Radiation Measurement Climate Research Facility Research Articles

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

Marine boundary layer aerosol in the eastern North Atlantic: seasonal variations and key controlling processes

Abstract. The response of marine low cloud systems to changes in aerosol concentration represents one of the largest uncertainties in climate simulations. Major contributions to this uncertainty are derived from poor understanding of aerosol under natural conditions and the perturbation by anthropogenic emissions. The eastern North Atlantic (ENA) is a region of persistent but diverse marine boundary layer (MBL) clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In this study, we examine MBL aerosol properties, trace gas mixing ratios, and meteorological parameters measured at the Atmospheric Radiation Measurement Climate Research Facility's ENA site on Graciosa Island, Azores, Portugal, during a 3-year period from 2015 to 2017. Measurements impacted by local pollution on Graciosa Island and during occasional intense biomass burning and dust events are excluded from this study. Submicron aerosol size distribution typically consists of three modes: Aitken (At, diameter Dp<∼100 nm), accumulation (Ac, Dp within ∼100 to ∼300 nm), and larger accumulation (LA, Dp>∼300 nm) modes, with average number concentrations (denoted as NAt, NAc, and NLA below) of 330, 114, and 14 cm−3, respectively. NAt, NAc, and NLA show contrasting seasonal variations, suggesting different sources and removal processes. NLA is dominated by sea spray aerosol (SSA) and is higher in winter and lower in summer. This is due to the seasonal variations of SSA production, in-cloud coalescence scavenging, and dilution by entrained free troposphere (FT) air. In comparison, SSA typically contributes a relatively minor fraction to NAt (10 %) and NAc (21 %) on an annual basis. In addition to SSA, sources of Ac-mode particles include entrainment of FT aerosols and condensation growth of Aitken-mode particles inside the MBL, while in-cloud coalescence scavenging is the major sink of NAc. The observed seasonal variation of NAc, being higher in summer and lower in winter, generally agrees with the steady-state concentration estimated from major sources and sinks. NAt is mainly controlled by entrainment of FT aerosol, coagulation loss, and growth of Aitken-mode particles into the Ac-mode size range. Our calculation suggests that besides the direct contribution from entrained FT Ac-mode particles, growth of entrained FT Aitken-mode particles in the MBL also represent a substantial source of cloud condensation nuclei (CCN), with the highest contribution potentially reaching 60 % during summer. The growth of Aitken-mode particles to CCN size is an expected result of the condensation of sulfuric acid, a product from dimethyl sulfide oxidation, suggesting that ocean ecosystems may have a substantial influence on MBL CCN populations in the ENA.

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

Abstract. 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. 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.

The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language

The Python ARM Radar Toolkit is a package for reading, visualizing, correcting and analysing data from weather radars. Development began to meet the needs of the Atmospheric Radiation Measurement Climate Research Facility and has since expanded to provide a general-purpose framework for working with data from weather radars in the Python programming language. The toolkit is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib, and makes use of Cython for interfacing with existing radar libraries written in C and to speed up computationally demanding algorithms. The source code for the toolkit is available on GitHub and is distributed under a BSD license.

Fast All-Sky Radiation Model for Solar Applications (FARMS): A Brief Overview of Mechanisms, Performance, and Applications: Preprint

Solar radiation can be computed using radiative transfer models, such as the Rapid Radiation Transfer Model (RRTM) and its general circulation model applications, and used for various energy applications. Due to the complexity of computing radiation fields in aerosol and cloudy atmospheres, simulating solar radiation can be extremely time-consuming, but many approximations--e.g., the two-stream approach and the delta-M truncation scheme--can be utilized. To provide a new fast option for computing solar radiation, we developed the Fast All-sky Radiation Model for Solar applications (FARMS) by parameterizing the simulated diffuse horizontal irradiance and direct normal irradiance for cloudy conditions from the RRTM runs using a 16-stream discrete ordinates radiative transfer method. The solar irradiance at the surface was simulated by combining the cloud irradiance parameterizations with a fast clear-sky model, REST2. To understand the accuracy and efficiency of the newly developed fast model, we analyzed FARMS runs using cloud optical and microphysical properties retrieved using GOES data from 2009-2012. The global horizontal irradiance for cloudy conditions was simulated using FARMS and RRTM for global circulation modeling with a two-stream approximation and compared to measurements taken from the U.S. Department of Energy's Atmospheric Radiation Measurement Climate Research Facility Southern Great Plains site. Ourmore » results indicate that the accuracy of FARMS is comparable to or better than the two-stream approach; however, FARMS is approximately 400 times more efficient because it does not explicitly solve the radiative transfer equation for each individual cloud condition. Radiative transfer model runs are computationally expensive, but this model is promising for broad applications in solar resource assessment and forecasting. It is currently being used in the National Solar Radiation Database, which is publicly available from the National Renewable Energy Laboratory at http://nsrdb.nrel.gov.« less

RACORO continental boundary layer cloud investigations: 3. Separation of parameterization biases single‐column model CAM5 simulations of shallow cumulus

AbstractClimatically important low‐level clouds are commonly misrepresented in climate models. The FAst‐physics System TEstbed and Research (FASTER) Project has constructed case studies from the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plain site during the RACORO aircraft campaign to facilitate research on model representation of boundary‐layer clouds. This paper focuses on using the single‐column Community Atmosphere Model version 5 (SCAM5) simulations of a multi‐day continental shallow cumulus case to identify specific parameterization causes of low‐cloud biases. Consistent model biases among the simulations driven by a set of alternative forcings suggest that uncertainty in the forcing plays only a relatively minor role. In‐depth analysis reveals that the model's shallow cumulus convection scheme tends to significantly under‐produce clouds during the times when shallow cumuli exist in the observations, while the deep convective and stratiform cloud schemes significantly over‐produce low‐level clouds throughout the day. The links between model biases and the underlying assumptions of the shallow cumulus scheme are further diagnosed with the aid of large‐eddy simulations and aircraft measurements, and by suppressing the triggering of the deep convection scheme. It is found that the weak boundary layer turbulence simulated is directly responsible for the weak cumulus activity and the simulated boundary layer stratiform clouds. Increased vertical and temporal resolutions are shown to lead to stronger boundary layer turbulence and reduction of low‐cloud biases.

An A - train satellite based stratiform mixed - phase cloud retrieval algorithm by combining active and passive sensor measurements, British Journal of Environment and Climate Change, Vol

Aims: To develop a new satellite-based mixed-phase cloud retrieval algorithm for improving mixed-phase cloud liquid water path (LWP) retrievals by combining Moderate Resolution Imaging Spectroradiometer (MODIS), CloudSat, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements. Study Design: Algorithm development and evaluation by using collocated NASA A-Train and the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) measurements at the North Slope Alaska (NSA) site. Place and Duration of Study: Collocated MODIS and ground-based measurements at NSA site from March 2000 to October 2004, MODIS measurements and retrievals during July 2006 over Eastern Pacific, and MODIS, CloudSat and CALIPSO measurements on April 04, 2007 over the Arctic Region. Methodology: The stratiform mixed-phase cloudswere treated as two adjunct water and ice layers for radiative calculations with the Discrete Ordinate Radiative Transfer (DISORT) model. The ice-phase properties were provided with the 2C-ICE product, which is produced from CloudSat radar and CALIPSO lidar measurements, and they were used as inputs in DISORT for the calculations. Then, the calculated mixed-phase cloud reflectances at selected wavelengths were compared with MODIS reflectances to retrieve liquid-phase cloud properties. Results: A new algorithm was developed to retrieve LWP in stratiform mixed-phase Original Research Article British Journal of Environment & Climate Change, 3(4): 587-611, 2013 588 clouds by using MODIS radiances and ice cloud properties from active sensor measurements. The algorithm was validated separately by using Operational MODIS retrievals of warm marine stratiform clouds and collocated surface measurements of Arctic stratiform mixed-phase clouds. The results show that the new algorithm reduced the positive LWP biases in the Operational MODIS LWP retrievals for stratiform mixedphase clouds from 35 and 68% to 10 and 22% in the temperature ranges of -5 to -10oC and -10 to -20oC, respectively. Conclusion: The combined A-Train active and MODIS measurements can be used to improve global mixed-phase cloud property retrievals.

The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

Biases in regional carbon budgets from covariation of surface fluxes and weather in transport model inversions

Abstract. Recent advances in atmospheric transport model inversions could significantly reduce uncertainties in land carbon uptake through the assimilation of CO2 concentration measurements at weekly and shorter timescales. The potential of these measurements for reducing biases in estimated land carbon sinks depends on the strength of covariation between surface fluxes and atmospheric transport at these timescales and how well transport models represent this covariation. Daily to seasonal covariation of surface fluxes and atmospheric transport was estimated in observations at the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, and compared to an atmospheric transport model inversion (CarbonTracker). Covariation of transport and surface fluxes was stronger in CarbonTracker than in observations on synoptic (daily to weekly) timescales, with a wet year (2007) having significant covariation compared to a dry year (2006). Differences between observed and CarbonTracker synoptic covariation resulted in a 0.3 ppm CO2 enhancement in boundary layer concentrations during the growing season, and a corresponding enhancement in carbon uptake by 13% of the seasonal cycle amplitude in 2007, as estimated by an offline simplified transport model. This synoptic rectification of surface flux variability was of similar magnitude to the interannual variability in carbon sinks alone, and indicates that interannual variability in the inversions can be affected by biases in simulated synoptic rectifier effects. The most significant covariation of surface fluxes and transport had periodicities of 10 days and greater, suggesting that surface flux inversions would benefit from improved simulations of the effects of soil moisture on boundary layer heights and surface CO2 fluxes. Soil moisture remote sensing could be used along with CO2 concentration measurements to further constrain atmospheric transport model inversions.

Estimation of cloud condensation nuclei concentration from aerosol optical quantities: influential factors and uncertainties

Abstract. Large-scale measurements of cloud condensation nuclei (CCN) are difficult to obtain on a routine basis, whereas aerosol optical quantities are more readily available. This study investigates the relationship between CCN and aerosol optical quantities for some distinct aerosol types using extensive observational data collected at multiple Atmospheric Radiation Measurement (ARM) Climate Research Facility (CRF) sites around the world. The influences of relative humidity (RH), aerosol hygroscopicity (fRH) and single scattering albedo (SSA) on the relationship are analyzed. Better relationships are found between aerosol optical depth (AOD) and CCN at the Southern Great Plains (US), Ganges Valley (India) and Black Forest sites (Germany) than those at the Graciosa Island (the Azores) and Niamey (Niger) sites, where sea salt and dust aerosols dominate, respectively. In general, the correlation between AOD and CCN decreases as the wavelength of the AOD measurement increases, suggesting that AOD at a shorter wavelength is a better proxy for CCN. The correlation is significantly improved if aerosol index (AI) is used together with AOD. The highest correlation exists between CCN and aerosol scattering coefficients (σsp) and scattering AI measured in situ. The CCN–AOD (AI) relationship deteriorates with increasing RH. If RH exceeds 75%, the relationship where AOD is used as a proxy for CCN becomes invalid, whereas a tight σsp–CCN relationship exists for dry particles. Aerosol hygroscopicity has a weak impact on the σsp–CCN relationship. Particles with low SSA are generally associated with higher CCN concentrations, suggesting that SSA affects the relationship between CCN concentration and aerosol optical quantities. It may thus be used as a constraint to reduce uncertainties in the relationship. A significant increase in σsp and decrease in CCN with increasing SSA is observed, leading to a significant decrease in their ratio (CCN / σsp) with increasing SSA. Parameterized relationships are developed for estimating CCN, which account for RH, particle size, and SSA.

The Arm Climate Research Facility: A Review of Structure and Capabilities

The Atmospheric Radiation Measurement (ARM) Climate Research Facility (www.arm.gov) provides atmospheric observations from diverse climatic regimes around the world. Because it is a U.S. Department of Energy (DOE) user facility, ARM data are freely available to anyone through the ARM Data Archive. With 20 years of operations, the facility recently added two mobile facilities and an aerial facility to its network of fixed-location sites. Research using ARM data has led to advances in areas ranging from radiative transfer to cloud microphysics. The American Recovery and Reinvestment Act of 2009 allowed ARM to enhance its observational capabilities with a broad array of new instruments at its fixed and mobile sites and the aerial facility. Instruments include scanning radars; water vapor, cloud/aerosol extinction, and Doppler lidars; aerosol instruments for measuring optical, physical, and chemical properties; and aircraft probes for measuring cloud and aerosol properties. Taking full advantage of these inst...

Retrievals of cloud optical depth and effective radius from Thin-Cloud Rotating Shadowband Radiometer measurements

[1] A Thin-Cloud Rotating Shadowband Radiometer (TCRSR) was developed and deployed in a field test at the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plains site. The TCRSR measures the forward-scattering lobe of the direct solar beam (i.e., the solar aureole) through an optically thin cloud (optical depth < 8). We applied the retrieval algorithm of Min and Duan (2005) to the TCRSR measurements of the solar aureole to derive simultaneously the cloud optical depth (COD) and cloud drop effective radius (DER), subsequently inferring the cloud liquid-water path (LWP). After careful calibration and preprocessing, our results indicate that the TCRSR is able to retrieve simultaneously these three properties for optically thin water clouds. Colocated instruments, such as the MultiFilter Rotating Shadowband Radiometer (MFRSR), atmospheric emitted radiance interferometer (AERI), and Microwave Radiometer (MWR), are used to evaluate our retrieval results. The relative difference between retrieved CODs from the TCRSR and those from the MFRSR is less than 5%. The distribution of retrieved LWPs from the TCRSR is similar to those from the MWR and AERI. The differences between the TCRSR-based retrieved DERs and those from the AERI are apparent in some time periods, and the uncertainties of the DER retrievals are discussed in detail in this article.

Using boundary layer equilibrium to reduce uncertainties in transport models and CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux inversions

Abstract. This paper reexamines evidence for systematic errors in atmospheric transport models, in terms of the diagnostics used to infer vertical mixing rates from models and observations. Different diagnostics support different conclusions about transport model errors that could imply either stronger or weaker northern terrestrial carbon sinks. Conventional mixing diagnostics are compared to analyzed vertical mixing rates using data from the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, the CarbonTracker data assimilation system based on Transport Model version 5 (TM5), and atmospheric reanalyses. The results demonstrate that diagnostics based on boundary layer depth and vertical concentration gradients do not always indicate the vertical mixing strength. Vertical mixing rates are anti-correlated with boundary layer depth at some sites, diminishing in summer when the boundary layer is deepest. Boundary layer equilibrium concepts predict an inverse proportionality between CO2 vertical gradients and vertical mixing strength, such that previously reported discrepancies between observations and models most likely reflect overestimated as opposed to underestimated vertical mixing. However, errors in seasonal concentration gradients can also result from errors in modeled surface fluxes. This study proposes using the timescale for approach to boundary layer equilibrium to diagnose vertical mixing independently of seasonal surface fluxes, with applications to observations and model simulations of CO2 or other conserved boundary layer tracers with surface sources and sinks. Results indicate that frequently cited discrepancies between observations and inverse estimates do not provide sufficient proof of systematic errors in atmospheric transport models. Some previously hypothesized transport model biases, if found and corrected, could cause inverse estimates to further diverge from carbon inventory estimates of terrestrial sinks.

Sky cover from MFRSR observations

Abstract. The diffuse all-sky surface irradiances measured at two nearby wavelengths in the visible spectral range and their modeled clear-sky counterparts are the main components of a new method for estimating the fractional sky cover of different cloud types, including cumuli. The performance of this method is illustrated using 1-min resolution data from a ground-based Multi-Filter Rotating Shadowband Radiometer (MFRSR). The MFRSR data are collected at the US Department of Energy Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Southern Great Plains (SGP) site during the summer of 2007 and represent 13 days with cumuli. Good agreement is obtained between estimated values of the fractional sky cover and those provided by a well-established independent method based on broadband observations.

Comparison of Arctic clouds between European Center for Medium‐Range Weather Forecasts simulations and Atmospheric Radiation Measurement Climate Research Facility long‐term observations at the North Slope of Alaska Barrow site

This study evaluated the European Center for Medium‐Range Weather Forecasts (ECMWF) model‐simulated clouds and boundary layer (BL) properties based upon Atmospheric Radiation Measurement Climate Research Facility observations at the North Slope of Alaska site during 1999–2007. The ECMWF model‐simulated near‐surface humidity had seasonal dependent biases as large as 20%, while also experiencing difficulty representing BL temperature inversion height and strength during the transition seasons. Although the ECMWF model captured the seasonal variation of surface heat fluxes, it had sensible heat flux biases over 20 W m−2 in most of the cold months. Furthermore, even though the model captured the general seasonal variations of low‐level cloud fraction (LCF) and liquid water path (LWP), it still overestimated the LCF by 20% or more and underestimated the LWP over 50% in the cold season. On average, the ECMWF model underestimated LWP by ∼30 g m−2 but more accurately predicted ice water path for BL clouds. For BL mixed‐phase clouds, the model predicted water‐ice mass partition was significantly lower than the observations, largely due to the temperature dependence of water‐ice mass partition used in the model. The ECMWF model captured the general response of cloud fraction and LWP on large‐scale vertical motion changes but overpredicted the magnitude of the difference, especially for LWP. The new cloud and BL schemes of the ECMWF model that were implemented after 2003 only resulted in minor improvements in BL cloud simulations in summer. These results indicate that significant improvements in cold season BL and mixed‐phase cloud processes in the model are needed.

Mechanisms Affecting the Transition from Shallow to Deep Convection over Land: Inferences from Observations of the Diurnal Cycle Collected at the ARM Southern Great Plains Site

AbstractSummertime observations for 11 yr from the Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains (SGP) site are used to investigate mechanisms controlling the transition from shallow to deep convection over land. It is found that a more humid environment immediately above the boundary layer is present before the start of late afternoon heavy precipitation events. The higher moisture content is brought by wind from the south. Greater boundary layer inhomogeneity in moist static energy, temperature, moisture, and horizontal wind before precipitation begins is correlated to larger rain rates at the initial stage of precipitation. In an examination of afternoon rain statistics, higher relative humidity above the boundary layer is correlated to an earlier onset and longer duration of afternoon precipitation events, whereas greater boundary layer inhomogeneity and atmospheric instability in the 2–4-km layer above the surface are positively correlated to the total rain amount and the maximum rain rate. Although other interpretations may be possible, these observations are consistent with theories for the transition from shallow to deep convection that emphasize the role of a moist lower free troposphere and boundary layer inhomogeneity.

The Evaluation of CloudSat and CALIPSO Ice Microphysical Products Using Ground-Based Cloud Radar and Lidar Observations

Abstract In this paper, the statistical properties of tropical ice clouds (ice water content, visible extinction, effective radius, and total number concentration) derived from 3 yr of ground-based radar–lidar retrievals from the U.S. Department of Energy Atmospheric Radiation Measurement Climate Research Facility in Darwin, Australia, are compared with the same properties derived using the official CloudSat microphysical retrieval methods and from a simpler statistical method using radar reflectivity and air temperature. It is shown that the two official CloudSat microphysical products (2B-CWC-RO and 2B-CWC-RVOD) are statistically virtually identical. The comparison with the ground-based radar–lidar retrievals shows that all satellite methods produce ice water contents and extinctions in a much narrower range than the ground-based method and overestimate the mean vertical profiles of microphysical parameters below 10-km height by over a factor of 2. Better agreements are obtained above 10-km height. Ways to improve these estimates are suggested in this study. Effective radii retrievals from the standard CloudSat algorithms are characterized by a large positive bias of 8–12 μm. A sensitivity test shows that in response to such a bias the cloud longwave forcing is increased from 44.6 to 46.9 W m−2 (implying an error of about 5%), whereas the negative cloud shortwave forcing is increased from −81.6 to −82.8 W m−2. Further analysis reveals that these modest effects (although not insignificant) can be much larger for optically thick clouds. The statistical method using CloudSat reflectivities and air temperature was found to produce inaccurate mean vertical profiles and probability distribution functions of effective radius. This study also shows that the retrieval of the total number concentration needs to be improved in the official CloudSat microphysical methods prior to a quantitative use for the characterization of tropical ice clouds. Finally, the statistical relationship used to produce ice water content from extinction and air temperature obtained by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is evaluated for tropical ice clouds. It is suggested that the CALIPSO ice water content retrieval is robust for tropical ice clouds, but that the temperature dependence of the statistical relationship used should be slightly refined to better reproduce the radar–lidar retrievals.

Regional CO2 and latent heat surface fluxes in the Southern Great Plains: Measurements, modeling, and scaling

Characterizing net ecosystem exchanges (NEE) of CO2 and sensible and latent heat fluxes in heterogeneous landscapes is difficult, yet critical given expected changes in climate and land use. We report here a measurement and modeling study designed to improve our understanding of surface to atmosphere gas exchanges under very heterogeneous land cover in the mostly agricultural U.S. Southern Great Plains (SGP). We combined three years of site‐level, eddy covariance measurements in several of the dominant land cover types with regional‐scale climate data from the distributed Mesonet stations and Next Generation Weather Radar precipitation measurements to calibrate a land surface model of trace gas and energy exchanges (isotope‐enabled land surface model (ISOLSM)). Yearly variations in vegetation cover distributions were estimated from Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index and compared to regional and subregional vegetation cover type estimates from the U.S. Department of Agriculture census. We first applied ISOLSM at a 250 m spatial scale to account for vegetation cover type and leaf area variations that occur on hundred meter scales. Because of computational constraints, we developed a subsampling scheme within 10 km “macrocells” to perform these high‐resolution simulations. We estimate that the Atmospheric Radiation Measurement Climate Research Facility SGP region net CO2 exchange with the local atmosphere was −240, −340, and −270 gC m−2 yr−1 (positive toward the atmosphere) in 2003, 2004, and 2005, respectively, with large seasonal variations. We also performed simulations using two scaling approaches at resolutions of 10, 30, 60, and 90 km. The scaling approach applied in current land surface models led to regional NEE biases of up to 50 and 20% in weekly and annual estimates, respectively. An important factor in causing these biases was the complex leaf area index (LAI) distribution within cover types. Biases in predicted weekly average regional latent heat fluxes were smaller than for NEE, but larger than for either ecosystem respiration or assimilation alone. However, spatial and diurnal variations of hundreds of W m−2 in latent heat fluxes were common. We conclude that, in this heterogeneous system, characterizing vegetation cover type and LAI at the scale of spatial variation are necessary for accurate estimates of bottom‐up, regional NEE and surface energy fluxes.

Cloud classes and radiative heating profiles at the Manus and Nauru Atmospheric Radiation Measurement (ARM) sites

The tropical western Pacific is a convective regime; however, the frequency and depth of convection is dependent on dynamical forcing which exhibits variability on a range of temporal scales and also on location within the region. Manus Island, Papua New Guinea, lies in the heart of the western Pacific warm pool region and exhibits frequent deep convection much of the time, while Nauru, which lies approximately 20 degrees to the east of Manus, is in a transition zone where the frequency of convection is dependent on the phase of the El Niño−Southern Oscillation. Because of this difference in dynamical regime, the distribution of clouds and the associated radiative heating is quite different at the two sites. Individual cloud types (boundary layer cumulus, thin cirrus, stratiform convective outflow) do occur at both sites, but with different frequencies. In this study we compare cloud profiles and heating profiles for specific cloud types at these two sites using data from the Atmospheric Radiation Measurement (ARM) Climate Research Facility. Results of this comparison indicate that while the frequency of specific cloud types differ between the two sites as one would expect, the characteristics of individual cloud classes are remarkably similar. This information could prove to be very useful for applying tropical ARM data to the broader region.

Understanding ice supersaturation, particle growth, and number concentration in cirrus clouds

Many factors control the ice supersaturation and microphysical properties in cirrus clouds. We explore the effects of dynamic forcing, ice nucleation mechanisms, and ice crystal growth rate on the evolution and distribution of water vapor and cloud properties in nighttime cirrus clouds using a one‐dimensional cloud model with bin microphysics and remote sensing measurements obtained at the Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility located near Lamont, OK. We forced the model using both large‐scale vertical ascent and, for the first time, mean mesoscale velocity derived from radar Doppler velocity measurements. Both heterogeneous and homogeneous nucleation processes are explored, where a classical theory heterogeneous scheme is compared with empirical representations. We evaluated model simulations by examining both bulk cloud properties and distributions of measured radar reflectivity, lidar extinction, and water vapor profiles, as well as retrieved cloud microphysical properties. Our results suggest that mesoscale variability is the primary mechanism needed to reproduce observed quantities. Model sensitivity to the ice growth rate is also investigated. The most realistic simulations as compared with observations are forced using mesoscale waves, include fast ice crystal growth, and initiate ice by either homogeneous or heterogeneous nucleation. Simulated ice crystal number concentrations (tens to hundreds particles per liter) are typically two orders of magnitude smaller than previously published results based on aircraft measurements in cirrus clouds, although higher concentrations are possible in isolated pockets within the nucleation zone.