An example of attenuation by wet snow on a radar dome

Abstract

A case study of the degradation of weather radar data by the accretion of wet snow on a radar dome is presented as a precautionary example to radar users. During the ERICA field program in 1989, accumulated precipitation on the radome at Halifax, Nova Scotia, attenuated signal in a distinctive, pie‐shaped sector on the upwind side of the radar.     

Climate Change Impacts for the Conterminous USA: An Integrated Assessment

Here we simulate dryland agriculture in the United States in order to assess potential future agricultural production under a set of general circulation model (GCM)-based climate change scenarios. The total national production of three major grain crops—corn, soybeans, and winter wheat—and two forage crops—alfalfa and clover hay—is calculated for the actual present day core production area (CPA) of each of these crops. In general, higher global mean temperature (GMT) reduces production and higher atmospheric carbon dioxide concentration ([CO₂]) increases production. Depending on the climatic change scenarios employed overall national production of the crops studied changes by up to plus or minus 25% from present-day levels. Impacts are more significant regionally, with crop production varying by greater than ±50% from baseline levels. Analysis of currently possible production areas (CPPAs) for each crop indicates that the regions most likely to be affected by climate change are those on the margins of the areas in which they are currently grown. Crop yield variability was found to be primarily influenced by local weather and geographic features rather than by large-scale changes in climate patterns and atmospheric composition. Future US agronomic potential will be significantly affected by the changes in climate projected here. The nature of the crop response will depend primarily on to what extent precipitation patterns change and also on the degree of warming experienced.     

Climate Change Impacts for the Conterminous USA: An Integrated Assessment Summary

This special issue of Climatic Change describes an effort to improve methodology for integrated assessment of impacts and consequences of climatic change. Highlights of the seven foregoing Parts (papers) that constitute this special issue are summarized here. The methodology developed involves construction of scenarios of climate change that are used to drive individual sectoral models for simulating impacts on crop production, irrigation demand, water supply and change in productivity and geography of unmanaged ecosystems. Economic impacts of the changes predicted by integrating the results of the several sectoral simulation models are calculated through an agricultural land-use model. While these analyses were conducted for the conterminous United States alone, their global implications are also considered in this summary as is the need for further improvements in integrated assessment methodology.     

A technical comment: Analysis of ‘examination of “global atmospheric temperature monitoring with satellite microwave measurements”’

The potential for residual hydrometeor contamination effects in the global temperature time series produced by Spencer and Christy from MSU channel 2 (MSU2) data has been addressed by Prabhakara et al. (1995, 1996). They use tropical oceanic MSU channel l (MSU1) data to estimate the hydrometeor effects on MSU2. We present several lines of evidence to show that their technique greatly overestimates the hydrometeor effects on MSU2. This overestimation is due to the faulty assumption that the hydrometeors that cause MSU1 warming are the same as (or always exist with) the hydrometeors that cause cooling in MSU2. Instead, the hydrometeors responsible for MSU1 warming are liquid phase, while those responsible for MSU2 cooling are large ice particles. Because liquid phase clouds are much more widespread than the large-ice portions of deep convective systems, their method greatly overestimates the areal coverage of contaminated tropical MSU2 data. In addition, we show that the convective screening procedure of Spencer and Christy removes the negative correlation between MSU1 and MSU2 their conclusions rest upon. Radiosonde validation of monthly tropical MSU2 anomalies over the tropical West Pacific also support these conclusions.     

Participatory modeling in dairy farm systems: a method for building consensual environmental sustainability using seasonal climate forecasts

Dairy farmers face increasing pressure to decrease environmental impact while remaining economically viable. Adaptation of farm management practices in response to seasonal climate forecasts may be one means of achieving these objectives. This paper describes the interactive and iterative process by which farmers, researchers, extension agents, regulatory agencies, and other stakeholders collaborated to create, calibrate, and validate the Dynamic North Florida Dairy Farm model (DyNoFlo), a whole-farm decision support system to decrease nitrogen leaching while maintaining profitability under variable climate conditions. Participatory modeling may enhance the creation of adoptable and adaptable user-friendly models that include environmental, economic and biophysical components. By providing farmers, policy makers, and other stakeholders with a more holistic view of current practices, common ground among them was more easily identified and collaboration was fostered. Farmer values included willingness to be good environmental stewards when they are profitable. The participatory research and development process enhanced understanding of and potential adaptation to seasonal climate variability conditioned to the El Niño Southern Oscillation (ENSO) phases in light of increasing environmental regulations and economic challenges. Adoption of the collaboratively-developed DyNoFlo is expected to be higher than usual because stakeholders feel greater ownership of the final product.     

Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.     

Edge-to-Stem Variability in Wet-Canopy Evaporation From an Urban Tree Row

Evaporation from wet-canopy (\(E_\mathrm{C}\)) and stem (\(E_\mathrm{S}\)) surfaces during rainfall represents a significant portion of municipal-to-global scale hydrologic cycles. For urban ecosystems, \(E_\mathrm{C}\) and \(E_\mathrm{S}\) dynamics play valuable roles in stormwater management. Despite this, canopy-interception loss studies typically ignore crown-scale variability in \(E_\mathrm{C}\) and assume (with few indirect data) that \(E_\mathrm{S}\) is generally \({<}2\%\) of total wet-canopy evaporation. We test these common assumptions for the first time with a spatially-distributed network of in-canopy meteorological monitoring and 45 surface temperature sensors in an urban Pinus elliottii tree row to estimate \(E_\mathrm{C}\) and \(E_\mathrm{S}\) under the assumption that crown surfaces behave as “wet bulbs”. From December 2015 through July 2016, 33 saturated crown periods (195 h of 5-min observations) were isolated from storms for determination of 5-min evaporation rates ranging from negligible to 0.67 \(\hbox {mm h}^{-1}\). Mean \(E_\mathrm{S}\) (0.10 \(\hbox {mm h}^{-1}\)) was significantly lower (\(p < 0.01\)) than mean \(E_\mathrm{C}\) (0.16 \(\hbox {mm h}^{-1}\)). But, \(E_\mathrm{S}\) values often equalled \(E_\mathrm{C}\) and, when scaled to trunk area using terrestrial lidar, accounted for 8–13% (inter-quartile range) of total wet-crown evaporation (\(E_\mathrm{S}+E_\mathrm{C}\) scaled to surface area). \(E_\mathrm{S}\) contributions to total wet-crown evaporation maximized at 33%, showing a general underestimate (by 2–17 times) of this quantity in the literature. Moreover, results suggest wet-crown evaporation from urban tree rows can be adequately estimated by simply assuming saturated tree surfaces behave as wet bulbs, avoiding problematic assumptions associated with other physically-based methods.     

Evaluating a modified point-based method to downscale cell-based climate variable data to high-resolution grids

To address the demand for high spatial resolution gridded climate data, we have advanced the Daymet point-based interpolation algorithm for downscaling global, coarsely gridded data with additional output variables. The updated algorithm, High-Resolution Climate Downscaler (HRCD), performs very good downscaling of daily, global, historical reanalysis data from 1° input resolution to 2.5 arcmin output resolution for day length, downward longwave radiation, pressure, maximum and minimum temperature, and vapor pressure deficit. It gives good results for monthly and yearly cumulative precipitation and fair results for wind speed distributions and modeled downward shortwave radiation. Over complex terrain, 2.5 arcmin resolution is likely too low and aggregating it up to 15 arcmin preserves accuracy. HRCD performs comparably to existing daily and monthly US datasets but with a global extent for nine daily climate variables spanning 1948–2006. Furthermore, HRCD can readily be applied to other gridded climate datasets.     

Recent progress in the operational forecasting of summer severe weather

Abstract

Summer severe weather (SSW) can strike suddenly and unexpectedly with disastrous consequences for human activity. Considerable progress has been made in the past ten years in the operational forecasting of SSW. Traditionally, SSW was defined to consist of tornadoes, strong winds, hail, lightning and heavy rain. Hazardous types of strong winds have recently been expanded to include microbursts, macrobursts and surfacing rear inflow jet damage behind mesoscale convective systems. Doppler radar was used to relate surface damage to the appropriate atmospheric phenomena, first diagnostically and then prognostically. This improvement in classification has fedback to and improved the forecast process. Concurrent progress has been made in the use of synoptic observations. The concept of helical wind profiles and improved knowledge of the role of dry mid‐level air has improved the forecasting of tornadoes and strong gusty winds. Moisture flux convergence, derived from surface measurements, shows great promise in identifying areas of storm initiation. Satellite imagery has been used to identify dynamical atmospheric boundaries. Numerical modelling of the interaction of environmental wind profiles and individual thunderstorms has greatly contributed to the understanding of SSW. Studies of spatial and temporal patterns of lightning, both specific cases and climatology, contribute to the forecasting of severe storms. Polarization radar results have shown progress in separating the signals of hail from those of rain and in the improved measurement of heavy rainfalls. Radar observation of clear air boundaries and their interactions show potential for the forecasting of thunderstorm initiation. Though not traditionally considered part of SSW, hurricanes that evolve into extra‐tropical storms share many of the same hazardous features. The progress in computing, communications and display technologies has also made substantial contributions to operational forecasting and to the dissemination of weather warnings.