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At least 19 recordsLinked to original sources

Stable hydrogen and oxygen isotope ratios for selected sites of the National Oceanic and Atmospheric Administration's Atmospheric Integrated Research Monitoring Network (AIRMoN)

Increasingly, hydrologic studies require information on the isotopic composition of natural waters. This report presents stable hydrogen (δ 2 H) and oxygen isotope ratios (δ 18 0) of precipitation samples from seven selected sites of the National Oceanic and Atmospheric Administration's Atmospheric Integrated Research Monitoring Network (AIRMoN) collected during the years 1992-1994.

Delaware;Illinois;New York;Ohio;Pennsylvania;Tenne↗

A Community Hydrometeorology Laboratory for Fostering Collaborative Research by the Atmospheric and Hydrologic Sciences

A new community laboratory for fostering collaborative research between the atmospheric and hydrologie sciences communities is described. This facility, located at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, allows scientists from both communities to more easily focus resources and attention on interdisciplinary problems in atmospheric, hydrologic, and other related sciences. Researchers can remotely access the computing tools to use them or to download them to their own facility, or they can visit NCAR and use the laboratory with other scientists in joint research projects. An application of this facility is described, where scientists from NCAR, the University of Colorado, and the United States Geological Survey used quantitative precipitation estimates from weather radar to simulate a flash flood in the Buffalo Creek watershed in the mountainous Front Range near Denver, Colorado.

Bulletin of the American Meteorological Society↗

Testing the accuracy of a 1-D volcanic plume model in estimating mass eruption rate

During volcanic eruptions, empirical relationships are used to estimate mass eruption rate from plume height. Although simple, such relationships can be inaccurate and can underestimate rates in windy conditions. One-dimensional plume models can incorporate atmospheric conditions and give potentially more accurate estimates. Here I present a 1-D model for plumes in crosswind and simulate 25 historical eruptions where plume height H obs was well observed and mass eruption rate M obs could be calculated from mapped deposit mass and observed duration. The simulations considered wind, temperature, and phase changes of water. Atmospheric conditions were obtained from the National Center for Atmospheric Research Reanalysis 2.5° model. Simulations calculate the minimum, maximum, and average values ( M min , M max , and M avg ) that fit the plume height. Eruption rates were also estimated from the empirical formula M empir = 140 H obs 4.14 ( M empir is in kilogram per second, H obs is in kilometer). For these eruptions, the standard error of the residual in log space is about 0.53 for M avg and 0.50 for M empir . Thus, for this data set, the model is slightly less accurate at predicting M obs than the empirical curve. The inability of this model to improve eruption rate estimates may lie in the limited accuracy of even well-observed plume heights, inaccurate model formulation, or the fact that most eruptions examined were not highly influenced by wind. For the low, wind-blown plume of 14–18 April 2010 at Eyjafjallajökull, where an accurate plume height time series is available, modeled rates do agree better with M obs than M empir .

Journal of Geophysical Research D: Atmospheres↗

Change in atmospheric mineral aerosols in response to climate: Last glacial period, preindustrial, modern, and doubled carbon dioxide climates

Desert dust simulations generated by the National Center for Atmospheric Research's Community Climate System Model for the current climate are shown to be consistent with present day satellite and deposition data. The response of the dust cycle to last glacial maximum, preindustrial, modern, and doubled-carbon dioxide climates is analyzed. Only natural (non-land use related) dust sources are included in this simulation. Similar to some previous studies, dust production mainly responds to changes in the source areas from vegetation changes, not from winds or soil moisture changes alone. This model simulates a +92%, +33%, and -60% change in dust loading for the last glacial maximum, preindustrial, and doubled-carbon dioxide climate, respectively, when impacts of carbon dioxide fertilization on vegetation are included in the model. Terrestrial sediment records from the last glacial maximum compiled here indicate a large underestimate of deposition in continental regions, probably due to the lack of simulation of glaciogenic dust sources. In order to include the glaciogenic dust sources as a first approximation, we designate the location of these sources, and infer the size of the sources using an inversion method that best matches the available data. The inclusion of these inferred glaciogenic dust sources increases our dust flux in the last glacial maximum from 2.1 to 3.3 times current deposition. Copyright 2006 by the American Geophysical Union.

Journal of Geophysical Research D: Atmospheres↗

Surface temperature patterns in complex terrain: Daily variations and long-term change in the central Sierra Nevada, California

A realistic description of how temperatures vary with elevation is crucial for ecosystem studies and for models of basin-scale snowmelt and spring streamflow. This paper explores surface temperature variability using temperature data from an array of 37 sensors, called the Yosemite network, which traverses both slopes of the Sierra Nevada in the vicinity of Yosemite National Park, California. These data indicate that a simple lapse rate is often a poor description of the spatial temperature structure. Rather, the spatial pattern of temperature over the Yosemite network varies considerably with synoptic conditions. Empirical orthogonal functions (EOFs) were used to identify the dominant spatial temperature patterns and how they vary in time. Temporal variations of these surface temperature patterns were correlated with large-scale weather conditions, as described by National Centers for Environmental Prediction-National Center for Atmospheric Research Reanalysis data. Regression equations were used to downscale larger-scale weather parameters, such as Reanalysis winds and pressure, to the surface temperature structure over the Yosemite network. These relationships demonstrate that strong westerly winds are associated with relatively warmer temperatures on the east slope and cooler temperatures on the west slope of the Sierra, and weaker westerly winds are associated with the opposite pattern. Reanalysis data from 1948 to 2005 indicate weakening westerlies over this time period, a trend leading to relatively cooler temperatures on the east slope over decadal timescale's. This trend also appears in long-term observations and demonstrates the need to consider topographic effects when examining long-term changes in mountain regions. Copyright 2007 by the American Geophysical Union.

Journal of Geophysical Research D: Atmospheres↗

Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i

Gridded bioclimatic variables representing yearly, seasonal, and monthly means and extremes in temperature and precipitation have been widely used for ecological modeling purposes and in broader climate change impact and biogeographical studies. As a result of their utility, numerous sets of bioclimatic variables have been developed on a global scale (e.g., WorldClim) but rarely represent the finer regional scale pattern of climate in Hawai'i. Recognizing the value of having such regionally downscaled products, we integrated more detailed projections from recent climate models developed for Hawai'i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (T min , T mean , T max ) and mean precipitation (P mean ) data at 250 m resolution. We used the most up-to-date dynamically downscaled projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two climate projections into a suite of 19 standard bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions for the Hawaiian Islands. These bioclimatic variables are available for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under representative concentration pathway (RCP) 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections) climate scenarios. The resulting dataset provides a more robust set of climate products that can be used for modeling purposes, impact studies, and management planning.

Hawaii↗

Climate scenarios for California

Possible future climate changes in California are investigated from a varied set of climate change model simulations. These simulations, conducted by three state-of-the-art global climate models, provide trajectories from three greenhouse gas (GHG) emission scenarios. These scenarios and the resulting climate simulations are not “predictions,” but rather are a limited sample from among the many plausible pathways that may affect California’s climate. Future GHG concentrations are uncertain because they depend on future social, political, and technological pathways, and thus the IPCC has produced four “families” of emission scenarios. To explore some of these uncertainties, emissions scenarios A2 (a medium-high emissions) and B1 (low emissions) were selected from the current IPCC Fourth climate assessment, which provides several recent model simulations driven by A2 and B1 emissions. The global climate model simulations addressed here were from PCM1, the Parallel Climate Model from the National Center for Atmospheric Research (NCAR) and U.S. Department of Energy (DOE) group, and CM2.1 from the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluids Dynamics Laboratory (GFDL). As part of the scenarios assessment, a statistical technique using properties of historical weather data was employed to correct model biases and “downscale” the global-model simulation of future climates to a finer level of detail, onto a grid of approximately 7 miles (12 kilometers), which is more suitable for impact studies at the scales needed by California decision makers. In current climate-change simulations, temperatures over California warm significantly during the twenty-first century, with temperature increases from approximately +3ºF (1.5ºC) in the lower emissions scenario (B1) within the less responsive model (PCM1) to +8ºF (4.5ºC) in the higher emissions scenario (A2) within the more responsive model (CM2.1). Three of the simulations (all except the low-emission scenario run of the low-response model) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter, with virtually all derived from North Pacific winter storms. Relatively little change in overall precipitation is projected. Climate warming has a profound influence in diminishing snow accumulations, because there is more rain and less snow, and earlier snowmelt. These snow losses increase as the warming increases, so that they are most severe under climate changes projected by the more sensitive model with the higher GHG emissions.

California↗

CONUS404: The NCAR-USGS 4-km long-term regional hydroclimate reanalysis over the CONUS

A unique, high-resolution, hydroclimate reanalysis, 40-plus-year (October 1979–September 2021), 4 km (named as CONUS404), has been created using the Weather Research and Forecasting Model by dynamically downscaling of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous United States. The paper describes the approach for generating the dataset, provides an initial evaluation, including biases, and indicates how interested users can access the data. The motivation for creating this National Center for Atmospheric Research (NCAR)–U.S. Geological Survey (USGS) collaborative dataset is to provide research and end-user communities with a high-resolution, self-consistent, long-term, continental-scale hydroclimate dataset appropriate for forcing hydrological models and conducting hydroclimate scientific analyses over the conterminous United States. The data are archived and accessible on the USGS Black Pearl tape system and on the NCAR supercomputer Campaign storage system.

Continental United States↗

Long-term monotonic trends in water budget components in the contiguous United States: Insights from two hydrologic models

Characterizing changes to water availability for domestic, industrial, agricultural, and other uses is essential to support water management. To better quantify these changes, the U.S. Geological Survey and National Science Foundation National Center for Atmospheric Research produced two hydrologic models simulating water budget components from 1980 to 2021 over the contiguous United States (CONUS). Both hydrologic models were driven by a common atmospheric forcing dataset and aggregated to common spatial and temporal scales, which enables a novel evaluation of congruency between the models. We present annual and seasonal trends in six water budget components (precipitation, evapotranspiration, streamflow, groundwater recharge, soil saturation, and snow water equivalent) based on the Mann–Kendall test for monotonic trend and Theil-Sen slope estimate for the water year 1983–2021 period for ~86,000 catchments in CONUS. Additional components and metrics from our analysis pipeline are available in an associated published dataset, which contains more than 46 million trend results. The water budget trends showed broad agreement with prior observational and modeling studies that indicate increasing trends in the northeast and decreasing trends in southwestern CONUS. We found the seasonal variability in water budget trends was greatest in the southern, central, and northwest CONUS. These findings support integrated trend assessments when coupled with trends in water quality and use.

Contiguous United States↗

A multiscale and multidisciplinary investigation of ecosystem-atmosphere CO 2 exchange over the rocky mountains of colorado

A field study combined with modeling investigation demonstrated that the organization of CO2 transport by mountain terrain strongly affects the regional CO2 budget. Atmospheric dynamics can lead to complicated flows generated by inhomogeneous landscapes, topography or synoptic weather systems. The field campaign conducted of a ground deployment, the Carbon in the Mountain Experiment (CME04), and an aircraft deployment of the national Center for Atmospheric Research (NCAR) C-130, the Airborne Carbon in the Mountains Experiment (ACME04) over the period of spring to fall of 2004 to cover the seasonal variation of ecosystem-atmosphere carbon exchange. The role of the mountain circulation in CO2 transport can be played over seemingly flat terrain by mesoscale flows generated by various physical processes. The three dimensional observation strategy considered can also be applied over flat terrain.

Bulletin of the American Meteorological Society↗

A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States

Simulated daily precipitation, temperature, and runoff time series were compared in three mountainous basins in the United States: (1) the Animas River basin in Colorado, (2) the East Fork of the Carson River basin in Nevada and California, and (3) the Cle Elum River basin in Washington State. Two methods of climate scenario generation were compared: delta change and statistical downscaling. The delta change method uses differences between simulated current and future climate conditions from the Hadley Centre for Climate Prediction and Research (HadCM2) General Circulation Model (GCM) added to observed time series of climate variables. A statistical downscaling (SDS) model was developed for each basin using station data and output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis regridded to the scale of HadCM2. The SDS model was then used to simulate local climate variables using HadCM2 output for current and future conditions. Surface climate variables from each scenario were used in a precipitation-runoff model. Results from this study show that, in the basins tested, a precipitation-runoff model can simulate realistic runoff series for current conditions using statistically downscaled NCEP output. But, use of downscaled HadCM2 output for current or future climate assessments are questionable because the GCM does not produce accurate estimates of the surface variables needed for runoff in these regions. Given the uncertainties in the GCMs ability to simulate current conditions based on either the delta change or downscaling approaches, future climate assessments based on either of these approaches must be treated with caution.

California, Colorado, Nevada, Washington↗

Airborne desert dust and aeromicrobiology over the Turkish Mediterranean coastline

Between 18 March and 27 October 2002, 220 air samples were collected on 209 of 224 calendar days, on top of a coastal atmospheric research tower in Erdemli, Turkey. The volume of air filtered for each sample was 340 liters. Two hundred fifty-seven bacterial and 2598 fungal colony forming units (CFU) were enumerated from the samples using a low-nutrient agar. Ground-based dust measurements demonstrated that the region is routinely impacted by dust generated regionally and from North Africa and that the highest combined percent recovery of total CFU and African dust deposition occurred in the month of April (93.4% of CFU recovery and 91.1% of dust deposition occurred during African dust days versus no African dust present, for that month). A statistically significant correlation was observed (peak regional African dust months of March, April and May; r s =0.576, P=0.000) between an increase in the prevalence of microorganisms recovered from atmospheric samples on dust days (regional and African as determined by ground-based dust measurements), versus that observed on non-dust days. Given the prevalence of atmospherically suspended desert dust and microorganisms observed in this study, and that culture-based studies typically only recover a small fraction (<1.0%) of the actual microbial population in any given environment, dust-borne microorganisms and other associated constituents (organic detritus, toxins, etc.) may play a significant role in the regional human and ecosystem health.

Atmospheric Environment↗

Calibration and intercomparison of acetic acid measurements using proton transfer reaction mass spectrometry (PTR-MS)

Acetic acid is one of the most abundant organic acids in the ambient atmosphere, with maximum mixing ratios reaching into the tens of parts per billion by volume (ppbv) range. The identities and associated magnitudes of the major sources and sinks for acetic acid are poorly characterized, due in part to the limitation in available measurement techniques. This paper demonstrates that Proton Transfer Reaction Mass Spectrometry (PTR-MS) can reliably quantify acetic acid vapor in ambient air. Three different PTR-MS configurations were calibrated at low ppbv mixing ratios using permeation tubes, which yielded calibration factors between 7.0 and 10.9 normalized counts per second per ppbv (ncps ppbv −1 ) at a drift tube field strength of 132 townsend (Td). Detection limits ranged from 0.06 to 0.32 ppbv with dwell times of 5 s. These calibration factors showed negligible humidity dependence. Using the experimentally determined calibration factors, PTR-MS measurements of acetic acid during the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) campaign were validated against results obtained using Mist Chambers coupled with Ion Chromatography (MC/IC). An orthogonal least squares linear regression of paired data yielded a slope of 1.14 ± 0.06 (2σ), an intercept of 0.049 ± 20 (2σ) ppbv, and an R 2 of 0.78. The median mixing ratio of acetic acid on Appledore Island, ME during the ICARTT campaign was 0.530 ± 0.025 ppbv with a minimum of 0.075 ± 0.004 ppbv, and a maximum of 3.555 ± 0.171 ppbv.

Atmospheric Measurement Techniques↗

Climate change projections for lake whitefish ( Coregonus clupeaformis ) recruitment in the 1836 Treaty Waters of the Upper Great Lakes

Lake whitefish ( Coregonus clupeaformis ) is an ecologically, culturally, and economically important species in the Laurentian Great Lakes. Lake whitefish have been a staple food source for thousands of years and, since 1980, have supported the most economically valuable (annual catch value ≈ US$16.6 million) and productive (annual harvest ≈ 7 million kg) commercial fishery in the upper Great Lakes (Lakes Huron, Michigan, and Superior). Climate changes, specifically changes in temperature, wind, and ice cover, are expected to impact the ecology, production dynamics, and value of this fishery because the success of recruitment to the fishery has been linked with these climatic variables. We used linear regression to determine the relationship between fall and spring air temperature indices, fall wind speed, winter ice cover, and lake whitefish recruitment in 13 management units located in the 1836 Treaty Waters of the Upper Great Lakes ceded by the Ottawa and Chippewa nations, a culturally and commercially important region for the lake whitefish fishery. In eight of the 13 management units evaluated, models including one or more climate variables (temperature, wind, ice cover) explained significantly more variation in recruitment than models with only the stock–recruitment relationship, using corrected Akaike's Information Criterion comparisons (ΔAICc > 3). Isolating the climate–recruitment relationship and projecting recruitment with the Coupled Hydrosphere-Atmosphere Research Model (CHARM) indicated the potential for increased lake whitefish recruitment in the majority of the 1836 Treaty Waters management units. These results can inform adaptive management strategies by providing anticipated implications of climate on lake whitefish recruitment.

Lake Huron, Lake Michigan, Lake Superior↗

Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States

This paper examines the hydrologic model performance in three snowmelt-dominated basins in the western United States to dynamically- and statistically downscaled output from the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP). Runoff produced using a distributed hydrologic model is compared using daily precipitation and maximum and minimum temperature timeseries derived from the following sources: (1) NCEP output (horizontal grid spacing of approximately 210 km); (2) dynamically downscaled (DDS) NCEP output using a Regional Climate Model (RegCM2, horizontal grid spacing of approximately 52 km); (3) statistically downscaled (SDS) NCEP output; (4) spatially averaged measured data used to calibrate the hydrologic model (Best-Sta) and (5) spatially averaged measured data derived from stations located within the area of the RegCM2 model output used for each basin, but excluding Best-Sta set (All-Sta). In all three basins the SDS-based simulations of daily runoff were as good as runoff produced using the Best-Sta timeseries. The NCEP, DDS, and All-Sta timeseries were able to capture the gross aspects of the seasonal cycles of precipitation and temperature. However, in all three basins, the NCEP-, DDS-, and All-Sta-based simulations of runoff showed little skill on a daily basis. When the precipitation and temperature biases were corrected in the NCEP, DDS, and All-Sta timeseries, the accuracy of the daily runoff simulations improved dramatically, but, with the exception of the bias-corrected All-Sta data set, these simulations were never as accurate as the SDS-based simulations. This need for a bias correction may be somewhat troubling, but in the case of the large station-timeseries (All-Sta), the bias correction did indeed 'correct' for the change in scale. It is unknown if bias corrections to model output will be valid in a future climate. Future work is warranted to identify the causes for (and removal of) systematic biases in DDS simulations, and improve DDS simulations of daily variability in local climate. Until then, SDS based simulations of runoff appear to be the safer downscaling choice.

Journal of Hydrology↗

Vegetation greenness impacts on maximum and minimum temperatures in northeast Colorado

The impact of vegetation on the microclimate has not been adequately considered in the analysis of temperature forecasting and modelling. To fill part of this gap, the following study was undertaken. A daily 850–700 mb layer mean temperature, computed from the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, and satellite-derived greenness values, as defined by NDVI (Normalised Difference Vegetation Index), were correlated with surface maximum and minimum temperatures at six sites in northeast Colorado for the years 1989–98. The NDVI values, representing landscape greenness, act as a proxy for latent heat partitioning via transpiration. These sites encompass a wide array of environments, from irrigated-urban to short-grass prairie. The explained variance (r 2 value) of surface maximum and minimum temperature by only the 850–700 mb layer mean temperature was subtracted from the corresponding explained variance by the 850–700 mb layer mean temperature and NDVI values. The subtraction shows that by including NDVI values in the analysis, the r 2 values, and thus the degree of explanation of the surface temperatures, increase by a mean of 6% for the maxima and 8% for the minima over the period March–October. At most sites, there is a seasonal dependence in the explained variance of the maximum temperatures because of the seasonal cycle of plant growth and senescence. Between individual sites, the highest increase in explained variance occurred at the site with the least amount of anthropogenic influence. This work suggests the vegetation state needs to be included as a factor in surface temperature forecasting, numerical modeling, and climate change assessments.

Meteorological Applications↗

Hydrological responses to dynamically and statistically downscaled climate model output

Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically downscaled output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis. A distributed hydrological model was then applied to the downscaled data. Relative to raw NCEP output, downscaled climate variables provided more realistic stimulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of downscaling technique, and point to the need for caution when interpreting future hydrological scenarios.

Geophysical Research Letters↗

Integrating terrestrial and aquatic ecosystems to constrain estimates of land-atmosphere carbon exchange

In this Perspective, we put forward an integrative framework to improve estimates of land-atmosphere carbon exchange based on the accumulation of carbon in the landscape as constrained by its lateral export through rivers. The framework uses the watershed as the fundamental spatial unit and integrates all terrestrial and aquatic ecosystems as well as their hydrologic carbon exchanges. Application of the framework should help bridge the existing gap between land and atmosphere-based approaches and offers a platform to increase communication and synergy among the terrestrial, aquatic, and atmospheric research communities that is paramount to advance landscape carbon budget assessments.

Nature Communications↗