Abstract

Cloud characteristics at two sites on the North Slope of Alaska separated by ∼100 km have been examined for the warmer months of 2001–03 using data collected from microwave radiometers, ceilometers, rotating shadowband radiometers, and pyranometers. Clouds at the inland site, Atqasuk, were found to have approximately 26% greater optical depths than those at the coastal site, Barrow, and the ratio of measured irradiance to clear-sky irradiance was nearly 20% larger at Barrow under cloudy conditions. It is hypothesized that a significant factor contributing to these differences is the upward fluxes of heat and water vapor over the wet tundra and lakes. Support for this hypothesis is found from the behavior of the liquid water paths for low clouds, which tend to be higher at Atqasuk than at Barrow for onshore winds but not for offshore ones, from differences in sensible heat fluxes, which are small but significant over the tundra but are nearly zero over the ocean adjacent to Barrow, and from the mixing ratios, which are significantly higher at Atqasuk than at Barrow. Results from a simple model further indicate that latent heat fluxes over the tundra and lakes can account for a significant fraction of the differences in the estimated boundary layer water content between Barrow and Atqasuk.

Abstract

This paper describes a technique of using a mass-consistent model to derive wind speeds over a microscale region (about 4 km²) of complex terrain. A serious limitation of these numerical models is that the calculated wind field is highly sensitive to certain input parameters, such as that used to simulate the atmospheric stability. Because accurate values for these parameters are not usually known, confidence in the calculated winds is low.

However, values for these parameters can be found by tuning the model to existing wind observations within a microscale area. This tuning is accomplished with an optimization procedure that adjusts the unknown parameters so that the discrepancy between the observed winds and model calculations of these winds is minimized.

The model was verified with eight sets of hourly averaged wind data. These data were obtained from measurements made at 28 sites covering a windfarm development in the Altamont Pass area of California. When the model was tuned to a small subset of the 28 sites, the model showed skill in predicting wind speeds for the remaining sites in six of the eight cases. The two that did not perform as well were low wind cases.

Abstract

The output of pulsed and AC output anemometers suffer from discretization noise when such anemometers are sampled at fast rates (>1 Hz). This paper describes the construction of an optimal filter designed to reduce this noise. By comparing the filtered output from an AC output cup anemometer with a nearby cup anemometer whose output is free from discretization noise, it is shown that the filter significantly reduces the noise. Wind speed time series obtained from the two anemometers are quite similar. Next, deconvolution is applied to the filtered time series to account for the anemometer response. Spectra from the deconvolved time series and a time series measured by a nearby sonic anemometer are compared, and for high-speed flows the spectra from the two instruments match quite well. The time series are also very similar; however, the cup anemometer generally cannot respond to the quick bursts of speed seen by the sonic anemometer. The filtering and deconvolution methods presented here are most appropriate for the high-speed flows relevant to wind energy studies. These methods make it possible to use inexpensive, rugged cup anemometers to measure a high-speed, turbulent wind field up to a frequency of about 5 Hz.

Abstract

Future land cover will have a significant impact on climate and is strongly influenced by the extent of agricultural land use. Differing assumptions of crop yield increase and carbon pricing mitigation strategies affect projected expansion of agricultural land in future scenarios. In the representative concentration pathway 4.5 (RCP4.5) from phase 5 of the Coupled Model Intercomparison Project (CMIP5), the carbon effects of these land cover changes are included, although the biogeophysical effects are not. The afforestation in RCP4.5 has important biogeophysical impacts on climate, in addition to the land carbon changes, which are directly related to the assumption of crop yield increase and the universal carbon tax. To investigate the biogeophysical climatic impact of combinations of agricultural crop yield increases and carbon pricing mitigation, five scenarios of land-use change based on RCP4.5 are used as inputs to an earth system model [Hadley Centre Global Environment Model, version 2–Earth System (HadGEM2-ES)]. In the scenario with the greatest increase in agricultural land (as a result of no increase in crop yield and no climate mitigation) there is a significant −0.49 K worldwide cooling by 2100 compared to a control scenario with no land-use change. Regional cooling is up to −2.2 K annually in northeastern Asia. Including carbon feedbacks from the land-use change gives a small global cooling of −0.067 K. This work shows that there are significant impacts from biogeophysical land-use changes caused by assumptions of crop yield and carbon mitigation, which mean that land carbon is not the whole story. It also elucidates the potential conflict between cooling from biogeophysical climate effects of land-use change and wider environmental aims.

Abstract

Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g., Pacific Ocean). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications.

Abstract

The use of autonomous profiling floats for observational estimates of radiometric quantities in the ocean is explored, and the use of this platform for validation of satellite-based estimates of remote sensing reflectance in the ocean is examined. This effort includes comparing quantities estimated from float and satellite data at nominal wavelengths of 412, 443, 488, and 555 nm, and examining sources and magnitudes of uncertainty in the float estimates. This study had 65 occurrences of coincident high-quality observations from floats and MODIS Aqua and 15 occurrences of coincident high-quality observations floats and Visible Infrared Imaging Radiometer Suite (VIIRS). The float estimates of remote sensing reflectance are similar to the satellite estimates, with disagreement of a few percent in most wavelengths. The variability of the float–satellite comparisons is similar to the variability of in situ–satellite comparisons using a validation dataset from the Marine Optical Buoy (MOBY). This, combined with the agreement of float-based and satellite-based quantities, suggests that floats are likely a good platform for validation of satellite-based estimates of remote sensing reflectance.

A series of water vapor intensive observation periods (WVIOPs) were conducted at the Atmospheric Radiation Measurement (ARM) site in Oklahoma between 1996 and 2000. The goals of these WVIOPs are to characterize the accuracy of the operational water vapor observations and to develop techniques to improve the accuracy of these measurements.

The initial focus of these experiments was on the lower atmosphere, for which the goal is an absolute accuracy of better than 2% in total column water vapor, corresponding to ~1 W m⁻² of infrared radiation at the surface. To complement the operational water vapor instruments during the WVIOPs, additional instrumentation including a scanning Raman lidar, microwave radiometers, chilled-mirror hygrometers, a differential absorption lidar, and ground-based solar radiometers were deployed at the ARM site. The unique datasets from the 1996, 1997, and 1999 experiments have led to many results, including the discovery and characterization of a large (> 25%) sonde-to-sonde variability in the water vapor profiles from Vaisala RS-80H radiosondes that acts like a height-independent calibration factor error. However, the microwave observations provide a stable reference that can be used to remove a large part of the sonde-to-sonde calibration variability. In situ capacitive water vapor sensors demonstrated agreement within 2% of chilled-mirror hygrometers at the surface and on an instrumented tower. Water vapor profiles retrieved from two Raman lidars, which have both been calibrated to the ARM microwave radiometer, showed agreement to within 5% for all altitudes below 8 km during two WVIOPs. The mean agreement of the total precipitable water vapor from different techniques has converged significantly from early analysis that originally showed differences up to 15%. Retrievals of total precipitable water vapor (PWV) from the ARM microwave radiometer are now found to be only 3% moister than PWV derived from new GPS results, and about 2% drier than the mean of radiosonde data after a recently defined sonde dry-bias correction is applied. Raman lidar profiles calibrated using tower-mounted chilled-mirror hygrometers confirm the expected sensitivity of microwave radiometer data to water vapor changes, but it is drier than the microwave radiometer (MWR) by 0.95 mm for all PWV amounts. However, observations from different collocated microwave radiometers have shown larger differences than expected and attempts to resolve the remaining inconsistencies (in both calibration and forward modeling) are continuing.

The paper concludes by outlining the objectives of the recent 2000 WVIOP and the ARM–First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX), the latter of which switched the focus to characterizing upper-tropospheric humidity measurements.

Abstract

This work is motivated by previous studies of transatlantic transport of Saharan dust and the observed quasi-static nature of coarse mode aerosol with a volume median diameter (VMD) of approximately 3.5 μm. The authors examine coarse mode contributions from transpacific transport of dust to North American aerosol properties using a dataset collected at the high-elevation Storm Peak Laboratory (SPL) and the nearby Atmospheric Radiation Measurement (ARM) Mobile Facility. Collected ground-based data are complemented by quasi-global model simulations and satellite and ground-based observations. The authors identify a major dust event associated mostly with a transpacific plume (about 65% of near-surface aerosol mass) in which the coarse mode with moderate (~3 μm) VMD is distinct and contributes substantially to total aerosol volume (up to 70%) and scattering (up to 40%). The results demonstrate that the identified plume at the SPL site has a considerable fraction of supermicron particles (VMD ~3 μm) and, thus, suggest that these particles have a fairly invariant behavior despite transpacific transport. If confirmed in additional studies, this invariant behavior may simplify considerably parameterizations for size-dependent processes associated with dust transport and removal.

Abstract

Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision making processes. Observation of precipitation in the complex Bay Area landscape of California’s coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels - especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and world-wide.

Many of the clouds important to the Earth's energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m⁻²; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and included 18 different algorithms to evaluate their retrieved LWP, optical depth, and effective radii. Surprisingly, evaluation of the simplest case, a single-layer overcast stratocumulus, revealed that huge discrepancies exist among the various techniques, even among different algorithms that are in the same general classification. This suggests that, despite considerable advances that have occurred in the field, much more work must be done, and we discuss potential avenues for future research.)