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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Continental-scale niche differentiation of dominant topsoil archaea in drylands

Archaea represent a diverse group of microorganisms often associated with extreme environments. However, an integrated understanding of biogeographical patterns of the specialist Haloarchaea and the potential generalist ammonia-oxidizing archaea (AOA) across large-scale environmental gradients remains limited. Here, we hypothesize that niche differentiation determines their distinct distributions along environmental gradients. To test the hypothesis, we use a continental-scale research network including 173 dryland sites across northern China. Our results demonstrate that Haloarchaea and AOA dominate topsoil archaeal communities. As hypothesized, Haloarchaea and AOA show strong niche differentiation associated with two ecosystem types mainly found in China's drylands (i.e. deserts vs. grasslands), and they differ in the degree of habitat specialization. The relative abundance and richness of Haloarchaea are higher in deserts due to specialization to relatively high soil salinity and extreme climates, while those of AOA are greater in grassland soils. Our results further indicate a divergence in ecological processes underlying the segregated distributions of Haloarchaea and AOA. Haloarchaea are governed primarily by environmental-based processes while the more generalist AOA are assembled mostly via spatial-based processes. Our findings add to existing knowledge of large-scale biogeography of topsoil archaea, advancing our predictive understanding on changes in topsoil archaeal communities in a drier world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Understanding Air-Sea Feedbacks to the MJO through Process Evaluation of Observations and Models (Final Report)

The goal of our project was to enhance our understanding of the role of coupling in the simulation of the Madden Julian Oscillation (MJO) in coupled climate models. We developed diagnostics of precipitation and Sea Surface Temperature variability in climate models and investigated how they are related to metrics of MJO simulation performance. We found a relationship between models with a high spatial coherence of precipitation and a good propagation of the MJO through the Maritime Continent region, but no relationship between metrics of precipitation organisation and intensity. Analysis of coupled and uncoupled model simulations from the Sixth Coupled Model Intercomparison Project (CMIP6) shows that coupling systematically improved the MJO simulation in both metrics that we investigated. We analysed the role of background moisture gradients in the MJO simulation performance following Lim et al. (2018) and found that although the model to model variation in moisture gradients to the west of Indonesia was related to model MJO performance, in general no single moisture gradient was as good at explaining a large fraction of MJO performance across the models as compared to a linear combination of those metrics, which was able to explain a significant fraction of the variance in the MJO performance. Those relationships were much stronger for coupled simulations than uncoupled simulations and stronger for those metrics that measured propagation of the MJO across the Maritime Continent than for overall MJO activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Colorado East River Community Observatory Data Collection

Abstract The U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow‐dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long‐term and spatially‐extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross‐disciplinary process‐based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value‐added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Methane flux from transplanted soil monoliths depends on moisture, but not origin

Soils both produce and consume methane (CH 4 ), a potent greenhouse gas that contributes to climate change. In coastal forests, upland soils are shifting from being CH 4 sinks to sources as sea levels rise, increasingly flooding soils with little prior inundation history. Ecosystem CH 4 budgets are highly uncertain due in part to the difficulty in separating fluxes measured at the soil surface into individual production and consumption processes which are likely to have different responses to future environmental conditions. Here, we measured growing season CH 4 fluxes from soil monoliths transplanted four years prior along an inundation and salinity gradient to determine how changes in abiotic conditions control CH 4 flux rates. To parse net fluxes measured at the soil surface into their component gross rates, we paired field measurements with a stable isotope pool dilution incubation of surface soils. Throughout the growing season, net soil surface CH 4 flux was positively correlated with soil moisture (p < 0.01), with lowland-located soils tending towards CH 4 sources (mean 0.349 ± 1.11 mg CH 4 -C m -2 hr -1 , error is standard deviation) and upland-located soils tending towards CH 4 sinks (-0.003 ± 0.003 mg CH 4 -C m -2 hr -1 ). Transplanted soils’ fluxes were statistically identical to their native neighbors once microtopography-driven differences in soil moisture were controlled for. The pool dilution experiment revealed that production and consumption rates were similar in upland and lowland surface soils (2.82 ± 3.29 µmol CH 4 g -1 dry soil d -1 production, 3.47 ± 2.03 µmol CH 4 g -1 dry soil d -1 consumption), indicating the majority of production likely occurs at depth in lowland soils. Both gross and net fluxes from transplanted soils showed no effect of soil origin after four years, suggesting low resistance of CH 4 cycling to global change drivers. Our results indicate the strength of the coastal forest CH 4 sink is likely to decrease in proportion to sea-level rise.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Role of Maritime Continent Land Convection on the Mean State and MJO Propagation

The Maritime Continent (MC) region is known as a ‘barrier’ in the life-cycle of the Madden-Julian oscillation (MJO). During boreal winter, the MJO detours the equatorial MC land region southward and propagates through the oceanic region. Also, about half of the MJO events that initiate over the Indian Ocean cease around the MC. The mechanism through which the MC affects MJO propagation, however, has remained unanswered. The current study investigates the MJO-MC interaction with a particular focus on the role of MC land convection. Using a global climate model that simulates both mean climate and MJO realistically, we performed two sensitivity experiments in which updraft plume radius is set to its maximum and minimum value only in the MC land grid points, making convective top deeper and shallower, respectively. In this work, our results show that MC land convection plays a key role in shaping the 3-D climatological moisture distribution around the MC through its local and non-local effects. Shallower and weaker MC land convection results in a steepening of the vertical and meridional mean moisture gradient over the MC region. The opposite is the case when MC land convection becomes deeper and stronger. The MJO’s eastward propagation is enhanced (suppressed) with the steeper (lower) mean moisture gradient. The moist static energy (MSE) budget of the MJO reveals the vertical and meridional advection of the mean MSE by MJO wind anomalies as the key processes that are responsible for the changes in MJO propagation characteristics. Our results pinpoint the critical role of the background moisture gradient on MJO propagation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Subtropical Jet in Reanalysis Data from STJ_PV

Subtropical jet position from a new method for locating the subtropical jet, called the tropopause gradient method. It is based on the peak gradient in potential temperature along the dynamic tropopause. This data has the identified subtropical jet latitude, level, and intensity across four different reanalysis products (CFSR-2, ERA-Interim, JRA-55, and MERRA-2), at both daily and monthly output frequency.

58 GEOSCIENCES↗
NASA NTRS2011

Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by including a water fraction correction. Also note that current reliance on the MODIS day-night algorithm as a source of LST limits the coverage of the database in the Polar Regions. We will consider relaxing the current restriction as part of future development.

Moncet, Jean-Luc↗
NASA NTRS2022

Constraining Arctic Climate Projections of Wintertime Warming With Surface Turbulent Flux Observations and Representation of Surface-Atmosphere Coupling

The drivers of rapid Arctic climate change—record sea ice loss, warming SSTs, and a lengthening of the sea ice melt season—compel us to understand how this complex system operates and use this knowledge to enhance Arctic predictability. Changing energy flows sparked by sea ice decline, spotlight atmosphere-surface coupling processes as central to Arctic system function and its climate change response. Despite this, the representation of surface turbulent flux parameterizations in models has not kept pace with our understanding. The large uncertainty in Arctic climate change projections, the central role of atmosphere-surface coupling, and the large discrepancy in model representation of surface turbulent fluxes indicates that these processes may serve as useful observational constraints on projected Arctic climate change. This possibility requires an evaluation of surface turbulent fluxes and their sensitivity to controlling factors (surface-air temperature and moisture differences, sea ice, and winds) within contemporary climate models (here Coupled Model Intercomparison Project 6). The influence of individual controlling factors and their interactions is diagnosed using a multi-linear regression approach. This evaluation is done for four sea ice loss regimes, determined from observational sea ice loss trends, to control for the confounding effects of natural variability between models and observations. The comparisons between satellite- and model-derived surface turbulent fluxes illustrate that while models capture the general sensitivity of surface turbulent fluxes to declining sea ice and to surface-air gradients of temperature and moisture, substantial mean state biases exist. Specifically, the central Arctic is too weak of a heat sink to the winter atmosphere compared to observations, with implications to the simulated atmospheric circulation variability and thermodynamic profiles. Models were found to be about 50% more efficient at turning an air-sea temperature gradient anomaly into a sensible heat flux anomaly relative to observations. Further, the influence of sea ice concentration on the sensible heat flux is underestimated in models compared to observations. The opposite is found for the latent heat flux variability in models; where the latent heat flux is too sensitive to a sea ice concentration anomaly. Lastly, the results suggest that present-day trends in sea ice retreat regions may serve as suitable observational constraints of projected Arctic warming.

turbulent fluxes↗
DOE OSTI2022

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic

Biomass burning smoke is advected over the southeastern Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly in both magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2017) and September (2016). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ARM shortwave spectrometers to study the clear-cloud transition zone and mixing processes

The proposed research is a collaborative effort between NASA/Goddard Space Flight Center and the Hebrew University of Jerusalem. While the NASA team focused on analyzing ground-based hyper-spectral radiance observations to understand cloud edge properties and their connection to mixing processes, the Hebrew University team tackled the problem through cloud modeling activities. By approximating the shortwave spectra in the cloud-clear transition zone as a linear combination of purely clear and purely cloudy spectra we can characterize the variations of cloud optical thickness and cloud droplet effective radius in the transition zone. When applying this method to the measurements of a ground-based shortwave spectroradiometer at the ARM’s SGP site, representing continental conditions, and MAGIC field campaign between Log Angeles, California and Honolulu, Hawaii, representing maritime scenarios, we found that cloud optical depth consistently decreases in both cases, but droplet size decreases much more substantially for the continental regime, suggesting different mixing processes for the continental and maritime conditions. The investigation and measurements of radiation clouds were coupled with a unique cloud modeling. A novel spectral bin microphysics was developed and implemented to the System of Atmospheric Modeling (SAM). In order to resolve cloud transition zones with high spatial gradients of microphysical variables a unique high resolution (10 m) was used in simulations. The model calculates droplet size distributions in each grid point. The model output was transferred to the NASA/GSFC team for utilization in radiative calculations and testing of both radiative algorithm and model representation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Mangrove Canopy Height Globally Related to Precipitation, Temperature and Cyclone Frequency

Mangrove wetlands are among the most productive and carbon-dense ecosystems in the world. Their structural attributesvary considerably across spatial scales, yielding large uncertainties in regional and global estimates of carbon stocks. Here, wepresent a global analysis of mangrove canopy height gradients and aboveground carbon stocks based on remotely sensed measurementsand field data. Our study highlights that precipitation, temperature and cyclone frequency explain 74% of the globaltrends in maximum canopy height, with other geophysical factors influencing the observed variability at local and regionalscales. We find the tallest mangrove forests in Gabon, equatorial Africa, where stands attain 62.8 m. The total global mangrovecarbon stock (above- and belowground biomass, and soil) is estimated at 5.03 Pg, with a quarter of this value stored inIndonesia. Our analysis implies sensitivity of mangrove structure to climate change, and offers a baseline to monitor nationaland regional trends in mangrove carbon stocks.

Simard, Marc↗
NASA NTRS2021

Distinct Surface Response to Black Carbon Aerosols

For the radiative impact of individual climate forcings, most previous studies focused on the global mean values at the top of the atmosphere (TOA), and less attention has been paid to surface processes, especially for black carbon (BC) aerosols. In this study, the surface radiative responses to five different forcing agents were analyzed by using idealized model simulations. Our analyses reveal that for greenhouse gases, solar irradiance, and scattering aerosols, the surface temperature changes are mainly dictated by the changes of surface radiative heating, but for BC, surface energy redistribution between different components plays a more crucial role. Globally, when a unit BC forcing is imposed at TOA, the net shortwave radiation at the surface decreases by −5.87±0.67 W m −2 (W m −2 ) −1 (averaged over global land without Antarctica), which is partially offset by increased downward longwave radiation (2.32±0.38 W m −2 (W m −2 ) −1 from the warmer atmosphere, causing a net decrease in the incoming downward surface radiation of −3.56±0.60 W m −2 (W m −2 ) −1 . Despite a reduction in the downward radiation energy, the surface air temperature still increases by 0.25±0.08 K because of less efficient energy dissipation, manifested by reduced surface sensible (−2.88±0.43 W m −2 (W m −2 ) −1 ) and latent heat flux (−1.54±0.27 W m −2 (W m −2 ) −1 ), as well as a decrease in Bowen ratio (−0.20±0.07 (W m −2 ) −1 ). Such reductions of turbulent fluxes can be largely explained by enhanced air stability (0.07±0.02 K (W m −2 ) −1 ), measured as the difference of the potential temperature between 925 hPa and surface, and reduced surface wind speed (−0.05±0.01 m s −1 (W m −2 ) −1 ). The enhanced stability is due to the faster atmospheric warming relative to the surface, whereas the reduced wind speed can be partially explained by enhanced stability and reduced Equator-to-pole atmospheric temperature gradient. These rapid adjustments under BC forcing occur in the lower atmosphere and propagate downward to influence the surface energy redistribution and thus surface temperature response, which is not observed under greenhouse gases or scattering aerosols. Our study provides new insights into the impact of absorbing aerosols on surface energy balance and surface temperature response.

radiative impact↗
NASA NTRS2013

Satellite Observations of Desert Dust-induced Himalayan Snow Darkening

The optically thick aerosol layer along the southern edge of the Himalaya has been subject of several recent investigations relating to its radiative impacts on the South Asian summer monsoon and regional climate forcing. Prior to the onset of summer monsoon, mineral dust from southwest Asian deserts is transported over the Himalayan foothills on an annual basis. Episodic dust plumes are also advected over the Himalaya, visible as dust-laden snow surface in satellite imagery, particularly in western Himalaya. We examined spectral surface reflectance retrieved from spaceborne MODIS observations that show characteristic reduction in the visible wavelengths (0.47 nm) over western Himalaya, associated with dust-induced solar absorption. Case studies as well as seasonal variations of reflectance indicate a significant gradient across the visible (0.47 nm) to near-infrared (0.86 nm) spectrum (VIS-NIR), during premonsoon period. Enhanced absorption at shorter visible wavelengths and the resulting VIS-NIR gradient is consistent with model calculations of snow reflectance with dust impurity. While the role of black carbon in snow cannot be ruled out, our satellite-based analysis suggests the observed spectral reflectance gradient dominated by dust-induced solar absorption during premonsoon season. From an observational viewpoint, this study underscores the importance of mineral dust deposition toward darkening of the western Himalayan snow cover, with potential implications to accelerated seasonal snowmelt and regional snow albedo feedbacks.

Remote Sensing↗
NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗
DOE OSTI2023

New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave

Emerging new-generation geostationary satellites have broadened the scope for studying the diurnal cycle of ecosystem functions. We exploit observations from the Geostationary Operational Environmental Satellite-R series to examine the effect of a severe U.S. heatwave in 2020 on the diurnal variations of ecosystem photosynthesis. We find divergent responses of photosynthesis to the heatwave across vegetation types and aridity gradients, with drylands exhibiting widespread midday and afternoon depression in photosynthesis. The diurnal centroid and peak time of dryland gross primary production (GPP) substantially shift toward earlier morning times, reflecting notable water and heat stress. Our geostationary satellite-based method outperforms traditional radiation-based upscaling methods from polar-orbiting satellite snapshots in estimating daily GPP and GPP loss during heatwaves. These findings underscore the potential of geostationary satellites for diurnal photosynthesis monitoring and highlight the necessity to consider the increased diurnal asymmetry in GPP under stress when evaluating carbon-climate interactions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Evaluation of water vapor distribution in general circulation models using satellite observations

This paper presents a comparison of the water vapor distribution obtained from two general circulation models, the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM), with satellite observations of total precipitable water (TPW) from Special Sensor Microwave/Imager (SSM/I) and upper tropospheric relative humidity (UTH) from GOES. Overall, both models are successful in capturing the primary features of the observed water vapor distribution and its seasonal variation. For the ECMWF model, however, a systematic moist bias in TPW is noted over well-known stratocumulus regions in the eastern subtropical oceans. Comparison with radiosonde profiles suggests that this problem is attributable to difficulties in modeling the shallowness of the boundary layer and large vertical water vapor gradients which characterize these regions. In comparison, the CCM is more successful in capturing the low values of TPW in the stratocumulus regions, although it tends to exhibit a dry bias over the eastern half of the subtropical oceans and a corresponding moist bias in the western half. The CCM also significantly overestimates the daily variability of the moisture fields in convective regions, suggesting a problem in simulating the temporal nature of moisture transport by deep convection. Comparison of the monthly mean UTH distribution indicates generally larger discrepancies than were noted for TPW owing to the greater influence of large-scale dynamical processes in determining the distribution of UTH. In particular, the ECMWF model exhibits a distinct dry bias along the Intertropical Convergence Zone (ITCZ) and a moist bias over the subtropical descending branches of the Hadley cell, suggesting an underprediction in the strength of the Hadley circulation. The CCM, on the other hand, demonstrates greater discrepancies in UTH than are observed for the ECMWF model, but none that are as clearly correlated with well-known features of the large-scale circulation.

Soden, Brian J.↗
DOE OSTI2022

Latitude, Elevation, and Mean Annual Temperature Predict Peat Organic Matter Chemistry at a Global Scale

Peatlands contain a significant fraction of global soil carbon, but how these reservoirs will respond to the changing climate is still relatively unknown. A global picture of the variations in peat organic matter chemistry will aid our ability to gauge peatland soil response to climate. Here, the goal of this research is to test the hypotheses that (a) peat carbohydrate content, an indicator of soil organic matter reactivity, will increase with latitude and decrease with mean annual temperatures, (b) while peat aromatic content, an indicator of recalcitrance, will vary inversely, and (c) elevation will have a similar effect to latitude. We used Fourier Transform Infrared Spectroscopy to examine variations in the organic matter functional groups of 1034 peat samples collected from 10 to 20, 30–40, and 60–70 cm depths at 165 individual sites across a latitudinal gradient of 79°N–65°S and from elevations of 0–4,773 m. Carbohydrate contents of high latitude peat were significantly greater than peat originating near the equator, while aromatic content showed the opposite trend. For peat from similar latitudes but different elevations, the carbohydrate content was greater and aromatic content was lower at higher elevations. Higher carbohydrate content at higher latitudes indicates a greater potential for mineralization, whereas the chemical composition of low latitude peat is consistent with their apparent relative stability in the face of warmer temperatures. The combination of low carbohydrates and high aromatics at warmer locations near the equator suggests the mineralization of high latitude peat until reaching recalcitrance under a new temperature regime.

54 ENVIRONMENTAL SCIENCES↗