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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Temperature and precipitation signal in two Alpine ice cores over the period 1961–2001

Water stable isotope ratios and net snow accumulation in ice cores are commonly interpreted as temperature or precipitation proxies. However, only in a few cases has a direct calibration with instrumental data been attempted. In this study we took advantage of the dense network of observations in the European Alpine region to rigorously test the relationship of the annual and seasonal resolved proxy data from two highly resolved ice cores with local temperature and precipitation. We focused on the time period 1961–2001 with the highest amount and quality of meteorological data and the minimal uncertainty in ice core dating (±1 year). The two ice cores were retrieved from the Fiescherhorn glacier (northern Alps, 3900 m a.s.l.), and Grenzgletscher (southern Alps, 4200 m a.s.l.). A parallel core from the Fiescherhorn glacier allowed assessing the reproducibility of the ice core proxy data. Due to the orographic barrier, the two flanks of the Alpine chain are affected by distinct patterns of precipitation. The different location of the two glaciers therefore offers a unique opportunity to test whether such a specific setting is reflected in the proxy data. On a seasonal scale a high fraction of δ 18 O variability was explained by the seasonal cycle of temperature (~60% for the ice cores, ~70% for the nearby stations of the Global Network of Isotopes in Precipitation – GNIP). When the seasonality is removed, the correlations decrease for all sites, indicating that factors other than temperature such as changing moisture sources and/or precipitation regimes affect the isotopic signal on this timescale. Post-depositional phenomena may additionally modify the ice core data. On an annual scale, the δ 18 O/temperature relationship was significant at the Fiescherhorn, whereas for Grenzgletscher this was the case only when weighting the temperature with precipitation. In both cases the fraction of interannual temperature variability explained was ~20%, comparable to the values obtained from the GNIP stations data. Consistently with previous studies, we found an altitude effect for the δ 18 O of -0.17‰/100 m for an extended elevation range combining data of the two ice core sites and four GNIP stations. Significant correlations between net accumulation and precipitation were observed for Grenzgletscher during the entire period of investigation, whereas for Fiescherhorn this was the case only for the less recent period (1961–1977). Local phenomena, probably related to wind, seem to partly disturb the Fiescherhorn accumulation record. Spatial correlation analysis shows the two glaciers to be influenced by different precipitation regimes, with the Grenzgletscher reflecting the characteristic precipitation regime south of the Alps and the Fiescherhorn accumulation showing a pattern more closely linked to northern Alpine stations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

How well do terrestrial biosphere models simulate coarse-scale runoff in the contiguous United States?

Significant changes in the water cycle are expected under current global environmental change. Robust assessment of present-day water cycle dynamics at continental to global scales is confounded by shortcomings in the observed record. Modeled assessments also yield conflicting results which are linked to differences in model structure and simulation protocol. Here we compare simulated gridded (1 spatial resolution) runoff from six terrestrial biosphere models (TBMs), seven reanalysis products, and one gridded surface station product in the contiguous United States (CONUS) from 2001 to 2005. We evaluate the consistency of these 14 estimates with stream gauge data, both as depleted flow and corrected for net withdrawals (2005 only), at the CONUS and water resource region scale, as well as examining similarity across TBMs and reanalysis products at the grid cell scale. Mean runoff across all simulated products and regions varies widely (range: 71 to 356 mm yr(-1)) relative to observed continental-scale runoff (209 or 280 mm yr(-1) when corrected for net withdrawals). Across all 14 products 8 exhibit Nash-Sutcliffe efficiency values in excess of 0.8 and three are within 10% of the observed value. Region-level mismatch exhibits a weak pattern of overestimation in western and underestimation in eastern regions although two products are systematically biased across all regions and largely scales with water use. Although gridded composite TBM and reanalysis runoff show some regional similarities, individual product values are highly variable. At the coarse scales used here we find that progress in better constraining simulated runoff requires standardized forcing data and the explicit incorporation of human effects (e.g., water withdrawals by source, fire, and land use change). (C) 2015 Elsevier B.V. All rights reserved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Accurate spatiotemporal predictions of daily stream temperature from statistical models accounting for interactions between climate and landscape

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity and productivity in stream ecosystems. The availability of spatially and temporally continuous estimates of stream temperature would improve the ability of biologists to fully explore the effects of stream temperature on biota. Most statistical stream temperature modeling techniques are limited in their ability to account for the influence of variables changing across spatial and temporal gradients. We identified and described important interactions between climate and spatial variables that approximate mechanistic controls on spatiotemporal patterns in stream temperature. With identified relationships we formed models to generate reach-scale basin-wide spatially and temporally continuous predictions of daily mean stream temperature in four Columbia River tributaries watersheds of the Pacific Northwest, USA. Models were validated with a testing dataset composed of completely distinct sites and measurements from different years. While some patterns in residuals remained, testing dataset predictions of selected models demonstrated high accuracy and precision (averaged RMSE for each watershed ranged from 0.85–1.54 °C) and was only 17% higher on average than training dataset prediction error. Aggregating daily predictions to monthly predictions of mean stream temperature reduced prediction error by an average of 23%. The accuracy of predictions was largely consistent across diverse climate years, demonstrating the ability of the models to capture the influences of interannual climatic variability and extend predictions to timeframes with limited temperature logger data. Results suggest that the inclusion of a range of interactions between spatial and climatic variables can approximate dynamic mechanistic controls on stream temperatures.

Siegel, Jared E.↗
DOE OSTI2017

Tropospheric Waveguide Teleconnections and Their Seasonality

Abstract One-point correlation maps of the subseasonal variability of 200-hPa meridional wind in nature and an atmospheric general circulation model are systematically analyzed to quantify the impact of the climatological-mean jets on tropospheric covariability as a result of the jets acting as waveguides for the propagation of Rossby waves. As anticipated by linear theory, signatures of jet influence are detected in terms of (i) the geographical position of the strongest teleconnections, (ii) the zonal orientation and extent of prominent patterns of variability, and (iii) the scale of the features that make up those patterns. Further evidence of jet waveguide influence comes from examining the seasonality of these teleconnection attributes. During winter, covariability can be essentially circumglobal, while during summer it tends to be confined within two separate sectors of the globe where the jets are especially strong. Experiments with a multilevel linear planetary wave model confirm that the analyzed characteristics of teleconnections in the waveguides can be attributed to the action of the mean state; no organization to the anomalous forcing of the atmosphere is required to produce these properties. Some attributes, however, depend on the presence of zonal variations in the climatological-mean state that are of similar scale to the teleconnection patterns themselves.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Influence of Vegetation on Simulation of the Water Balance and Hydrological Response to El Niño–Southern Oscillation in Western Tropical South America

Land surface models (LSMs) rely on vegetation parameters for use in hydrological and energy balance analysis, monitoring, and forecasting. This study examines the influence that vegetation representation in the Noah-Multiparameterization (Noah-MP) LSM has on hydrological simulations across the diverse climate zones of western tropical South America (WTSA), with specific consideration of hydrological variability associated with El Niño–Southern Oscillation (ENSO). The influence of model representation of vegetation on simulated hydrology is evaluated through three simulation experiments that use 1) satellite-derived constant MODIS; 2) satellite-derived time-varying MODIS; and 3) the Noah-MP dynamic leaf model. We find substantial differences in vegetation fields between these simulations, with the Noah-MP dynamic leaf model diverging significantly from satellite-derived vegetation fields in many ecoregions. Impacts on simulated hydrology were, however, found to be modest across climate zones, except for select extreme events. Also, although impacts on hydrology under ENSO-induced variability were small, we find that the Noah-MP dynamic leaf model simulates a positive relationship between rainfall and vegetation in humid ecoregions of WTSA, where satellite observations may indicate the opposite. The relatively small sensitivity of simulated hydrology to vegetation scheme suggests that the performance of hydrological monitoring and forecasting in WTSA that uses Noah-MP is largely unaffected by the choice of vegetation scheme, such that using a simple climatological default is generally no worse than adopting more complicated options. The presence of some differences between the time-varying and constant MODIS simulations for hydrologic extremes, however, indicates that time-varying MODIS configuration might be more suitable for hydrological hazards applications.

South America↗
NASA NTRS2021

Air Pollution Trends Measured From Terra: CO and AOD Over Industrial, Fire-prone, and Background Regions

Following past studies to quantify decadal trends in global carbon monoxide (CO) using satellite observations, we update estimates and find a CO trend in column amounts of about −0.50 % per year between 2002 to 2018, which is a deceleration compared to analyses performed on shorter records that found −1 % per year. Aerosols are co-emitted with CO from both fires and anthropogenic sources but with a shorter lifetime than CO. A combined trend analysis of CO and aerosol optical depth (AOD) measurements from space helps to diagnose the drivers of regional differences in the CO trend. We use the long-term records of CO from the Measurements of Pollution in the Troposphere (MOPITT) and AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Other satellite instruments measuring CO in the thermal infrared, AIRS, TES, IASI, and CrIS, show consistent hemispheric CO variability and corroborate results from the trend analysis performed with MOPITT CO. Trends are examined by hemisphere and in regions for 2002 to 2018, with uncertainties quantified. The CO and AOD records are split into two sub-periods (2002 to 2010 and 2010 to 2018) in order to assess trend changes over the 16 years. We focus on four major population centers: Northeast China, North India, Europe, and Eastern USA, as well as fire-prone regions in both hemispheres. In general, CO declines faster in the first half of the record compared to the second half, while AOD trends show more variability across regions. We find evidence of the atmospheric impact of air quality management policies. The large decline in CO found over Northeast China is initially associated with an improvement in combustion efficiency, with subsequent additional air quality improvements from 2010 onwards. Industrial regions with minimal emission control measures such as North India become more globally relevant as the global CO trend weakens. We also examine the CO trends in monthly percentile values to understand seasonal implications and find that local changes in biomass burning are sufficiently strong to counteract the global downward trend in atmospheric CO, particularly in late summer.

carbon monoxide↗
DOE OSTI2016

Projections of leaf area index in earth system models

The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key land–atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO 2 ), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in some parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Lastly, our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Causal Pathways for Temperature Predictability from Snow Depth

Dynamical subseasonal-to-seasonal (S2S) weather forecasting has made strides in recent years, thanks partly to better initialization and representation of physical variables in models. For instance, realistic initializations of snow and soil moisture in models yield enhanced temperature predictability on S2S time scales. Snow depth and soil moisture also mediate month-to-month persistence of near-surface air temperature. Here the role of snow depth as predictor of temperature one month ahead in the Northern Hemisphere is examined via two causal pathways. Through the first pathway, snow depth anomalies in month 1 persist into month 2 and are then linked to temperature anomalies through snow–temperature feedback mechanisms. The first pathway is active from fall to summer, and its effect peaks before the melting season: in winter in the low latitudes, in spring in the midlatitudes, and in early summer in the high latitudes. The second pathway, where snow depth anomalies in month 1 lead to soil moisture anomalies in month 2 (through melting), which are then linked to temperature anomalies in month 2 through soil moisture–temperature feedbacks, is most active in spring and summer. The effect of the second pathway peaks during the melting season, namely, later in the year than the first pathway. The latitudes of the highest mediated effect through both pathways follow a seasonal cycle, shifting northward along with the seasonal insolation cycle. Finally, in keeping with this seasonal cycle, the highest snow depth mediation occurs to the north and the highest soil moisture mediation to the south of the latitudes with the highest overall temperature predictability from snow depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Process-Oriented Diagnostics: Principles, Practice, Community Development, and Common Standards

Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.

58 GEOSCIENCES↗
DOE OSTI2016

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005

Here, despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr –1 , respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr –1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Influence of Tibetan Plateau sensible heat on pre-monsoon dust burdens over South Asia: Observational and modeling evidence

Tibetan Plateau sensible heating (TPSH) effect is recognized as a key driver of the South Asian climate; however its role in regulating the regional dust burdens over South Asia during the pre-monsoon season remains unclear. Using long-term reanalysis and/or observational dust and TPSH data, we show that the dust optical depth at 550 nm (DOD 550 ) over South Asia is significantly positively correlated with TPSH at the interannual scale. DOD 550 over South Asia increases (decreases) by 20 % (12 %) in the strongest (weakest) TPSH years. The phenomenon of TPSH-dust relationships is reinforced by sensitivity experiments by altering TPSH from global climate models. Further mechanism analysis reveals that TPSH perturbation induces regional circulation anomalies over South Asia, which features a low-tropospheric cyclonic response around the Tibetan Plateau. This cyclonic anomaly strengthens the prevailing northwesterly winds, increasing regional dust emissions and transportation over South Asia. This novel mechanism helps understand the South Asian dust change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Evolution and forcing mechanisms of El Niño over the past 21,000 years

The El Niño Southern Oscillation (ENSO) is Earth’s dominant source of interannual climate variability, but its response to global warming remains highly uncertain1. To improve our understanding of ENSO’s sensitivity to external climate forcing, it is paramount to determine its past behaviour by using palaeoclimate data and model simulations. Palaeoclimate records show that ENSO has varied considerably since the Last Glacial Maximum (21,000 years ago), and some data sets predict a gradual intensification of ENSO over the past ~6,000 years. Other attempts to simulate the transient evolution of ENSO have relied on simplified models or snapshot experiments. In this work, we assess a series of transient Coupled General Circulation Model simulations forced by changes in greenhouse gasses, orbital forcing, the meltwater discharge and the ice-sheet history throughout the past 21,000 years. Consistent with most palaeo-ENSO reconstructions, our model simulates an orbitally induced strengthening of ENSO during the Holocene epoch, which is caused by increasing positive ocean–atmosphere feedbacks. During the early deglaciation, ENSO characteristics change drastically in response to meltwater discharges and the resulting changes in the Atlantic Meridional Overturning Circulation and equatorial annual cycle. Increasing deglacial atmospheric CO 2 concentrations tend to weaken ENSO, whereas retreating glacial ice sheets intensify ENSO. The complex evolution of forcings and ENSO feedbacks and the uncertainties in the reconstruction further highlight the challenge and opportunity for constraining future ENSO responses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Global Disease Outbreaks Associated with the 2015–2016 El Niño Event

Interannual climate variability patterns associated with the El Niño-Southern Oscillation phenomenon result in climate and environmental anomaly conditions in specific regions worldwide that directly favor outbreaks and/or amplification of variety of diseases of public health concern including chikungunya, hantavirus, Rift Valley fever, cholera, plague, and Zika. We analyzed patterns of some disease outbreaks during the strong 2015–2016 El Niño event in relation to climate anomalies derived from satellite measurements. Disease outbreaks in multiple El Niño-connected regions worldwide (including Southeast Asia, Tanzania, western US, and Brazil) followed shifts in rainfall, temperature, and vegetation in which both drought and fooding occurred in excess (14–81% precipitation departures from normal). These shifts favored ecological conditions appropriate for pathogens and their vectors to emerge and propagate clusters of diseases activity in these regions. Our analysis indicates that intensity of disease activity in some ENSO-teleconnected regions were approximately 2.5–28% higher during years with El Niño events than those without. Plague in Colorado and New Mexico as well as cholera in Tanzania were significantly associated with above normal rainfall (p<0.05); while dengue in Brazil and southeast Asia were significantly associated with above normal land surface temperature (p<0.05). Routine and ongoing global satellite monitoring of key climate variable anomalies calibrated to specific regions could identify regions at risk for emergence and propagation of disease vectors. Such information can provide sufficient lead-time for outbreak prevention and potentially reduce the burden and spread of ecologically coupled diseases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.

54 ENVIRONMENTAL SCIENCES↗