Radiation Measurement Southern Great Plains Research Articles

A Physics-Based DNI Model Assessing All-Sky Circumsolar Radiation.

SummaryBy investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region. In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2–7.

Observational Study of the Thermodynamics and Morphological Characteristics of a Midlatitude Continental Cold Pool Event

Abstract Convectively generated cold pools are important to the Earth system as they exert strong controls on deep convective-storm initiation, intensity, and life cycle. Despite their importance, efforts to introduce such cold pool controls into weather and climate models lack guidance and/or physical constraints from cold pool observations. This work presents a detailed, purely observational analysis of a cold pool event that took place on 23–24 May 2011 in north-central Oklahoma. The characteristics of the cold pool, and the spatiotemporal evolution of the hydrometeors and dynamics in the proximity of the cold pool, are studied with high-resolution observations. The unprecedented dataset used in this work to study cold pool characteristics includes an enhanced network of surface weather stations, a high-temporal-frequency sounding array, and the NEXRAD and Atmospheric Radiation Measurement (ARM) Southern Great Plains radar networks. The potential use of NEXRAD surveillance scans to estimate height and propagation speed of the leading edge of the cold pool (LECP) is presented in this work. Manual identification and tracking of the LECP from NEXRAD imagery shows a spatial and temporal heterogeneity of the LECP properties. Surprisingly, over its detected life cycle, the LECP speed remains almost constant, even though the strength of the cold pool diminishes in time and its height varies. Radar analysis shows that pulses of graupel and hail within downdrafts in the convective system generating the cold pool appeared to be related to temporary increases in the LECP height.

A 17-year climatology of temperature inversions above clouds over the ARM SGP site: The roles of cloud radiative effects

Abstract Atmospheric temperature inversions, i.e., temperatures increasing with altitude, modulate both radiative and buoyancy fluxes in the atmosphere. A temperature inversion layer often occurs immediately above a cloud layer that cools radiatively and thereby strengthens the capping temperature inversion. This study aims to investigate the characteristics of temperature inversions above clouds and their relationships with cloud-top radiative cooling. Using a 17-year (January 2001 to December 2017) high-quality and continuous radiosonde dataset collected at the Atmospheric Radiation Measurement Southern Great Plains Central Facility site, key temperature inversion parameters, namely, the occurrence frequency (dp), depth (dz), temperature difference (dT), and gradient (dT/dz), are derived for single- and double-layer clouds (SLC and DLC, respectively). The occurrence frequency of temperature inversions above single-layer clouds decreases dramatically as cloud tops rise from low to high altitudes. When an overlying higher cloud layer is present, the inversion becomes less frequent, shallower, and weaker than without it. This may be because higher clouds weaken the cloud-top radiative cooling of the underneath clouds by enhancing downwelling infrared radiation. This is supported by radiative transfer simulations. There are distinctive seasonal cycles of cloud-top radiative cooling for high clouds that are primarily driven by variations in shortwave heating. Distinctive seasonal cycles of temperature inversions also occurred regardless of the cloud regime (SLC or DLC) and altitude (low or high clouds). They appear to be driven by the seasonal cycle of cloud coverage (i.e., a greater amount of clouds undergoes stronger area-mean radiative cooling) although the shortwave heating seasonal cycle also plays a role for high clouds. Cloud radiative cooling cannot explain the diurnal cycle of temperature inversions.

Validating the Water Vapor Variance Similarity Relationship in the Interfacial Layer Using Observations and Large‐Eddy Simulations

AbstractIn previous work, the similarity relationship for the water vapor variance in the interfacial layer (IL) at the top of the convective boundary layer (CBL) was proposed to be proportional to the convective velocity scale and the gradients of the water vapor mixing ratio and the Brunt‐Vaisala frequency in the entrainment zone. In the presence of wind shear in the IL, the similarity relationship was hypothesized to also include a dependence on the gradient Richardson number. Simultaneous measurements of the surface buoyancy flux, wind‐shear profiles from a radar wind profiler, water vapor mixing ratio and temperature measurements and their gradients from a Raman lidar provide a unique opportunity to thoroughly examine the function used in defining the variance and validate it. These observations were made over the Atmospheric Radiation Measurement Southern Great Plains site. We identified 19 cases from 2016 during which the CBL was quasi‐stationary and well mixed for at least 2 hr in the afternoon. Furthermore, we simulated the CBL using a large‐eddy simulation (LES) model for these cases and derived the water vapor variance and other profiles to test the similarity function. Utilizing this unique combination of observations and LES, we demonstrate that the water vapor variance in the IL has little‐to‐no dependence on wind shear. Furthermore, we demonstrate that the predicted variance using the original similarity function matches the observed and LES‐modeled variance very well, with linear correlations between the two variances of 0.82 and 0.95, respectively.

Analysis of Low-level Temperature Inversions and Their Effects on Aerosols in the Lower Atmosphere

High-quality and continuous radiosonde, aerosol and surface meteorology datasets are used to investigate the statistical characteristics of meteorological parameters and their effects on aerosols. The data were collected at the Atmospheric Radiation Measurement Southern Great Plains climate research facility during 2000–15. The parameters and vertical distribution of temperature inversion layers were found to have strong diurnal and seasonal changes. For surface-based temperature inversion (SBI), the mean frequency and depth of temperature inversion layers were 39.4% and 198 m, respectively. The temperature difference between the top and bottom of SBI was 4.8°C, and so the temperature gradient was 2.4°C (100 m)−1. The detailed vertical distributions of temperature inversion had been determined, and only the temperature inversion layers below 1000 m showed diurnal and seasonal variations. Mean surface aerosol number concentrations increased by 43.0%, 21.9% and 49.2% when SBIs were present at 0530, 1730 and 2330 LST, respectively. The effect of SBI on surface aerosol concentration was weakest in summer (18.1%) and strongest in winter (58.4%). During elevated temperature inversion events, there was no noticeable difference in surface aerosol number concentrations. Temperature differences and temperature gradients across SBIs correlated fairly well with aerosol number concentrations, especially for temperature gradients. The vertical distribution of aerosol optical properties with and without temperature inversions was different. Surface aerosol measurements were representative of the air within (below), but not above, SBIs and EIs. These results provide a basis for developing a boundary layer aerosol accumulation model and for improving radiative transfer models in the lower atmosphere.

Long-Term Retrievals of Cloud Type and Fair-Weather Shallow Cumulus Events at the ARM SGP Site

AbstractA long-term climatology of classified cloud types has been generated for 13 years (1997–2009) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for seven cloud categories: low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. The classification was based on the cloud macrophysical quantities of cloud top, cloud base, and physical thickness of cloud layers, as measured by active sensors such as the millimeter-wavelength cloud radar (MMCR) and micropulse lidar (MPL). Climate variability of cloud characteristics has been examined using the 13-yr cloud-type retrieval. Low clouds and cirrus showed distinct diurnal and seasonal cycles. Total cloud occurrence followed the variation of low clouds, with a diurnal peak in early afternoon and a seasonal maximum in late winter. Additionally, further work has been done to identify fair-weather shallow cumulus (FWSC) events for 9 years (2000–08). Periods containing FWSC, a subcategory of clouds classified as low clouds, were produced using cloud fraction information from a total-sky imager and ceilometer. The identified FWSC periods in our study show good agreement with manually identified FWSC, missing only 6 cases out of 70 possible events during the spring to summer seasons (May–August).

Regional Moisture Budget and Land‐Atmosphere Coupling Over the U.S. Southern Great Plains Inferred From the ARM Long‐Term Observations

AbstractTen‐year warm‐season (May–August) observations at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to examine the large‐scale atmospheric moisture budget and the strength of land‐atmosphere coupling. Atmospheric moisture budget components, computed from the ARM variational analysis data, are compared between different wet/dry scenarios and convection regimes. Consistent with previous studies, large‐scale moisture flux convergence dominates the SGP regional moisture budget on the daily time scale, but surface evaporation does play a more prominent role on late‐afternoon deep convection days. Moreover, the relationship between moisture flux convergence and precipitation increases significantly from dry year to wet years. The land‐atmosphere coupling strength is quantified for local convective events within the “Local L‐A Coupling Process Chain” (Santanello et al., 2018, https://doi.org/10.1175/BAMS‐D‐17‐0001.1). The impact of land surface on the evolution of planetary boundary layer is highly dependent on the vegetation leaf area index. A significantly larger surface sensible heat flux is found over the SGP forest region in midsummer, which is accompanied by a much higher planetary boundary layer development and a large increase in shallow cumulus cloud fraction. The significant relationship between afternoon precipitation and surface turbulent flux only exists in the northeast part of the ARM SGP site, which is mainly covered by grassland/pasture.

Atmospheric Instability Dominates the Long‐Term Variation of Cloud Vertical Overlap Over the Southern Great Plains Site

AbstractAccurate representation of cloud vertical overlap in climate models is particularly significant for predicting the total cloud fraction (TCF) and calculating radiative budget. It refers to the parameterization of overlap parameter—decorrelation length scale L—but the potential of dynamical factors in developing parameterization of L has still received far less attention. Using ground‐based radar observation over Atmospheric Radiation Measurement Southern Great Plains site, here long‐term seasonal‐averaged L is retrieved and shows a very high anticorrelation with TCF from different data sets, indicating that TCF is sensitive to the way of cloud overlap. Therefore, combined with meteorological reanalysis data set, a robust multiple regression model between L and dynamical factors is built and exhibits smaller TCF bias compared with previous parameterization of L. Contribution calculation further verifies that atmospheric instability contributes 70% of L variation, indicating that it dominates the long‐term variation of L over Southern Great Plains site. This finding implies that dynamical factors should be taken into account in the parameterization of L.

Overview of the HI-SCALE Field Campaign: A New Perspective on Shallow Convective Clouds

AbstractShallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land–atmosphere interactions and aerosol–radiation–cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.

Surface Solar Irradiance in Continental Shallow Cumulus Fields: Observations and Large-Eddy Simulation

Abstract This study examines shallow cumulus cloud fields and their surface shortwave radiative effects using large-eddy simulation (LES) along with observations across multiple days at the Atmospheric Radiation Measurement Southern Great Plains atmospheric observatory. Pronounced differences are found between probability density functions (PDFs) of downwelling surface solar irradiance derived from observations and LES one-dimensional (1D) online radiation calculations. The shape of the observed PDF is bimodal, which is only reproduced by offline three-dimensional (3D) radiative transfer calculations, demonstrating PDF bimodality as a 3D radiative signature of continental shallow cumuli. Local differences between 3D and 1D radiative transfer calculations of downwelling surface solar irradiance are, on average, larger than 150 W m−2 on one afternoon. The differences are substantially reduced when spatially averaged over the LES domain and temporally averaged over the diurnal cycle, but systematic 3D biases ranging from 2 to 8 W m−2 persist across different days. Covariations between the domain-averaged surface irradiance, framed as a surface cloud radiative effect, and the simulated cloud fraction are found to follow a consistent diurnal relationship, often exhibiting hysteresis. In contrast, observations show highly variable behavior. By subsampling the LES domain, it is shown that this is due to the limited sampling density of inherently 3D observations. These findings help to define observational requirements for detecting such relationships, provide valuable insight for evaluating weather and climate models against surface observations as they push to ever higher resolutions, and have important implications for future assessments of solar renewable energy potential.

Differences in Ice Cloud Optical Depth From CALIPSO and Ground‐Based Raman Lidar at the ARM SGP and TWP Sites

AbstractIce cloud column optical depths from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite and ground‐based Raman lidars (RLs) at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites are compared for 8 and 4 years, respectively. The cloudy mean ice cloud column optical depths from CALIPSO Version 4 data product with a horizontal resolution of 5 km are 78% (63%) larger than those from the RLs with a resolution of 10 min/30 m at SGP (TWP) for collocated transparent profiles. The main difference at SGP is caused by the lidar ratio that for CALIPSO is related to its treatment of the multiple scattering factor. The main difference at TWP is caused by the averaging resolution. Large differences in the optical depth distribution between the CALIPSO and RL are found for small optical depths at both sites, which are due to optically very thin clouds only detectable by the RLs. The differences in ice cloud column optical depth distributions and their mean values between the CALIPSO and RL can largely be reconciled over both sites after accounting for the averaging resolutions, lidar ratios along with the CALIPSO multiple scattering factor treatment, sensitivity to optically very thin ice clouds, and definition of transparent profiles. This work also examines in detail the lidar ratios that are directly observed by the RLs, which does not support the temperature‐dependent parameterizations of ice cloud lidar ratio and multiple scattering factor used in CALIPSO's Version 4 data product.

Investigation of Liquid Cloud Microphysical Properties of Deep Convective Systems: 2. Parameterization of Raindrop Size Distribution and its Application for Convective Rain Estimation

AbstractThe liquid cloud microphysical properties for stratiform rain (SR) have been investigated in Part 1 of this series. Since convective rain (CR) has characteristics in raindrop size distribution (DSD) and precipitation that are distinct from SR, we investigate the CR properties in this study by using 20 hr of CR samples collected by 17 Automatic Parsivel Units disdrometers during the Midlatitude Continental Convective Clouds Experiment over the Atmospheric Radiation Measurement Southern Great Plains site. A full spectrum of DSD is constructed based on a total of 23 size channels (0.321 to 9.785 mm), and both Gamma and Exponential fitted functions are applied to extract the DSD shape parameters. Compared to SR properties, CR has distinct features including broader size range and narrower exponential slope parameter (λE). These results indicate that the assumption of constant exponential intercept parameter (N0E) is inappropriate for CR rain rate estimates. Additionally, the subsetting scheme of the CR DSD spectra also has a strong impact on the Gamma/Exponential functions. Therefore, a new CR DSD parameterization scheme is developed by choosing the appropriate spectra subset with the constraint of rain rate and using the constant λE instead of constant N0E. With the input of the radar reflectivity at the lowest observed height, the newly calculated CR rain rates match well with the collocated surface rain gauge measurements (127 Mesonet stations and 17 Automatic Parsivel Units), while the rain rates calculated using traditional Z‐R relationship are 3–4 times larger, indicating that constant λE is a better assumption for CR DSD.

Using AIRS and ARM SGP Clear‐Sky Observations to Evaluate Meteorological Reanalyses: A Hyperspectral Radiance Closure Approach

AbstractUsing the ground‐based measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and spectral radiance from the Atmospheric Infrared Sounder (AIRS) on National Aeronautics and Space Administration Aqua, we evaluate the temperature and humidity profiles from European Center for Medium Range Weather Forecasting ERA‐Interim and Modern‐Era Retrospective analysis for Research and Applications Version 2 reanalyses. Four sets of synthetic AIRS spectra are calculated using 51 clear‐sky sounding profiles from the ARM SGP observations, the collocated AIRS L2 retrievals and the two reanalyses, respectively. A subset of AIRS channels sensitive to temperature, CO2, or H2O but not to other trace gases is chosen and further categorized into different groups according to the peak altitudes of their weighting functions. Synthetic radiances are then compared to the observed AIRS radiances for each group. For all groups, the observed AIRS radiances agree well with the synthetic ones based on the ARM SGP soundings or the AIRS L2 retrievals. The brightness temperature (BT) differences are within ±0.5 K. For two reanalyses, BT differences in all temperature‐sensitive groups are generally within ±0.5 K; but the mean BT differences in all groups sensitive to both T and H2O are negative. Together, they suggest a wet bias in the free troposphere in both reanalyses. Moreover, such BT differences can be seen in the analysis of AIRS clear‐sky radiances over the entire 30–40°N zone. A grid‐search retrieval suggests that 9–30% reduction for reanalysis humidity between 200 and 800 hPa is needed to correct such wet bias.

Comparison of Daytime Low‐Level Cloud Properties Derived From GOES and ARM SGP Measurements

AbstractLarge‐scale satellite data are critical for both verifying and improving general circulation model parameterizations of clouds and radiation for climate prediction. For reliable application of satellite data sets in cloud processes and climate models, it is important to have a reasonable estimate of the errors in the derived cloud properties. The daytime single‐layered low‐level cloud properties retrieved by the Geostationary Operational Environmental Satellite system (GOES) are compared with ground‐based observations and retrievals over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility from June 1998 to December 2006. The GOES retrievals are made via the Visible‐Infrared Solar‐infrared Split‐window Technique. They are spatially averaged within a 0.15° × 0.15° box centered on the ARM SGP site, and the ARM surface observations are temporally averaged ±15 min around the GOES scans to produce collocated pairs. Comparisons are made for monthly means, diurnal means, and one‐to‐one GOES and ARM collocated pairs. GOES Teff is highly correlated with ARM Ttop cloud temperature, having an R2 value of 0.75, though GOES exhibits a cold bias. GOES‐retrieved τ and liquid water path have very good agreement with ARM retrievals with R2s of 0.45 and 0.47, while re (GOES), on average, is about 2 μm greater than ARM re. An examination of solar and viewing geometry has shown that GOES‐retrieved mean re and τ values are impacted by solar zenith angle and especially scattering angle, which is not unexpected and needs to be accounted for by users.

Cloud Area Distributions of Shallow Cumuli: A New Method for Ground-Based Images

We develop a new approach that resolves cloud area distributions of single-layer shallow cumuli from ground-based observations. Our simple and computationally inexpensive approach uses images obtained from a Total Sky Imager (TSI) and complementary information on cloud base height provided by lidar measurements to estimate cloud equivalent diameter (CED) over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30 s). We illustrate the feasibility of our approach by comparing the estimated CEDs with those derived from collocated and coincident high-resolution (0.03 km) Landsat cloud masks with different spatial and temporal patterns of cloud cover collected over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. We demonstrate that (1) good (~7%) agreement between TSI and Landsat characteristic cloud size can be obtained for clouds that fall within the region of the sky observable by the TSI and (2) large clouds that extend beyond this region are responsible for noticeable (~16%) underestimation of the TSI characteristic cloud size. Our approach provides a previously unavailable dataset for process studies in the convective boundary layer and evaluation of shallow cumuli in cloud-resolving models.

Characteristics of Water Vapor Turbulence Profiles in Convective Boundary Layers During the Dry and Wet Seasons Over Darwin

AbstractThis study explores water vapor turbulence in the convective boundary layer (CBL) using the Raman lidar observations from the Atmospheric Radiation Measurement site located at Darwin, Australia. An autocovariance technique was used to separate out the random instrument error from the atmospheric variability during time periods when the CBL is cloud‐free, quasi‐stationary, and well mixed. We identified 45 cases, comprising of 8 wet and 37 dry seasons events, over the 5‐year data record period. The dry season in Darwin is known by warm and dry sunny days, while the wet season is characterized by high humidity and monsoonal rains. The inherent variability of the latter resulted in a more limited number of cases during the wet season. Profiles of the integral scale, variance, coefficient of the structure function, and skewness were analyzed and compared with similar observations from the Raman lidar at the Atmospheric Radiation Measurement Southern Great Plains (SGP) site. The wet season shows larger median variance profiles than the dry season, while the median profile of the variance from the dry season and the SGP site are found to be more comparable particularly between 0.4 and 0.75 zi. The variance and coefficient of the structure function show qualitatively the same vertical pattern. Furthermore, deeper CBL, larger gradient of water vapor mixing ratio at zi, and the strong correlation with the water vapor variance at zi are seen during the dry season. The median value in the skewness is mostly positive below 0.6 zi unlike the SGP site.

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

AbstractMany Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stations near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in‐depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud‐related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.

Erroneous Attribution of Deep Convective Invigoration to Aerosol Concentration

Abstract Contiguous time–height cloud objects at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site are matched with surface condensation nuclei (CN) concentrations and retrieved thermodynamic and kinematic vertical profiles for warm-cloud-base, cold-cloud-top systems in convectively unstable environments. Statistical analyses show that previously published conclusions that increasing CN concentrations cause a decrease in minimum cloud-top temperature (CTT) at the SGP site through the aerosol convective invigoration effect are unfounded. The CN–CTT relationship is statistically insignificant, while correlations between convective available potential energy (CAPE), level of neutral buoyancy (LNB), and CN concentration account for most of the change in the CN–CTT positive correlation. Removal of clouds with minimum CTTs > −36°C from the analysis eliminates the CN–CTT correlation. Composited dirty conditions at the SGP have ~1°C-warmer low levels and ~1°C-cooler upper levels than clean conditions. This correlation between aerosol concentrations and thermodynamic profiles may be caused by an increase in regional rainfall preceding deep convective conditions as CN concentration decreases. Increased rainfall can be expected to increase wet deposition of aerosols, cool low-level temperatures, and warm upper-level temperatures. The masking of a potential aerosol effect by such small thermodynamic changes implies that the strategy of analyzing subsets of aerosol data by binned meteorological factor values is not a valid method for discerning an aerosol effect in some situations. These findings highlight the need for more careful, detailed, and strategic observations to confidently isolate and quantify an aerosol deep convective invigoration effect.

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

AbstractMany weather forecast and climate models simulate warm surface air temperature (T2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T2m bias using a short‐term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions of surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.

Introduction to CAUSES: Description of Weather and Climate Models and Their Near-Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains.

We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from nine institutes, perform a series of 5 day hindcasts, each initialized from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale.