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The article presents information on a study conducted on the impact of climate change on agriculture. In the study, Community Land Model (CLM)-Crop was validated against observations from two agricultural sites in the U.S., that were planted with maize and soybean. CLM-Crop was compared with the standard CLM3.5, which is associated with crops as grass. The study found that crop yields and productivity had a negative correlation with temperature, and a positive correlation with precipitation.

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]

In a changing climate, there is an interest in predicting how extreme rainfall events may change. Using historical records, several recent papers have evaluated whether high-intensity precipitation scales with temperature in accordance with the Clausius--Clapeyron (C--C) relationship. For varying locations in Europe, these papers have identified both super C--C relationships as well as a breakdown of the C--C relationship under dry conditions. In this paper, a similar analysis is carried out for the United States using data from 14 weather stations clustered in four different hydroclimatic regions: the coastal northeast, interior New York, the central plains, and the western plains. In all regions except interior New York state, 99th percentile 1-h precipitation generally followed the C--C relation. In interior New York, there was evidence that intensity scaled with a super C--C relationship. For the 99.9th percentile precipitation, interior New York displayed some moderate evidence of a super C--C relationship, the western plains showed little relation between precipitation and temperature, and the remainder of sites generally scaled with the C--C relationship. Also, if only July, August, and September precipitation is considered, all stations except those in interior New York have little relation between temperature and precipitation, suggesting that precipitation intensity during summer months may not be well constrained by the C--C relationship. Overall, the C--C relationship (or a variation thereof) does not appear to constrain extreme precipitation in all regions and in all seasons, and its ability to aid in constraining future predictions of extreme precipitation may only be relevant to certain locales and time periods. [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]

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]

High-quality, daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decision making. This paper describes the development, application, and assessment of methods to construct daily high-resolution (∼50-m cell size) meteorological grids for the 2003 calendar year in the Upper South Santiam Watershed (USSW), a 500-km2 mountainous catchment draining the western slope of the Oregon Cascade Mountains. Elevations within the USSW ranged from 194 to 1650 m. Meteorological elements modeled were minimum and maximum temperature; total precipitation, rainfall, and snowfall; and solar radiation and radiation-adjusted maximum temperature. The Parameter–Elevation Regressions on Independent Slopes Model (PRISM) was used to interpolate minimum and maximum temperature and precipitation. The separation of precipitation into rainfall and snowfall components used a temperature-based regression function. Solar radiation was simulated with the Image-Processing Workbench. Radiation-based adjustments to maximum temperature employed equations developed from data in the nearby H. J. Andrews Experimental Forest. The restrictive terrain of the USSW promoted cold-air drainage and temperature inversions by reducing large-scale airflow. Inversions were prominent nearly all year for minimum temperature and were noticeable even for maximum temperature during the autumn and winter. Precipitation generally increased with elevation over the USSW. In 2003, precipitation was nearly always in the form of rain at the lowest elevations but was about 50% snow at the highest elevations. Solar radiation followed a complex pattern related to terrain slope, aspect, and position relative to other terrain features. Clear, sunny days with a large proportion of direct radiation exhibited the greatest contrast in radiation totals, whereas cloudy days with primarily diffuse radiation showed little contrast. Radiation-adjusted maximum temperatures showed similar patterns. The lack of a high-quality observed dataset was a major issue in the interpolation of precipitation and solar radiation. However, observed data available for the USSW were superior to those available for most mountainous regions in the western United States. In this sense, the methods and results presented here can inform others performing similar studies in other mountainous regions. [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]

In this paper, the authors analyze simulations of present and future climates in the western United States performed with four regional climate models (RCMs) nested within two global ocean–atmosphere climate models. The primary goal here is to assess the range of regional climate responses to increased greenhouse gases in available RCM simulations. The four RCMs used different geographical domains, different increased greenhouse gas scenarios for future-climate simulations, and (in some cases) different lateral boundary conditions. For simulations of the present climate, RCM results are compared to observations and to results of the GCM that provided lateral boundary conditions to the RCM. For future-climate (increased greenhouse gas) simulations, RCM results are compared to each other and to results of the driving GCMs. When results are spatially averaged over the western United States, it is found that the results of each RCM closely follow those of the driving GCM in the same region in both present and future climates. This is true even though the study area is in some cases a small fraction of the RCM domain. Precipitation responses predicted by the RCMs in many regions are not statistically significant compared to interannual variability. Where the predicted precipitation responses are statistically significant, they are positive. The models agree that near-surface temperatures will increase, but do not agree on the spatial pattern of this increase. The four RCMs produce very different estimates of water content of snow in the present climate, and of the change in this water content in response to increased greenhouse gases. [ABSTRACT FROM AUTHOR]

We assess monthly temperature and precipitation data derived by six statistically downscaled data sets for 14 general circulation models (GCMs) from the Climate Model Intercomparison Program Phase 5. We use a simple monthly water balance model to quantify and decompose uncertainties associated with the GCMs and statistical techniques in projections for four snow‐dominated regions in the western United States. The end‐of‐century projections from GCMs exhibit substantial variation in change over the regions (temperature range of 2.8–8.0 °C and precipitation range of −22–31%). The six downscaled data sets exhibit disparate high‐resolution representations of the magnitude and spatial patterns of future temperature (up to 2.2 °C) and precipitation (up to 30%) for a common GCM. Two data sets derived by the same downscaling method (Multivariate Adaptive Constructed Analogs) produce median losses of snow water equivalent over the Upper Colorado of 51% and 81%. The principal causes of the differences among the downscaled projections are related to the gridded observations used to bias correct the historical GCM output. Specifically, (1) whether a fixed atmospheric lapse rate (−6.5 °C/km) or a spatially and temporally varying lapse rate is used to extrapolate lower elevation observations to high‐elevations and (2) whether high‐elevation station data (e.g., SNOTEL) are included in the observations. The GCM projections are the largest source of uncertainty in the monthly water balance model simulations; however, the differences among seasonal projections produced by downscaled data sets in some regions highlight the need for careful evaluation of the statistically downscaled data in climate impact studies. Plain Language Summary: Climate change information simulated by global climate models is downscaled using statistical methods to translate spatially coarse regional projections to finer resolutions that are desirable to researchers and managers to assess local climate impacts. Several statistical downscaling methods have been developed over the past 15 years, resulting in multiple data sets derived by different methods. We apply a simple hydrology model to demonstrate how the differences among these data sets result in disparate projections of snow loss and future changes in runoff in the western United States and provide guidance to users on how to select and evaluate the appropriate data set for their needs. Key Points: Application of a water balance model reveals differences among statistically downscaled data sets result in varying future projectionsThe choice of bias correction data set is a major factor in the simulation of high‐elevation snow and the amount of snow loss in the futureThe spread among GCMs is the largest source of uncertainty in projections, but differences attributed to downscaled data can be substantial [ABSTRACT FROM AUTHOR]

ABSTRACT The widespread influence of El Niño-Southern Oscillation ( ENSO) phases on regional climate is well known to contribute to climate anomalies around the world. Understanding the effects of climate variability given the ENSO phases is an important component to improve crop management and reduce climate risk due to potential changes in temperature and precipitation patterns. Recognizing the spatial structure in precipitation and temperature under different ENSO phases is fundamental for comprehensive environmental assessments through decisions and policies that consider climate variability at regional scales. The objective of this study was to investigate if there is evidence of changes to the spatial structure of precipitation and temperature associated with the three categories of ENSO phases: El Niño, La Niña and neutral years in two study areas, the Southeast (Alabama, Florida and Georgia) and the Midwest (Nebraska) United States. Daily data of maximum temperature, minimum temperatures and precipitation (1981-2010) from 407 weather stations were analysed. The spatial structure was measured by analysing the monthly correlation matrices calculated from daily observations. To determine the potential changes to spatial structures of precipitation and temperature, the spatial structure was compared with using the complete 30 year period divided by the three categories of the ENSO phases. The results showed statistically significant differences in the spatial structures of maximum and minimum temperature and precipitation (amount and distribution) among the different ENSO phases and between the combinations of two kinds of different ENSO phases. Statistically significant changes in spatial correlation at seasonal and monthly timescales among the different ENSO phases were also observed in both regions. The statistical differences were not uniform in both regions, and some region-variable-month combinations showed no significance. Knowledge of spatial structure among some combinations of different ENSO phases at each region would be useful to improve further applications of weather/climate data in other disciplines such as geo-spatial weather generator. [ABSTRACT FROM AUTHOR]

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of record-breaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico's history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing. [ABSTRACT FROM AUTHOR]

Climate 'normals' are statistical estimates of present and/or near-future climate means for such quantities as seasonal temperature or precipitation. In a changing climate, simply averaging a large number of previous years of data may not be the best method for estimating normals. Here eight formulations for climate normals, including the recently proposed 'hinge' function, are compared in artificial- and real-data settings. Although the hinge function is attractive conceptually for representing accelerating climate changes simply, its use is in general not yet justified for divisional U.S. seasonal temperature or precipitation. Averages of the most recent 15 and 30 yr have performed better during the recent past for U.S. divisional seasonal temperature and precipitation, respectively; these averaging windows are longer than those currently employed for this purpose at the U.S. Climate Prediction Center. [ABSTRACT FROM AUTHOR]

The article provides an overview of the 1981-2010 U.S. Climate Normals released by the National Oceanic and Atmospheric Administration (NOAA). It explains that this release features temperature homogenization at the monthly level and precipitation statistics. It also uses quality-controlled daily data and novel statistical methods for the calculation of daily temperature normals. Contact information is provided for interested parties.

Global solar radiation Rg is an important input for crop models to simulate crop responses. Because the scarcity of long and continuous records of Rg is a serious limitation in many countries, Rg is estimated using models. For crop-model application, empirical Rg models that use commonly measured meteorological variables, such as temperature and precipitation, are generally preferred. Although a large number of models of this kind exist, few have been evaluated for conditions in the United States. This study evaluated the performances of 16 empirical, temperature- and/or precipitation-based Rg models for the southeastern United States. By taking into account spatial distribution and data availability, 30 locations in the region were selected and their daily weather data spanning eight years obtained. One-half of the data was used for calibrating the models, and the other half was used for evaluation. For each model, location-specific parameter values were estimated through regressions. Models were evaluated for each location using the root-mean-square error and the modeling efficiency as goodness-of-fit measures. Among the models that use temperature or precipitation as the input variable, the Mavromatis model showed the best performance. The piecewise linear regression-based Wu et al. model (WP) performed best not only among the models that use both temperature and precipitation but also among the 16 models evaluated, mainly because it has separate relationships for low and high radiation levels. The modeling efficiency of WP was from ~5% to more than 100% greater than those of the other models, depending on models and locations. [ABSTRACT FROM AUTHOR]

Data from observations and the Intergovernmental Panel on Climate Change (IPCC) twentieth-century climate change model [phase 3 of the Coupled Model Intercomparison Project (CMIP3)] simulations were analyzed to examine the decadal changes of the impact of ENSO on air temperature Tair and precipitation P over the United States. The comparison of composites for the early period (1915–60) and the recent period (1962–2006) indicates that cooling (warming) over the south and warming (cooling) over the north during ENSO warm (cold) winters have been weakening. The ENSO influence on winter P over the Southwest is strengthening, while the impact on P over the Ohio Valley is weakening for the recent decades. These differences are not due to the long-term trends in Tair or P; they are attributed to the occurrence of the central Pacific (CPAC) ENSO events in the recent years. The CPAC ENSO differs from the canonical eastern Pacific (EPAC) ENSO. The EPAC ENSO has a sea surface temperature anomaly (SSTA) maximum in the eastern Pacific. Enhanced convection extends from the date line to the eastern Pacific, with negative anomalies in the western Pacific. The atmospheric responses resemble a tropical Northern Hemisphere pattern. The wave train is consistent with the north–south Tair contrast over North America during the EPAC ENSO winters. The CPAC ENSO has enhanced convection in the central Pacific. The atmospheric responses show a Pacific–North American pattern. It is consistent with west–east contrast in Tair and more rainfall over the Southwest during the CPAC ENSO winters. [ABSTRACT FROM AUTHOR]

This study examines the relationship between diurnal temperature range (DTR) and land use/land cover (LULC) in a portion of the Southeast. Temperature data for all synoptically weak days within a 10-yr period are gathered from the National Climatic Data Center for 144 weather stations. Each station is classified as one of the following LULC types: urban, agriculture, evergreen forest, deciduous forest, or mixed forest. A three-way analysis of variance and paired-sample t tests are used to test for significant DTR differences due to LULC, month, and airmass type. The LULC types display two clear groups according to their DTR, with agricultural and urban areas consistently experiencing the smallest DTRs, and the forest types experiencing greater DTRs. The dry air masses seem to enhance the DTR differences between vegetated LULC types by emphasizing the differences in evapotranspiration. Meanwhile, the high moisture content of moist air masses prohibits extensive evapotranspirational cooling in the vegetated areas. This lessens the DTR differences between vegetated LULC types, while enhancing the differences between vegetated land and urban areas. All of the LULC types exhibit an annual bimodal DTR pattern with peaks in April and October. Since both vegetated and nonvegetated areas experience the bimodal pattern, this may conflict with previous research that names seasonal changes in evapotranspiration as the most probable cause for the annual trend. These findings suggest that airmass type has a larger and more consistent influence on the DTR of an area than LULC type and therefore may play a role in causing the bimodal DTR pattern, altering DTR with the seasonal distribution of airmass occurrence. [ABSTRACT FROM AUTHOR]

On 24–25 February 2005, a significant East Coast cyclone deposited from 4 to nearly 12 in. (∼10–30 cm) of snow on parts of the northeastern United States. The heaviest snowfall and most rapid deepening of the cyclone coincided with the favorable positioning of an upper-level, short-wave trough immediately upstream of a preexisting surface cyclone. The surface cyclone in question formed approximately 15 h before the heaviest snowfall along a coastal front in a region of frontogenesis and heavy precipitation. The incipient surface cyclone subsequently intensified as it moved to the northeast, consistently generating the strongest convection to the east-northeast of the low-level circulation center. The use of potential vorticity (PV) inversion techniques and a suite of mesoscale model simulations illustrates that the early intensification of the incipient surface cyclone was primarily driven by diabatic effects and was not critically dependent on the upper-level wave. These facts, taken in conjunction with the observed structure, energetics, and Lagrangian evolution of the incipient surface disturbance, identify it as a diabatic Rossby vortex (DRV). The antecedent surface vorticity spinup associated with the DRV phase of development is found to be integral to the subsequent rapid growth. The qualitative similarity with a number of observed cases of explosive cyclogenesis leaves open the possibility that a DRV-like feature comprises the preexisting positive low-level PV anomaly in a number of cyclogenetic events that exhibit a two-stage evolution. [ABSTRACT FROM AUTHOR]

Droughts can be characterized by their severity, frequency and duration, and areal extent. Depth–area–duration analysis, widely used to characterize precipitation extremes, provides a basis for the evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. Gridded precipitation and temperature data were used to force a physically based macroscale hydrologic model at 1/2° spatial resolution over the continental United States, and construct a drought history from 1920 to 2003 based on the model-simulated soil moisture and runoff. A clustering algorithm was used to identify individual drought events and their spatial extent from monthly summaries of the simulated data. A series of severity–area–duration (SAD) curves were constructed to relate the area of each drought to its severity. An envelope of the most severe drought events in terms of their SAD characteristics was then constructed. The results show that (a) the droughts of the 1930s and 1950s were the most severe of the twentieth century for large areas; (b) the early 2000s drought in the western United States is among the most severe in the period of record, especially for small areas and short durations; (c) the most severe agricultural droughts were also among the most severe hydrologic droughts, however, the early 2000s western U.S. drought occupies a larger portion of the hydrologic drought envelope curve than does its agricultural companion; and (d) runoff tends to recover in response to precipitation more quickly than soil moisture, so the severity of hydrologic drought during the 1930s and 1950s was dampened by short wet spells, while the severity of the early 2000s drought remained high because of the relative absence of these short-term phenomena. [ABSTRACT FROM AUTHOR]

The fifth-generation PSU-NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the U.S. precipitation annual cycle is evaluated with a 1982–2002 continuous baseline integration driven by the NCEP-DOE second Atmospheric Model Intercomparison Project (AMIP II) reanalysis. The causes for major model biases (differences from observations) are studied through supplementary seasonal sensitivity experiments with various driving lateral boundary conditions (LBCs) and physics representations. It is demonstrated that the CMM5 has a pronounced rainfall downscaling skill, producing more realistic regional details and overall smaller biases than the driving global reanalysis. The precipitation simulation is most skillful in the Northwest, where orographic forcing dominates throughout the year; in the Midwest, where mesoscale convective complexes prevail in summer; and in the central Great Plains, where nocturnal low-level jet and rainfall peaks occur in summer. The actual model skill, however, is masked by existing large LBC uncertainties over datapoor areas, especially over oceans. For example, winter dry biases in the Gulf States likely result from LBC errors in the south and east buffer zones. On the other hand, several important regional biases are identified with model physics deficiencies. In particular, summer dry biases in the North American monsoon region and along the east coast of the United States can be largely rectified by replacing the Grell with the Kain—Fritsch cumulus scheme. The latter scheme, however, yields excessive rainfall in the Atlantic Ocean but large deficits over the Midwest. The fall dry biases over the lower Mississippi River basin, common to all existing global and regional models, remain unexplained and the search for their responsible physical mechanisms will be challenging. In addition, the representation of cloud-radiation interaction is essential in determining the precipitation distribution and regional water recycling, for which the new scheme implemented in the CMM5 yields significant improvement. [ABSTRACT FROM AUTHOR]

Intra- to multidecadal variation in annual streamflow, precipitation, and temperature over the continental United States are evaluated here through the calculation of Mann-Whitney U statistics over running-time windows of 6-30-yr duration. When this method is demonstrated on time series of nationally averaged annual precipitation and mean temperature during 1896-2001, it reveals that 8 of the 10 wettest years occurred during the last 29 yr of that 106-yr period, and 6 of the 10 warmest years during the last 16. Both of these results indicate highly significant departures from long-term stationarity in U.S. climate at the end of the twentieth century. The effects of increased wetness are primarily evident in the central and eastern United States, while evidence of warmth is found throughout the Rocky Mountain region and in the West. Analysis of annual streamflow records across the United States during 1939-98 shows broadly consistent effects. Initial evidence of the recent wet regime is most apparent in eastern streamflow, which shows a clear pattern of high-ranked mean annual values during the 1970s. Over the midwestern states, a coherent pattern of high-ranked annual flow is found during multidecadal periods beginning during the late 1960s and early 1970s and ending in either 1997 or 1998. During the late 1980s and early 1990s, a significant incidence of low-ranked annual flow conditions throughout the West was roughly coincident with the onset of western warmth during the mid-1980s. Evidence of highly significant transitions to wetter and warmer conditions nationally, and consistent variation in streamflow analyses, suggests that increased hydrological surplus in the central and eastern United States and increased hydrological deficit in the West may be representative of the initial stages of climate change over the continental United States. [ABSTRACT FROM AUTHOR]

Presents a study that reviewed the parameter-elevation regression on independent slopes model (PRISM), its Alaskan input data and its limitations. Patterns of variability in temperature and precipitation from PRISM modeled maps; Relation of the patterns to climactic processes; Alaskan vegetation ecosystem map based on a phenological classification from advanced very high resolution radiometer data.

Weather regimes are used to determine changes in the statistical distribution of winter precipitation and temperature at eight locations within the western United States. Six regimes are identified from daily 700-mb heights during 46 winters (1949--95) over the North Pacific sector applying cluster analysis; these include the Pacific-North American (PNA) pattern, reverse-PNA, a tropical-Northern Hemisphere (TNH) regime, and a Pacific ohm block. Most of the regimes have a statistically significant effect on the local median temperature, as well as daily temperature extremes; differences between locations are secondary to the large-scale effects. Local precipitation frequency is also conditioned significantly by certain weather regimes, but differences between groups of locations are larger. Precipitation extremes are dispersed and hard to classify. The dependence of local temperature statistics on the warm-or cold-air advection associated with particular weather regimes is discussed, as is the dependence of precipitation anomalies on the regimes' displaced storm tracks. The extent to which the El Nino-Southern Oscillation modulates the probability of occurrence of each of the six weather regimes is then investigated. Warm event (El Nino) winters are found to be associated with a significant increase in prevalence of a TNH regime, in which negative height anomalies exhibit a northwest-southeast tilt over the North Pacific. During La Nina winters, this TNH regime occurs significantly less frequently, while a regime characterized by a ridge over southwestern North America becomes more prevalent. These two regimes are associated with regional precipitation-frequency anomalies of opposite sign, that contribute to a north-south contrast in precipitation anomalies over the western United States during El Nino and La Nina winters. On interdecadal timescales, the frequency-of-occurrence of the PNA pattern is found to be notably higher during the 1970s and early 1980s. [ABSTRACT FROM AUTHOR]

The National Center for Atmospheric Research (NCAR) Land Surface Model (LSM, version 1.0) provides a comprehensive treatment of land surface processes for the NCAR Community Climate Model version 3 (CCM3). It replaces the prescribed surface wetness, prescribed snow cover, surface albedo, and surface flux parameterizations used in the CCM2. A 15-yr simulation of the coupled atmosphere (CCM3) and land (LSM1.0) models using observed sea surface temperatures for the period December 1978--September 1993 is used to document the model's land surface climate. The model simulates many of the observed geographic and seasonal patterns of surface air temperature, precipitation, and soil water. In general, the transition seasons (spring, autumn) are better simulated than winter and summer. Annual precipitation and runoff are well simulated for some river basins and poorly simulated for others. In general, precipitation is better simulated than runoff. The inclusion of net land-atmosphere CO2 exchange is an important component of the land model, allowing it to be used for studies of the global carbon cycle. The model simulates annual net primary production that is consistent with other estimates of annual production. The model also simulates a clearly defined growing season based on temperature and soil water. [ABSTRACT FROM AUTHOR]

The article focuses on the calculation on the eruption cycles of geysers by Shaul Hurwitz of the U.S. Geological Survey in Menlo Park, California, and his team. The team suggests that years with lots of rain or snow resulted in higher than usual pressures in the 200°C-plus underground reservoirs that feed geysers, which shortens geyser eruption cycles. Conversely, after snowmelt in spring, cold water sinks into geyser conduits and lowers their temperature, lengthening eruption intervals. It mentions that this work is based on data recorded between 1998 and 2006 by temperature sensors in geyser outflow channels.

Temperature and precipitation records from 1949 to 1998 were examined for 25 stations throughout the State of Alaska. Mean, maxima, and minima temperatures, diurnal temperature range, and total precipitation were analyzed for linear trends using least squares regressions. Annual and seasonal mean temperature increases were found throughout the entire state, and the majority were found to be statistically significant at the 95% level or better. The highest increases were found in winter in the Interior region (2.2 deg. C) for the 50 year period of record. Decreases in annual and seasonal mean diurnal temperature range were also found, of which only about half were statistically significant. A state-wide decrease in annual mean diurnal temperature range was found to be 0.3 deg. C, with substantially higher decreasesin the South/Southeastern region in winter. Increases were found in total precipitation for 3 of the 4 seasons throughout most of Alaska,while summer precipitation showed decreases at many stations. Few ofthe precipitation trends were found to be statistically significant,due to high interannual variability. Barrow, our only station in theArctic region, shows statistically significant decreases in annual and winter total precipitation. These findings are largely in agreement with existing literature, although they do contradict some of the precipitation trends predicted by the CO2-doubling GCM's. [ABSTRACT FROM AUTHOR]