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The article presents updates related to climatology and weather studies in the U.S. Research reveals the significance of using the cloud-resolving model simulations in forecasting the dangers of lightning. Meanwhile, several climate scientists are searching for the best method in estimating the peak surface winds of a hurricane. Moreover, it also looks on the effectiveness of the latest multiple-Doppler tornado detection in determining the upcoming tornadoes.

The article presents a study which examines the effect of synoptic-scale forcing on the stratospheric sudden warnings (SSWs) in 2006 in the U.S. The researchers used the meteorological fields from Goddard Earth Observing System (GEOS)-4 analyses in determining such effects. The study found out that stratospheric polar displaced off the pole due to earlier minor warming events. The researchers also suggest that there is a need for further investigation to determine the kind of fraction of major SSWs are initiated.

Climate models provide information that resource managers, policy makers, and researchers can use when planning for the future. While this information is valuable in the broad sense, the low spatial-resolution often lacks local details that resource managers and decision makers need to plan for their communities. Therefore, statistically downscaled climate projections provide a high-resolution output and offer local information that is more beneficial than coarse-resolution global climate model output. In the Red River Basin, located in the south-central U.S., this detailed information is used to develop long-term water plans. This area is prone to drought conditions and heavy precipitation events, and studies have consistently estimated that this will continue in the future. This paper introduces a dataset of statistically downscaled climate projections of daily minimum and maximum temperature and daily precipitation that is a useful tool for studies regarding climatological and hydrological aspects in the region. The dataset was created using two quantile mapping techniques to downscale the CCSM4, MPI-ESM-LR, and MIROC5 model outputs to a 0.1-degree spatial resolution. Furthermore, we describe the added value of coproduction of knowledge between climate scientists and end users, or in this case impacts modelers and decision makers, for creating climate projections that can be used for climate risk assessments. A case study of the data's development and application is provided, detecting the mean daily changes in temperature and precipitation through the end of the century in the Red River Basin for two representative concentration pathways. After applying the users' inputs to develop the datasets, results for this example estimate an increase in mean daily precipitation in the eastern portion of the basin and as much as a 15% decline in the west by the end of the century. Furthermore, mean daily temperature is expected to rise across the entire basin in all scenarios by up to 6–7°C. [ABSTRACT FROM AUTHOR]

Freezing rain can cause severe impacts, particularly when it persists for many hours. In this paper, we present the climatology of long-duration (6 or more hours) freezing rain events in the United States and Canada from 1979 to 2016. We identify three focus regions from this climatology and examine the archetypal thermodynamic evolution of events in each region using surface and radiosonde observations. Long-duration events occur most frequently in the northeastern United States and southeastern Canada, where freezing rain typically begins as lower-tropospheric warm-air advection develops the warm layer aloft. This warm-air advection and the latent heat of fusion released when rain freezes at the surface erode the cold layer, and freezing rain transitions to rain once the surface temperature reaches 0°C. In the southeastern United States, a larger percentage of events are of long duration than elsewhere in North America. Weak surface cold-air advection and evaporative cooling in the particularly dry onset cold layers there prevent surface temperatures from rising substantially during events. Finally, the south-central United States has a regional maximum in the occurrence of the top 1% of events by duration (18 or more hours), despite the relative rarity of freezing rain there. These events are associated with particularly warm/deep onset warm layers, with persistent low-level cold-air advection maintaining the cold layer. The thermodynamic evolutions we have identified highlight characteristics that are key to supporting persistent freezing rain in each region and may warrant particular attention from forecasters tasked with predicting these events. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

Although substantial attention has focused on efforts by the new Administration to block environmental policies, climate politics have been contentious in the US since well before the election of Donald Trump. In this paper, we extend previous work on empirical examinations of echo chambers in US climate politics using new data collected on the federal climate policy network in summer 2016. We test for the similarity and differences at two points in time in homophily and echo chambers using Exponential Random Graph Models (ERGM) to compare new findings from 2016 to previous work on data from 2010. We show that echo chambers continue to play a significant role in the network of information exchange among policy elites working on the issue of climate change. In contrast to previous findings where echo chambers centered on a binding international commitment to emission reductions, we find that the pre-existing echo chambers have almost completely disappeared and new structures have formed around one of the main components of the Obama Administration’s national climate policy: the Clean Power Plan. These results provide empirical evidence that science communication and policymaking at the elite level shift in relation to the policy instruments under consideration. [ABSTRACT FROM AUTHOR]

Traditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin), over a 24-h period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-yr normals) and recent temperature trends and changes. However, many first-order weather stations-such as those at airports-also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day's temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging [i.e., (Tmax 1 Tmin)/2; average of 24 hourly temperature values] for 215 first-order weather stations across the conterminous United States (CONUS) over the 30-yr period 1981-2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method [(Tmax 1 Tmin)/2], particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice-daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially coherent pattern in the difference compared to the most recent part of the temperature record (2001-15). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change. [ABSTRACT FROM AUTHOR]

This is the second of two papers that examine the organization of the precipitation field during central U.S. cold-season cyclones involving inverted troughs (ITs). The first paper (Part I) used a climatology and composites to find synoptic-scale differences between storms with precipitation located ahead of the IT (ahead cases) and those with precipitation located behind the IT (behind cases). This paper expands the conclusions in Part I through the use of a comparative case study between two cyclones. The first cyclone, on 29 October 1996, was an ahead case that produced heavy rainfall and was associated with a potential vorticity (PV) anomaly moving across the central plains. The IT formed in the lee of the Rockies prior to 0600 UTC 29 October and moved east into the northern plains over the next 18 h. The trough itself was coincident with the limiting streamline, which separated moist air rising over the warm front from dry air subsiding behind the cyclone. The second cyclone, on 17–18 January 1996, had precipitation on both sides of the IT and was associated with heavy snow and blizzard conditions in the northern plains and significant ice accumulation in the western Great Lakes. The IT was associated with large frontogenesis over the snow area. The ascent was further enhanced by a jet streak moving across southern Canada. Dynamically, the IT resembled a warm front, with veering winds with height and a strong frontal inversion. The mechanism that appeared to control the different precipitation organization between the two systems was the orientation of the PV anomalies and the airstreams associated with their secondary circulations. This resulted in a differing orientation of the baroclinicity north and east of the cyclone. In the ahead case, the rising branches of the secondary circulations forced by the northern and southern anomalies remained separate. This allowed the baroclinicity to develop along the traditional warm front, while the IT never developed a thermal gradient as it moved east. In the both sides case, the southern stream anomaly helped to fix the northern anomaly-forced jet streak in place, so that a strong temperature gradient developed along the IT with strong frontogenesis and warm-air advection observed behind the IT. As the frontal circulation developed, the direct circulation associated with the right entrance region of a jet streak enhanced the ascent to the west of the IT. A conceptual model is proposed based upon the case studies and the results of Part I. This model can be used by forecasters to differentiate between the precipitation regimes in cyclones associated with ITs. [ABSTRACT FROM AUTHOR]

In this essay, I attempt to chronicle and assess the position of climatology in American geography during the past one hundred years with emphasis on the role of papers published in the Annals. The approach I take is influenced by my interest in the decline of physical geography, including climatology, beginning in the last half of the 1800s ( ) to its resurgence in the 1950s and 1960s, and reviews of the climatology literature that have appeared recently (e.g., ). I discuss climatology in American geography in three loosely defined periods. The first is the formative era in which physical geography, including climatology, was a dominant part of our discipline, and physical geographical conditions such as climate were considered determinants of human affairs. In the middle (regional) era, the physical geographic environment, including climatology, was largely taken to be a static physical milieu or “stage” on which human activity occurred, simply to be described by geographers as part of regional analyses. The modern era, which begins just after the Second World War, is a period of increasing diversity of subject matter and method in climatology within geography and in American geography generally. Today climatology enjoys a “growth industry” status that hardly could be imagined forty years ago. These changes did not occur in isolation but in the context of the scientific, technological, social, economic, and political conditions, and changes in which they were embedded, and the status of climatology as a field of study was an important part of the context. I also comment on the number of climatology papers published in the Annals, including a curious anomaly in the last decade. [ABSTRACT FROM AUTHOR]

Inhomogeneities in U.S. radiosonde data that used the VIZ and Vaisala RS80 cannot be explained by radiation errors, which can be removed by the heat balance models. WMO intercomparision data, modeling results, temperature time series, and 1200 minus 0000 UTC temperature differences are examined to show that there appears to be an error in the U.S. RS80/RSN93 temperature correction software. Radiosonde soundings taken at U.S. stations that launch Vaisala RS80 radiosondes, which are integrated within the National Weather Service (NWS) Microcomputer Automatic Radio-Theodolite (Micro-ART) system, should not be used in climate studies since there is a large systematic error of unknown origin in the temperature data. This paper is the first of two and is primarily concerned with the midtroposphere. The second paper discusses the large unexplained 0000 and 1200 UTC differences in the stratosphere. [ABSTRACT FROM AUTHOR]

This paper is the first of two papers that examines the organization of the precipitation field in central U.S. cold-season cyclones involving inverted troughs. The first portion of the study examines the varying precipitation distribution that occurred during a 6-yr synoptic climatology of inverted trough cases. The definition of inverted trough cases has been expanded from the groundbreaking work by Keshishian et al. by 1) not requiring a closed cyclonic isobar along the frontal wave along the conventional surface front and 2) not requiring a surface thermal gradient to be present along the inverted trough. Only 8.5% of the expanded dataset produced the precipitation primarily occurring to the west of the inverted trough ("behind" cases) as seen in Keshishian et al. The largest group of cases, comprising about 40% of the cases, produced precipitation that primarily occurred between the inverted trough and the conventional warm front ("ahead" cases). A composite study compared a subset of the ahead cases with a subset of the behind cases. The ahead cases tended to be more progressive with a stronger jet stream located over the center of the parent low. Broad warm-air advection and frontogenesis in the lower troposphere were observed between the inverted trough and the surface warm front. Cold-air advection to the west of the inverted trough precluded the development of "wraparound precipitation." In contrast, the behind cases had a stronger low-latitude wave couplet with a trough upstream of the surface low and a ridge downstream. The region of warm-air advection and frontogenesis were displaced to the west of the inverted trough and surface cyclone. In addition, the entrance region of a southwest-northeast-oriented jet streak aided the development of ascent to the west of the inverted trough while precluding the development of precipitation to the north of the conventional warm front. Thus, the inverted trough tended to act like a warm front in behind cases,... [ABSTRACT FROM AUTHOR]

This paper describes a new climate model and its potential application to the study of ENSO impacts. The model is a regional spectral model embedded within a global coupled ocean–atmosphere model. The atmospheric part of the model consists of a global spectral model with triangular truncation T63 and a nested regional spectral model. The regional model is a relocatable spectral perturbation model that can be run at any horizontal resolution. In this paper the regional model was run with a resolution of 40 km. The global atmosphere model is coupled to the Max Planck global ocean model. No flux correction or anomaly coupling is used. An ensemble of 120-day integrations was conducted using the coupled nested system for the boreal winters of 1987 and 1988. A control integration was also performed in which observed SSTs were used in both the global and regional models. Two domains were chosen for the regional model: the southeast United States and western North America. Results from the global models show that the models reproduce many of the large-scale ENSO climate variations including the shifts in the Pacific ITCZ and SPCZ along with a Pacific–North America response in the 500-hPa height field. These results are compared against the corresponding ECMWF and Global Precipitation Climatology Centre analysis. Over the southeast United States both the global and regional models captured the precipitation variations between the two years as compared with the monthly mean cooperative station data. It is shown that the regional model solution is consistent with the global model solution, but with more realistic detail. Finally prospects for using this coupled nested ocean–atmosphere regional spectral model for downscaling are discussed. [ABSTRACT FROM AUTHOR]

In this paper, an objective analysis of spatial tornado risk in the United States is performed, using a somewhat different dataset than in some previous tornado climatologies. The focus is on significant tornadoes because their reporting frequency has remained fairly stable for several decades. Also, data before 1973 are excluded, since those tornadoes were rated after the fact and were often overrated. Tornado pathlength within the vicinity of a grid point is used to show tornado risk, as opposed to tornado days or the total number of reported tornadoes. The possibility that many tornadoes in the Great Plains were underrated due to the lack of damage indicators, causing a low bias in the number of significant tornadoes there, is mostly discounted through several analyses. The kernel density analysis of 1973-2011 significant tornadoes performed herein shows that the area of highest risk for tornadoes in the United States extends roughly from Oklahoma to Tennessee and northwestern Georgia, with the highest risk in the southeastern United States, from central Arkansas across most of Mississippi and northern Alabama. [ABSTRACT FROM AUTHOR]

Numerical models have long predicted that the deforestation of the Amazon would lead to large regional changes in precipitation and temperature, but the extratropical effects of deforestation have been a matter of controversy. This paper investigates the simulated impacts of deforestation on the northwest United States December-February climate. Integrations are carried out using the Ocean-Land-Atmosphere Model (OLAM), here run as a variable-resolution atmospheric GCM, configured with three alternative horizontal grid meshes: 1) 25-km characteristic length scale (CLS) over the United States, 50-km CLS over the Andes and Amazon, and 200-km CLS in the far-field; 2) 50-km CLS over the United States, 50-km CLS over the Andes and Amazon, and 200-km CLS in the far-field; and 3) 200-km CLS globally. In the high-resolution simulations, deforestation causes a redistribution of precipitation within the Amazon, accompanied by vorticity and thermal anomalies. These anomalies set up Rossby waves that propagate into the extratropics and impact western North America. Ultimately, Amazon deforestation results in 10%-20% precipitation reductions for the coastal northwest United States and the Sierra Nevada. Snowpack in the Sierra Nevada experiences declines of up to 50%. However, in the coarse-resolution simulations, this mechanism is not resolved and precipitation is not reduced in the northwest United States. These results highlight the need for adequate model resolution in modeling the impacts of Amazon deforestation. It is concluded that the deforestation of the Amazon can act as a driver of regional climate change in the extratropics, including areas of the western United States that are agriculturally important. [ABSTRACT FROM AUTHOR]

The 1981-2010 'U.S. Climate Normals' released by the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center include a suite of monthly, seasonal, and annual statistics that are based on precipitation, snowfall, and snow-depth measurements. This paper describes the procedures used to calculate the average totals, frequencies of occurrence, and percentiles that constitute these normals. All parameters were calculated from a single, state-of-the-art dataset of daily observations, taking care to produce normals that were as representative as possible of the full 1981-2010 period, even when the underlying data records were incomplete. In the resulting product, average precipitation totals are available at approximately 9300 stations across the United States and parts of the Caribbean Sea and Pacific Ocean islands. Snowfall and snow-depth statistics are provided for approximately 5300 of those stations, as compared with several hundred stations in the 1971-2000 normals. The 1981-2010 statistics exhibit the familiar climatological patterns across the contiguous United States. When compared with the same calculations for 1971-2000, the later period is characterized by a smaller number of days with snow on the ground and less total annual snowfall across much of the contiguous United States; wetter conditions over much of the Great Plains, Midwest, and northern California; and drier conditions over much of the Southeast and Pacific Northwest. These differences are a reflection of the removal of the 1970s and the addition of the 2000s to the 30-yr-normals period as part of this latest revision of the normals. [ABSTRACT FROM AUTHOR]

Climate time series often have artificial shifts induced by instrumentation changes, station relocations, observer changes, etc. Climate time series also often exhibit long-term trends. Much of the recent literature has focused on identifying the structural breakpoint time(s) of climate time series-the so-called changepoint problem. Unfortunately, application of rudimentary mean-shift changepoint tests to scenarios with trends often leads to the erroneous conclusion that a mean shift occurred near the series' center. This paper examines this problem in detail, constructing some simple homogeneity tests for series with trends. The asymptotic distribution of the proposed statistic is derived; en route, an attempt is made to unify the asymptotic properties of the changepoint methods used in today's climate literature. The tests presented here are linked to the ubiquitous t test. Application is made to two temperature records: 1) the continental United States record and 2) a local record from Jacksonville, Illinois. [ABSTRACT FROM AUTHOR]

The NOAA National Climatic Data Center maintains tables for temperature and precipitation extremes in each of the U.S. states. Many of these tables were several years out of date, however, and therefore did not include a number of recent record-setting meteorological observations. Furthermore, there was no formal process for ensuring the currency of the tables or evaluating observations that might tie or break a statewide climate record. This paper describes the evaluation and revision of the statewide climate-extremes tables for all-time maximum and minimum temperature, greatest 24-h precipitation and snowfall, and greatest snow depth (the five basic climate elements observed on a daily basis by the NOAA Cooperative Weather Network). The process resulted in the revision of 40% of the values listed in those tables and underscored both the necessity of manual quality-assurance methods and the importance of continued climate-monitoring and data-rescue activities to ensure that potential record values are not overlooked. [ABSTRACT FROM AUTHOR]

Abstract: Discovering and accessing hydrologic and climate data for use in research or water management can be a difficult task that consumes valuable time and personnel resources. Until recently, this task required discovering and navigating many different data repositories, each having its own website, query interface, data formats, and descriptive language. New advances in cyberinfrastructure and in semantic mediation technologies have provided the means for creating better tools supporting data discovery and access. In this paper we describe a freely available and open source software tool, called HydroDesktop, that can be used for discovering, downloading, managing, visualizing, and analyzing hydrologic data. HydroDesktop was created as a means for searching across and accessing hydrologic data services that have been published using the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS). We describe the design and architecture of HydroDesktop, its novel contributions in web services-based hydrologic data search and discovery, and its unique extensibility interface that enables developers to create custom data analysis and visualization plug-ins. The functionality of HydroDesktop and some of its existing plug-ins are introduced in the context of a case study for discovering, downloading, and visualizing data within the Bear River Watershed in Idaho, USA. [Copyright &y& Elsevier]

In many regions of the world, the extremes of winter cold are a major determinant of the geographic distribution of perennial plant species and of their successful cultivation. In the United States, the U.S. Department of Agriculture (USDA) Plant Hardiness Zone Map (PHZM) is the primary reference for defining geospatial patterns of extreme winter cold for the horticulture and nursery industries, home gardeners, agrometeorologists, and plant scientists. This paper describes the approaches followed for updating the USDA PHZM, the last version of which was published in 1990. The new PHZM depicts 1976-2005 mean annual extreme minimum temperature, in 2.8°C (5°F) half zones, for the conterminous United States, Alaska, Hawaii, and Puerto Rico. Station data were interpolated to a grid with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) climate-mapping system. PRISM accounts for the effects of elevation, terrain-induced airmass blockage, coastal effects, temperature inversions, and cold-air pooling on extreme minimum temperature patterns. Climatologically aided interpolation was applied, based on the 1971-2000 mean minimum temperature of the coldest month as the predictor grid. Evaluation of a standard-deviation map and two 15-yr maps (1976-90 and 1991-2005 averaging periods) revealed substantial vertical and horizontal gradients in trend and variability, especially in complex terrain. The new PHZM is generally warmer by one 2.8°C (5°F) half zone than the previous PHZM throughout much of the United States, as a result of a more recent averaging period. Nonetheless, a more sophisticated interpolation technique, greater physiographic detail, and more comprehensive station data were the main causes of zonal changes in complex terrain, especially in the western United States. The updated PHZM can be accessed online (). [ABSTRACT FROM AUTHOR]

Using observational data and outputs from an ocean general circulation model, the growth and decay of the South Atlantic subtropical dipole (SASD) are studied. The SASD is the most dominant mode of interannual variability in the South Atlantic Ocean, and its sea surface temperature (SST) anomaly shows a dipole pattern that is oriented in the northeast-southwest direction. The positive (negative) pole develops because the warming of the mixed layer by the contribution from the climatological shortwave radiation is enhanced (suppressed) when the mixed layer is thinner (thicker) than normal. The mixed layer depth anomaly over the positive (negative) pole is due to the suppressed (enhanced) latent heat flux loss associated with the southward migration and strengthening of the subtropical high. During the decay phase, since the temperature difference between the mixed layer and the entrained water becomes anomalously large (small) as a result of the positive (negative) mixed layer temperature anomaly, the cooling of the mixed layer by the entrainment is enhanced (reduced). In addition, the cooling of the mixed layer by the contribution from the climatological latent heat flux is enhanced (suppressed) by the same thinner (thicker) mixed layer. This paper demonstrates the importance of taking into account the interannual variations of the mixed layer depth in discussing the growth and decay of SST anomalies associated with the SASD. [ABSTRACT FROM AUTHOR]

This paper examines the broad social purpose of US climate science, which has benefitted from a public investment of more than $30 billion over the last 20 years. A public values analysis identifies five core public values that underpin the interagency program. Drawing from interviews, meeting observations, and document analysis, I examine the decision processes and institutional structures that lead to the implementation of climate science policy, and identify a variety of public values failures accommodated by this system. In contrast to other cases which find market values frameworks (the 'profit as progress' assumption) at the root of public values failures, this case shows how 'science values' ('knowledge as progress') may serve as an inadequate or inappropriate basis for achieving broader public values. For both institutions and individual decision makers, the logic linking science to societal benefit is generally incomplete, incoherent, and tends to conflate intrinsic and instrumental values. I argue that to be successful with respect to its motivating public values, the US climate science enterprise must avoid the assumption that any advance in knowledge is inherently good, and offer a clearer account of the kinds of research and knowledge advance likely to generate desirable social outcomes. [ABSTRACT FROM AUTHOR]

Hydrologists and other users need to know the uncertainty of the satellite rainfall datasets across the range of time--space scales over the whole domain of the dataset. Here, ''uncertainty'' refers to the general concept of the ''deviation'' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, which can be achieved with these satellite rainfall datasets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission dual-frequency precipitation radar. The TRMM Multisatellite Precipitation Analysis (TMPA) products over the United States spanning a record of 6 yr are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), which leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly, and weekly time scales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer, and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly time scales with the error doubling from monthly to weekly. However, at very low GV data density (<20%% of the domain), the transfer accuracy is too low to show any distinction as a function of the time scale of transfer. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA), which is designed to look at internal modes of variability, has often been applied beyond its intended design to study coupled modes of variability in combined datasets, also referred to as combined PCA. There are statistical techniques better suited for this purpose such as singular value decomposition (SVD) and canonical correlation analysis (CCA). In this paper, a different technique is examined that has not often been applied in climate science, that is, redundancy analysis (RA). Similar to multivariate regression, RA seeks to maximize the variance accounted for in one random vector that is linearly regressed against another random vector. RA can be used for forecasting and prediction studies of the climate system. This technique has the added advantage that the time-lagged redundancy index offers a robust method of identifying lead-lag relations among climate variables. In this study, combined PCA and RA of global sea surface temperatures (SSTs) and sea level pressures (SLPs) are carried out for the National Centers for Environmental Prediction (NCEP) reanalysis data and a simulation of the Canadian Centre for Climate Modeling and Analysis (CCCma) climate model. A simplified state-space model is also constructed to aid in the diagnosis and interpretation of the results. The relative advantages and disadvantages of combined PCA and RA are discussed. Overall, RA tends to provide a clearer and more consistent picture of the underlying physical processes than combined PCA. [ABSTRACT FROM AUTHOR]

In this paper, the authors evaluate the significance of multidecadal trends in seasonal-mean summertime precipitation and precipitation characteristics over the southwestern United States using stochastic, chain-dependent daily rainfall models. Unlike annual-mean precipitation, trends during the summertime monsoon, covering the period 1931–2000, indicate an overall increase in seasonal precipitation, the number of rainfall events, and the coverage of rainfall events in peripheral regions north of the “core” monsoon area of Arizona and western New Mexico. In addition, there is an increasing trend in intense storm activity and a decreasing trend in extreme dry-spell lengths. Over other regions of the domain, there are no discernible trends found in any of the observed characteristics. These trends are robust to the choice of start dates and, in the case of seasonal-mean precipitation, appear to persist into the current century. [ABSTRACT FROM AUTHOR]

A study conducted in the late 1980s determined the climatically optimal placement of the Major League Baseball season based on temperature and several other climatic variables. Numerous changes have taken place in Major League Baseball since the late 1980s which could have potentially altered the placement of the climatically optimal season. This paper updates the previous work in order to determine the current climatically optimal season. The results indicate that the climatically optimal season has only changed slightly since the time of the previous study. As found in the previous study, based on climate alone, the season should begin and end at a later date than is currently the case. [ABSTRACT FROM AUTHOR]

In this paper, a diagnostic metric—termed the local-convergence ratio—is used to analyze the contribution of evaporation and atmospheric moisture-flux convergence to model-based estimates of climatological precipitation over the North American continent. Generally, the fractional evaporative contribution is largest during spring and summer when evaporation is largest and decreases as evaporation decreases. However, there appears to be at least three regions with distinct spatiotemporal seasonal evolutions of this ratio. Over both the northern and western portions of the continent, the fractional evaporative contribution peaks in spring and early summer and decreases during fall and into winter. Over the northern portion, this fall decrease is related to an increase in atmospheric moisture-flux convergence associated with enhanced meridional moisture fluxes into the region; over the western coastal regions, the fall decrease in evaporative contribution is associated with a decrease in evaporation and an increase in total moisture-flux convergence, most likely associated with increased storm activity. In contrast, over the central portions of the continent, the fractional evaporative contribution to precipitation remains relatively low in spring—when enhanced low-level jet activity increases the low-level atmospheric moisture flux convergence into the region—and instead peaks in summer and fall—when the moisture-flux convergence associated with the low-level jet decreases and precipitation is balanced predominantly by local evaporation. Finally, over the southwestern United States and northwestern Mexico, the fractional evaporative contribution to precipitation is found to contain a wintertime minimum as well as a secondary minimum during summer. This latter feature is due to a substantial increase in low-level atmospheric moisture-flux convergence associated with the large-scale monsoon circulation that influences this region during this time. [ABSTRACT FROM AUTHOR]

In support of climate monitoring and assessments, the National Oceanic and Atmospheric Administration's (NOAA's) National Climatic Data Center has developed an improved version ofthe U.S. Historical Climatology Network temperature dataset (HCN version 2). In this paper, the HCN version 2 temperature data are described in detail, with a focus on the quality-assured data sources and the systematic bias adjustments. The bias adjustments are discussed in the context of their effect on U.S. temperature trends from the period 1895-2007 and in terms of the differences between version 2 and its widely used predecessor (now referred to as HCN version 1). Evidence suggests that the collective effect of changes in observation practice at U.S. HCN stations is systematic and ofthe same order of magnitude as the background climate signal. For this reason, bias adjustments are essential to reducing the uncertainty in U.S. climate trends. The largest biases in the HCN are shown to be associated with changes to the time of observation and with the widespread changeover from liquid-in-glass thermometers to the maximum minimum temperature system (MMTS). With respect to HCN version 1, HCN version 2 trends in maximum temperatures are similar, while minimum temperature trends are somewhat smaller because of 1) an apparent overcorrection in HCN version 1 for the MMTS instrument change and 2) the systematic effect of undocumented station changes, which were not addressed in HCN version 1. [ABSTRACT FROM AUTHOR]

Abstract: Severe thunderstorms constitute a major weather hazard in Europe, with an estimated total damage of 5–8 billion euros each year nowadays. Even though there is an upward trend in damage due to increases in vulnerability and possibly also due to climate change impacts, a pan-European database of severe thunderstorm reports in a homogeneous data format did not exist until a few years ago. The development of this European Severe Weather Database (ESWD) provided the final impetus for the establishment of the European Severe Storms Laboratory (ESSL) as a non-profit research organisation in 2006, after having started as an informal network in 2002. Our paper provides an overview of the first research results that have been achieved by ESSL. We start by outlining the reporting practice and quality-control procedure for the database, which has been enhanced by a major software upgrade in the fall of 2008. It becomes apparent that the state of reporting converges to a realistic description of the severe storms climatology, corroborating, for instance, earlier estimates of tornado occurrence in Europe. Nevertheless, a further rise in the number of reported events must be expected, even without the presence of any physical trends. The European tornado and damaging wind intensity distributions as a function of the Fujita scale are quantitatively similar to long-term distributions from the USA, except for a strong underreporting of weak events (F0) that still persists in Europe. In addition, the ESSL has recently proposed a new wind speed scale, the Energy- or “E-scale” which is linked to physical quantities and can be calibrated. Finally, we demonstrate the large potential of ESWD data use for forecast or nowcasting/warning verification purposes. [Copyright &y& Elsevier]

This paper studies the impact of pragmatic and optimal transfer schemes on the incentives for regions to join international climate agreements. With an applied model that comprises twelve world regions we investigate: (i) a benchmark without transfers, (ii) scenarios with allocation-based rules where coalition members receive tradable emission permits proportional to initial or future emissions, (iii) scenarios with outcome-based rules where the coalition surplus is distributed proportional to emissions, and (iv) a scenario based on an optimal sharing rule where the coalition surplus is distributed proportional to outside option payoffs. We find that well-designed transfer schemes can stabilise larger coalitions and increase global abatement levels. In our applied setting we find that for allocation-based and outcome-based rules only small coalitions are stable, and, in the case of grandfathered emission permits, there is no stable coalition at all. Some obstacles associated with grandfathered emission permits can be overcome by incorporating the expected growth of emissions in developing countries in the distribution of emission permits. For the optimal transfer scheme we find that larger coalitions, which include key players such as the United States and China, can be stable, but no transfer scheme is capable of stabilising the Grand Coalition. [Copyright &y& Elsevier]

This paper tries to identify one of the reasons for the poor land skin temperature simulated by a climate model over Greenland. It first compares ICEsat surface height measurements over Greenland with those used by the model and reveals that the surface height of Greenland prescribed in the National Center for Atmospheric Research (NCAR) Community Climate System Model/Community Land Model version 3 (CCSM/CLM3) differs greatly from the satellite measurements from National Aeronautics and Space Administration (NASA) ICEsat at edges and central glacier regions. This deficiency, in part, leads to underestimated skin temperatures at coastal regions-the areas where significant ice sheet melt is observed. Furthermore, sensitivity studies reveal that surface skin temperature simulations of Greenland would be significantly improved if the more accurate surface height is used. The problem of the height used in current global climate model is mainly due to the fact that the model has to use coarse grid size, and within one grid, land surface height has high heterogeneity. How to assign a proper surface height for each model grid and meanwhile adequately present the high heterogeneity of land surface is a great challenge in current model development. [ABSTRACT FROM AUTHOR]

In the Atmospheric Model Intercomparison Project (AMIP) simulations the sea surface temperatures (SSTs) are specified and the oceanic evolution consistent with air–sea interaction is not included. This omission could lead to errors in the atmospheric response to SSTs. At the same time, the AMIP experimental setup is well suited for investigating many aspects of climate variability (e.g., the attribution of the interannual atmospheric variability) and continues to be extensively used. As coupled El Niño–Southern Oscillation (ENSO) SST variability is a dominant factor in determining the predictable component of the observed interannual atmospheric variability, the difference in the atmospheric response to ENSO SSTs between AMIP and coupled simulations is investigated. The results indicate that the seasonal atmospheric response to ENSO between coupled and uncoupled integrations is similar, and the inclusion of oceanic evolution consistent with air–sea interaction does not play a dominant role. The analysis presented in this paper is one step toward assessing differences in atmospheric response to SSTs in coupled and uncoupled simulations, and is required to correctly interpret the results of AMIP simulations. [ABSTRACT FROM AUTHOR]

The 12–13 June 2002 convective initiation case from the International H2O Project (IHOP_2002) field experiment over the central Great Plains of the United States is simulated numerically with the Advanced Regional Prediction System (ARPS) at 3-km horizontal resolution. The case involves a developing mesoscale cyclone, a dryline extending from a low center southwestward with a cold front closely behind, which intercepts the midsection of the dryline, and an outflow boundary stretching eastward from the low center resulting from earlier mesoscale convection. Convective initiation occurred in the afternoon at several locations along and near the dryline or near the outflow boundary, but was not captured by the most intensive deployment of observation instruments during the field experiment, which focused instead on the dryline–outflow boundary intersection point. Standard and special surface and upper-air observations collected during the field experiment are assimilated into the ARPS at hourly intervals in a 6-h preforecast period in the control experiment. This experiment captured the initiation of four groups of convective cells rather well, with timing errors ranging between 10 and 100 min and location errors ranging between 5 and 60 km. The general processes of convective initiation are discussed. Interestingly, a secondary initiation of cells due to the collision between the main outflow boundary and the gust fronts developing out of model-predicted convection earlier is also captured accurately about 7 h into the prediction. The organization of cells into a squall line after 7 h is reproduced less well. A set of sensitivity experiments is performed in which the impact of assimilating nonstandard data gathered by IHOP_2002, and the length and interval of the data assimilation are examined. Overall, the control experiment that assimilated the most data produced the best forecast although some of the other experiments did better in some aspects, including the timing and location of the initiation of some of the cell groups. Possible reasons for the latter results are suggested. The lateral boundary locations are also found to have significant impacts on the initiation and subsequent evolution of convection, by affecting the interior flow response and/or feeding in more accurate observation information through the boundary, as available gridded analyses from a mesoscale operational model were used as the boundary condition. Another experiment examines the impact of the vertical correlation scale in the analysis scheme on the cold pool analysis and the subsequent forecast. A companion paper will analyze in more detail the process and mechanism of convective initiation, based on the results of a nested 1-km forecast. [ABSTRACT FROM AUTHOR]

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1° equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representative of the radiative mean between the two cloud layers. In atmospheres prone to temperature inversions, the MODIS cloud algorithm places the cloud above the inversion and hence is as much as 200 hPa off its true location. The wealth of new information available from the MODIS operational cloud products offers the promise of improved cloud climatologies. This paper 1) describes the cloud-top pressure and amount algorithm that has evolved through collection 5 as experience has been gained with in-flight data from NASA Terra and Aqua platforms; 2) compares the MODIS cloud-top pressures, converted to cloud-top heights, with similar measurements from airborne and space-based lidars; and 3) introduces global maps of MODIS and High Resolution Infrared Sounder (HIRS) cloud-top products. [ABSTRACT FROM AUTHOR]

Motivated by the intensity and severity of winds and temperature falls that frequently accompany rapidly developing cold fronts in northern Utah, this paper presents a 25-yr climatology of strong cold frontal passages over the Intermountain West and adjoining western United States. Using conventional surface observations and the North American Regional Reanalysis, strong cold frontal passages are identified based on a temperature fall of 7°C or greater in a 2–3-h period, a concurrent pressure rise of 3 hPa or greater, and the presence of a large-scale 700-hPa temperature gradient of at least 6°C (500 km)-1. The number of strong cold frontal passages exhibits a strong continental signature with very few events (<10) along the Pacific coast and more than 200 events east of the Continental Divide. The number of events increases dramatically from the Cascade Mountains and Sierra Nevada to northern Utah, indicating that the Intermountain West is a frequent cold front breeding ground. A composite of the 25 strongest events at Salt Lake City (based on the magnitude of the temperature fall) reveals that confluent deformation acting on a broad baroclinic zone over central Nevada commonly initiates Intermountain frontogenesis. The confluent deformation develops in southwesterly large-scale flow and appears to be enhanced by flow deflection around the Sierra Nevada. Quasi-stationary development and intensification of the southwest–northeast-oriented cold front then occurs as a mobile upper-level trough approaches from the west. The front becomes mobile as cold advection and ascent associated with the upper-level trough overtake the low-level front. Cloud and precipitation observations suggest that differential diabatic heating contributes to the rapid frontal intensification in many events. [ABSTRACT FROM AUTHOR]

Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from temperature records of radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quantifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combination of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade-1) in the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radiosondes. [ABSTRACT FROM AUTHOR]

For the purpose of producing datasets for regional-scale climate change research and application, the NCEP–NCAR reanalysis for the period 1948–2005 was dynamically downscaled to hourly, 10-km resolution over California using the Regional Spectral Model. This is Part I of a two-part paper, describing the details of the downscaling system and comparing the downscaled analysis [California Reanalysis Downscaling at 10 km (CaRD10)] against observation and global analysis. An extensive validation of the downscaled analysis was performed using station observations, Higgins gridded precipitation analysis, and Precipitation-Elevation Regression on Independent Slopes Model (PRISM) precipitation analysis. In general, the CaRD10 near-surface wind and temperature fit better to regional-scale station observations than the NCEP–NCAR reanalysis used to force the regional model, supporting the premise that the regional downscaling is a viable method to attain regional detail from large-scale analysis. This advantage of CaRD10 was found on all time scales, ranging from hourly to decadal scales (i.e., from diurnal variation to multidecadal trend). Dynamically downscaled analysis provides ways to study various regional climate phenomena of different time scales because all produced variables are dynamically, physically, and hydrologically consistent. However, the CaRD10 is not free from problems. It suffers from positive bias in precipitation for heavy precipitation events. The CaRD10 is inaccurate near the lateral boundary where regional detail is damped by the lateral boundary relaxation. It is important to understand these limitations before the downscaled analysis is used for research. [ABSTRACT FROM AUTHOR]

Freezing precipitation is a persistent winter weather problem that costs the United States millions of dollars annually. Costs and infrastructure disruption may be greatly reduced by ice-storm warnings issued by the National Weather Service (NWS), and by the development of climatologies that allow improved design of infrastructure elements. However, neither the NWS nor developers of climatologies have had direct measurements of ice-storm accumulations as a basis for issuing warnings and developing storm design standards. This paper describes the development of an aviation routine/special weather report (METAR/SPECI) remark that will report quantitative ice thickness at over 650 locations during ice storms using new algorithms developed for the Automated Surface Observing System (ASOS). Characteristics of the ASOS icing sensor, a field program to develop the algorithms, tests of accuracy, application of the algorithms, and sources of error are described, as is the implementation of an ice-thickness METAR/SPECI remark. The algorithms will potentially allow freezing precipitation events to be tracked with regard to ice accumulation in near–real time as they progress across the United States. [ABSTRACT FROM AUTHOR]

Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or “fingerprints” associated with past events. The potential refinement and applicability of this method as an operational forecasting tool employed by comparing numerical weather prediction forecasts with fingerprints already identified for major weather events are also discussed. [ABSTRACT FROM AUTHOR]

This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis. [ABSTRACT FROM AUTHOR]

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate. [ABSTRACT FROM AUTHOR]

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 global coupled climatic model has been used to generate a 10 000-yr simulation of “present” climate. The resultant dataset has been used to investigate a number of aspects of extremes associated with annual mean rainfall. Multimillennial time series of normalized rainfall amounts for selected points are used to highlight secular variability, spatial variations, and the differences between pluvial and drought conditions. Global distributions are also presented for selected rainfall characteristics, including the frequency of occurrence of specified rainfall anomalies with annual durations, the frequency of occurrence of 5-yr sequences of specified rainfall anomalies, and the maximum and minimum normalized rainfall amounts attained in the simulation. Such features cannot be obtained from observations because of their limited duration. A case study is also made of a megadrought over the southwestern United States, together with an analysis of the associated causal mechanisms. Given the exclusion of all external forcing from the model, it is concluded that the extreme annual mean rainfall extremes presented in the paper are attributable to stochastic events. [ABSTRACT FROM AUTHOR]

The study of climate change has deep roots in the history of North American ecology. At the time of the Wall Street crash and the Depression of the 1930s, America's Great Plains were struck by the Dust Bowl, a phenomenon of catastrophic soil erosion that resulted from the combined effects of intensive farming practices and a particularly harsh drought. Contemporaneously, the ecologist Frederic Clements proposed a theory of plant succession that itself took the history of the Great Plains as its model, and drew on the notion of climatic cycles. This theory became established as the model for ecological expertise in the politics of conservation adopted by the Roosevelt administration. In this paper, I will show how climatology became inscribed in plant ecology not only for epistemological reasons, but also due to an ideology that promoted the ecologist as an expert in the optimization of resources, in an illustration of the tripartite relationship between ecology, politics, and climate change. [ABSTRACT FROM AUTHOR]

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. [ABSTRACT FROM AUTHOR]

The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes. [ABSTRACT FROM AUTHOR]

The development of the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) will make possible a wealth of new geophysical research opportunities in the areas of atmospheric chemistry, climate forcing, weather system structure and evolution, the carbon and water vapor cycles, and ecosystem processes. In this paper, we present a brief background on the history of the HIAPER project and discuss the modifications made to the basic aircraft [a Gulfstream V (GV) business jet] and the infrastructure systems installed to transform it into an environmental research platform. General aircraft performance capabilities that make the GV uniquely suited for high-altitude, long-range studies of geophysical phenomena are also discussed. The conduct of research with HIAPER will require that suitable instrumentation payloads are available for use on the aircraft, and the processes followed by the National Science Foundation (NSF) and the National Center for Atmospheric Research (NCAR) for the development of an initial platform instrumentation suite to meet critical measurements needs are described. HIAPER's unique configuration and capabilities will make it an effective tool for the conduct of weather and water cycle research, the study of atmospheric chemistry and climate forcing, and the monitoring of biosphere structure and productivity, as we shall discuss. We conclude with an overview of the objectives of the initial HIAPER flight-testing program and the process whereby this new research platform will be made available to members of the scientific community for the support of environmental research. [ABSTRACT FROM AUTHOR]

Amidst stormy atmospheric circulation, there are prominent recurrent patterns of variability in the planetary circulation, such as the Antarctic Oscillation (AAO), Arctic Oscillation (AO) or North Atlantic Oscillation (NAO), and the Pacific–North America (PNA) pattern. The role of the synoptic eddy and low-frequency flow (SELF) feedback in the formation of these dominant low-frequency modes is investigated in this paper using the linear barotropic model with the SELF feedback proposed in Part I. It is found that the AO-like and AAO-like leading singular modes of the linear dynamical system emerge from the stormy background flow as the result of a positive SELF feedback. This SELF feedback also prefers a PNA-like singular vector as well among other modes under the climatological conditions of northern winters. A model with idealized conditions of basic mean flow and activity of synoptic eddy flow and a prototype model are also used to illustrate that there is a natural scale selection for the AAO- and AO-like modes through the positive SELF feedback. The zonal scale of the localized features in the Atlantic (southern Indian Ocean) for AO (AAO) is largely related to the zonal extent of the enhanced storm track activity in the region. The meridional dipole structures of AO- and AAO-like low-frequency modes are favored because of the scale-selective positive SELF feedback, which can be heuristically understood by the tilted-trough mechanism. [ABSTRACT FROM AUTHOR]

Although the large U.S. meteorological community has made significant strides in weather diagnosis and prediction, progress has been slowed by a lack of cooperation, coordination, and pooling of resources. This paper analyzes such problems in a number of areas, ranging from numerical weather prediction to forecast dissemination, and proposes an alternative approach of greater community involvement in decision making, coupled with closely coordinated research and application, which might facilitate improvement in our ability to predict the weather and to apply weather information to societal needs. [ABSTRACT FROM AUTHOR]