-line trend analysis are presented in this paper. Several soft computing techniques such as Kohonen s Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗
NASA NTRS1988

Temporal trends and transport within and around the Antarctic polar vortex during the formation of the 1987 Antarctic ozone hole

During AAOE in 1987 an ER-2 high altitude aircraft made twelve flights out of Punta Arenas, Chile (53 S, 71 W) into the Antarctic polar vortex. The aircraft was fitted with fast response instruments for in situ measurements of many trace species including O3, ClO, BrO, NO sub y, NO, H2O, and N2O. Grab samples of long-lived tracers were also taken and a scanning microwave radiometer measured temperatures above and below the aircraft. Temperature, pressure, and wind measurements were also made on the flight tracks. Most of these flights were flown to 72 S, at a constant potential temperature, followed by a dip to a lower altitude and again assuming a sometimes different potential temperature for the return leg. The potential temperature chosen was 425 K (17 to 18 km) on 12 of the flight legs, and 5 of the flight legs were flown at 450 K (18 to 19 km). The remaining 7 legs of the 12 flights were not flown on constant potential temperature surfaces. Tracer data have been analyzed for temporal trends. Data from the ascents out of Punta Arenas, the constant potential temperature flight legs, and the dips within the vortex are used to compare tracer values inside and outside the vortex, both with respect to constant potential temperature and constant N2O. The time trend during the one-month period of August 23 through September 22, 1987, shows that ozone decreased by 50 percent or more at altitudes form 15 to 19 km. This trend is evident whether analyzed with respect to constant potential temperature or constant N2O. The trend analysis for ozone outside the vortex shows no downward trend during this period. The analysis for N2O at a constant potential temperature indicates no significant trend either inside or outside the vortex; however, a decrease in N2O with an increase in latitude is evident.

Proffitt, M. H.↗
DOE OSTI2016

Seasonal responses of terrestrial ecosystem water‐use efficiency to climate change

Abstract Ecosystem water‐use efficiency ( EWUE ) is an indicator of carbon–water interactions and is defined as the ratio of carbon assimilation ( GPP ) to evapotranspiration ( ET ). Previous research suggests an increasing long‐term trend in annual EWUE over many regions and is largely attributed to the physiological effects of rising CO 2 . The seasonal trends in EWUE , however, have not yet been analyzed. In this study, we investigate seasonal EWUE trends and responses to various drivers during 1982–2008. The seasonal cycle for two variants of EWUE , water‐use efficiency ( WUE , GPP / ET ), and transpiration‐based WUE ( WUE t , the ratio of GPP and transpiration), is analyzed from 0.5° gridded fields from four process‐based models and satellite‐based products, as well as a network of 63 local flux tower observations. WUE derived from flux tower observations shows moderate seasonal variation for most latitude bands, which is in agreement with satellite‐based products. In contrast, the seasonal EWUE trends are not well captured by the same satellite‐based products. Trend analysis, based on process‐model factorial simulations separating effects of climate, CO 2 , and nitrogen deposition ( NDEP ), further suggests that the seasonal EWUE trends are mainly associated with seasonal trends of climate, whereas CO 2 and NDEP do not show obvious seasonal difference in EWUE trends. About 66% grid cells show positive annual WUE trends, mainly over mid‐ and high northern latitudes. In these regions, spring climate change has amplified the effect of CO 2 in increasing WUE by more than 0.005 gC m −2 mm −1 yr −1 for 41% pixels. Multiple regression analysis further shows that the increase in springtime WUE in the northern hemisphere is the result of GPP increasing faster than ET because of the higher temperature sensitivity of GPP relative to ET . The partitioning of annual EWUE to seasonal components provides new insight into the relative sensitivities of GPP and ET to climate, CO 2, and NDEP .

Huang, Mengtian↗