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This paper investigates the sensitivity of golfers' performance to meteorological conditions at the men's US Masters tournament over the 40-year period 1980–2019. The mean and standard deviation of round scores are related to local temperature, humidity, wind speed and direction, and concurrent and antecedent precipitation. Mean scores are more dependent on weather conditions than the variability of scores in a given round. The best predictor of mean scores is the wet-bulb temperature in rounds one and two, and the zonal wind speed in rounds three and four. Across both sets of rounds (1 and 2, and 3 and 4), the wet-bulb temperature is a better predictor of mean scores than the air temperature, which implies that atmospheric moisture content affects scores. In general, golfers take fewer shots and so perform better in warmer and calmer conditions. The synergestic effect of several weather variables explains over 44% of the variance in mean scores. Mean meteorological conditions during play are a much better predictor of the players' average performance than the standard deviation of the weather variables. The golfers' performance becomes more variable in cooler conditions with a wider range of scores. Precipitation during play and the dampness of the ground (as quantified by rainfall up to ten days before play) do not have a consistent and statistically significant effect on the competitors' performance. In short, this paper demonstrates that golf scores are dependent on weather conditions. [ABSTRACT FROM AUTHOR]

Air-sea carbon dioxide (CO2) fluxes have recently been reported for the US South Atlantic Bight (SAB), a low-latitude shallow continental shelf bordered landward by abundant salt marshes and rivers. In this study, influences of terrestrial inputs on sea surface partial pressure of CO2 (pCO2) in the SAB are quantified. After re- moving pCO2 variations due to the annual temperature cycle and air-sea gas ex- change from the in situ pCO2, temperature and gas exchange corrected (TG-corrected) pCO2 is derived. Contributions from the river, marsh, and continental shelf to the TG-corrected pCO2 are then calculated. Findings show that CO2 contributions from the river [ΔpCO2 (river)] are highest close to the coast and decrease with distance off-shore. For example, ΔpCO2 (river) was up to 30 µatm on the inner shelf but averaged only 0--3 µatm on the outer shelf during January and March 2005. CO2 contributions from salt marshes [ΔpCO2 (marsh)] show similar spatial and seasonal distributions as ΔpCO2 (river), with the magnitude of ΔpCO2 (marsh) being about three times as high as that of ΔpCO2 (river). CO2 contributions from within the shelf [ΔpCO2 (shelf)] are highest among the three components. Compared to ΔpCO2 (river) and ΔpCO2 (marsh), ΔpCO2 (shelf) show the smallest onshore-offshore gradients. Despite large uncertainties, this study clearly demonstrates the importance of terrestrial impacts on coastal ocean CO2 distribution. [ABSTRACT FROM AUTHOR]

The Infrared Atmospheric Sounding Interferometer (IASI) is an ultra-spectral satellite sensor with 8461 spectral channels. IASI spectra contain high information content on atmospheric, cloud, and surface properties. The instrument presents a challenge for using thousands of spectral channels in a physical retrieval system or in a Numerical Weather Prediction (NWP) data assimilation system. In this paper we describe a method of simultaneously retrieving atmospheric temperature, moisture, and cloud properties using all available IASI channels without sacrificing computational speed. The essence of the method is to convert the IASI channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Studies show that about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in the super channel domain directly therefore both reducing the computational need and preserving the information content of the IASI measurements. While no large-scale validation has been performed on any retrieval methodology presented in this paper, comparisons of the retrieved atmospheric profiles, sea surface temperatures, and surface emissivities with co-located ground- and aircraft-based measurements over four days in Spring 2007 over the South-Central United States indicate excellent agreement. [ABSTRACT FROM AUTHOR]

This paper explored the potential of a global climate model for sub-seasonal forecasting of precipitation and 2m air temperature. The categorical forecast skill of 10 precipitation and temperature indices was investigated using the 28-year sub-seasonal hindcasts from the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill for mean precipitation and temperature as well as for the frequency and duration of extremes was highly dependent on the forecasting indices, regions, seasons, and leads. Forecasts for 7- and 14-day temperature indices showed skill even at weeks 3 and 4, and generally were more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region at sub-seasonal scale. While the forecast skill related to mean precipitations was low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and number of consecutive dry days showed considerably high skill for the western coastal region. The presence of active Madden--Julian Oscillation (MJO) events improved CFSv2 weekly mean precipitation forecast skill over most parts of the CONUS, but it did not necessarily improve the weekly mean temperature forecasts. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts were less skillful than those calculated directly from CFSv2 daily forecasts, suggesting the usefulness of CFSv2 for sub-seasonal hydrological forecasting. [ABSTRACT FROM AUTHOR]

In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference Network (USCRN) stations was used as a reference for rural station selection; stations with similar environmental conditions in the U.S. Historical Climatology Network (USHCN) were selected as reference stations using a machine learning method, and then the maximum surface air temperature (Tmax) series, minimum surface air temperature (Tmin) series and mean surface air temperature (Tmean) series of rural stations during 1921–2020 were compared with those for all nearby stations (including both rural and urban stations) to evaluate urbanization effects in the USHCN observation data series of the contiguous United States, which can be regarded as urbanization bias contained in the latest homogenized USHCN observation data. The results showed that the urbanization effect on the Tmean trend of USHCN stations is 0.002 °C dec−1, and the urbanization contribution is 35%, indicating that urbanization around USHCN stations has led to at least one-third of the overall warming recorded at USHCN stations over the last one hundred years. The urbanization effects on Tmax and Tmin trends of USHCN stations are −0.015 °C dec−1 and 0.013 °C dec−1, respectively, and the urbanization contribution for Tmin is 34%. These results have significance for understanding the systematic bias in USHCN temperature data, and they provide a reference for subsequent studies on data correction and climate change monitoring. [ABSTRACT FROM AUTHOR]

High air temperatures are a public health threat, causing 1300 deaths annually in the United States (US) along with heat-related morbidity and increased electricity consumption for air-conditioning (AC). Increasing tree canopy cover has been proposed as one way to reduce urban air temperatures. Here, we assemble tree cover and developed land-cover information for 97 US cities, housing 59 million people, and use regression relationships to analyze how much current urban tree cover reduces summer (JJA) air temperatures and associated heat-related mortality, morbidity, and electricity consumption. We find that 78% of urban dwellers are in neighborhoods with less than 20% tree cover. Some 15.0 million people (25% of total) experience a reduction of 0.5–1.0°C from tree cover, with another 7.9 million (13% of total) experiencing a reduction of greater than 1.0°C. Current relationships between temperature and health outcomes imply that urban tree cover helps avoid 245–346 deaths annually. Heat–mortality relationships in the 1980s, when a smaller fraction of US households had AC, imply a greater role in the past for urban tree cover in avoiding heat-related mortality. As AC availability has increased, the value of tree cover for avoiding heat-related mortality has decreased, while the value of tree cover for reducing electricity consumption likely has increased. Currently, for the 97 cities studied, the total annual economic value of avoided mortality, morbidity, and electricity consumption is an estimated $1.3–2.9 billion, or $21–49 annually per capita. Applying our results to the entire US urban population, we estimate urban tree cover annually supplies heat-reduction services worth $5.3–12.1 billion. [ABSTRACT FROM AUTHOR]

Prior research evaluating snowfall conditions and temporal trends in the United States often acknowledges the role of various synoptic-scale weather systems in governing snowfall variability. While synoptic classifications have been performed in other regions of North America in applications to snowfall, there remains a need for enhanced understanding of the atmospheric mechanisms of snowfall in the central United States. Here we conduct a novel synoptic climatological investigation of the weather systems responsible for snowfall in the central United States from 1948 to 2021 focused on their identification and the quantification of associated snowfall totals and events. Ten unique synoptic weather types (SWTs) were identified, each resulting in distinct regions of enhanced snowfall across the study domain aligning with regions of sufficiently cold air temperatures and forcing mechanisms. While a substantial proportion of seasonal snowfall is attributed to SWTs associated with surface troughs and/or midlatitude cyclones, in portions of the southeastern and western study domain, as much as 70% of seasonal snowfall occurs during systems with high pressure centers as the domain's synoptic-scale forcing. Easterly flow, potentially resulting in topographic uplift from high pressure to east of the domain, was associated with between 15% and 25%of seasonal snowfall in Nebraska and South Dakota. On average, 64.8% of the SWT occurrences resulted in snowfall within the study region, ranging between 40.1%and 93.5%by SWT. Synoptic climatological investigations provide valuable insights into the unique weather systems that generate hydroclimatic variability. SIGNIFICANCE STATEMENT: By evaluating the weather patterns that are responsible for snowfall in the central United States, key insights can be gained into how and why snowfall varies and potentially changes over space and time. Using an approach that categorizes weather patterns based on their similarities, here 10 unique snowfall-producing weather patterns are identified and analyzed from 1948 to 2021. Each pattern resulted in different snowfall amounts across the central United States, varying substantially spatially and within the calendar year. Approximately 65% of the time that these weather patterns occur, snowfall is observed in the region. The majority of snowfall-producing weather patterns are associated with low pressure systems, but in some regions up to 70% of snowfall is associated with instances of high pressure in which winds can cause upward motions associated with topography. [ABSTRACT FROM AUTHOR]

Model output statistics (MOS) guidance forecasts have been produced at stations and provided to National Weather Service forecasters and private entities for over three decades. As the numerical weather prediction models became more accurate, MOS followed that trend. Up until a few years ago, the MOS produced at observation locations met the basic need for guidance. With the advent of the Interactive Forecast Preparation System and the National Digital Forecast Database, gridded MOS forecasts became needed as guidance for forecasters. One method of providing such grids is to objectively analyze the MOS forecasts for points. A basic successive correction method has been extended to analyze MOS forecasts and surface weather variables. This method is being applied to MOS forecasts to provide guidance for producing grids of sensible weather elements such as temperature, clouds, and snow amount. Guidance forecasts have been implemented for the conterminous United States for most weather elements contained in routine weather forecasts. This paper describes the method applied to daytime maximum temperature over the conterminous United States and gives example results. [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]

This paper presents and evaluates a method for the construction of long-range and wide-area temporal spatial datasets of daily precipitation and temperature (maximum and minimum). This method combines the interpolation of daily ratios/fractions derived from ground-based meteorological station records and respective fields of monthly estimates. Data sources for the described implementation over the conterminous United States (CONUS) are two independent and quality-controlled inputs: 1) an enhanced compilation of daily observations derived from the National Climatic Data Center digital archives and 2) the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) maps. The results of this study show that this nonconventional interpolation preserves the spatial and temporal distribution of both the PRISM maps (monthly, topography-sensitive patterns) and the original daily observations. Statistics of a preliminary point comparison with the observed values at high-quality and independent reference sites show a reasonable agreement and a noticeable improvement over the nearest station method in orographically sensitive areas. The implemented datasets provide daily precipitation and temperature values at 2.5-min (around 4 km) resolution for 1960–2001. Combining seamless spatial and temporal coverage and topographic sensitivity characteristics, the datasets offer the potential for supporting current and future regional and historical hydrologic assessments over the CONUS. [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]

ABSTRACT In light of numerous studies documenting a decline in the diurnal temperature range (DTR) over much of the globe, some authors have in recent years examined the annual march of the DTR in an effort to understand better the factors that influence the DTR's seasonal variations. These papers show that, over the southern two-thirds of the eastern United States, the DTR's climatology features peaks in spring and autumn and minima in winter and mid-to-late summer. However, the factors responsible for these characteristics remain uncertain. In this study, the DTR climatology of the eastern United States is analyzed in detail using daily surface and 850-mb data, with emphasis on possible relationships to seasonal changes in vegetation. It is shown that the warm season dip in the DTR deepens and widens from north to south across the study area, in accordance with a lengthening of the growing season. Furthermore, the dip is particularly prominent in the annual march of the DTR on mostly sunny days, indicating that seasonal variations in cloudiness are not responsible for this feature. The climatologies of daily maximum and daily minimum temperatures are found to be very different from each other: the former flattens out after the springtime peak in the DTR whereas the latter exhibits a pronounced mid-to-late summer maximum. These findings suggest that, by inhibiting daytime surface heating, evapotranspiration from vegetation contributes significantly to the dip in the DTR during the warm season in the eastern United States. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the dependence of summertime daily maximum temperature on antecedent soil moisture using daily surface observations from a selection of stations in the contiguous United States and daily time series of soil moisture computed with a simple local water balance model. The computed soil moisture time series are offered as an alternative to Palmer's soil moisture anomaly (Z) index, the Palmer Drought Severity Index (PDSI), and other such time series. In contrast to other water balance models that have been designed for the computation of soil moisture time series, the model herein is driven by daily rather than monthly data, uses the Priestley-Taylor method in lieu of Thornthwaite's method to calculate potential evapotranspiration, allows for runoff during dry periods as well as when soil moisture is not at field capacity, includes a crude scheme for taking into account the effects of snowmelt on the water balance, and permits geographical variations in soil water capacity. The Priestley-Taylor method is considered to yield more realistic estimates of evapo-transpiration than Thornthwaite's method since it accounts for net radiation and represents a special case of the widely used Penman-Monteith method. Total runoff is parameterized according to the Variable Infiltration Capacity model. Based on a comparison with soil moisture measurements at Peoria, Illinois, the model appears to simulate the variability of soil moisture anomalies (W[sup']) reasonably well. Analysis of the relationship between W[sup'] and daily maximum temperatures (T[submax]) shows that in the central and eastern United States during the summer, the entire frequency distribution of standardized T[submax] is shifted toward higher values following a 'low-W[sup']' day (i.e., a day on which W[sup'] falls into the bottom quartile of its frequency distribution). The shift is most pronounced at the high end of the temperature distribution, indicating that as the... [ABSTRACT FROM AUTHOR]

Summary: Drylands of the southwestern United States are rapidly warming, and rainfall is becoming less frequent and more intense, with major yet poorly understood implications for ecosystem structure and function. Thermography‐based estimates of plant temperature can be integrated with air temperature to infer changes in plant physiology and response to climate change. However, very few studies have evaluated plant temperature dynamics at high spatiotemporal resolution in rainfall pulse‐driven dryland ecosystems.We address this gap by incorporating high‐frequency thermal imaging into a field‐based precipitation manipulation experiment in a semi‐arid grassland to investigate the impacts of rainfall temporal repackaging.All other factors held constant, we found that fewer/larger precipitation events led to cooler plant temperatures (1.4°C) compared to that of many/smaller precipitation events. Perennials, in particular, were 2.5°C cooler than annuals under the fewest/largest treatment.We show these patterns were driven by: increased and consistent soil moisture availability in the deeper soil layers in the fewest/largest treatment; and deeper roots of perennials providing access to deeper plant available water. Our findings highlight the potential for high spatiotemporal resolution thermography to quantify the differential sensitivity of plant functional groups to soil water availability. Detecting these sensitivities is vital to understanding the ecohydrological implications of hydroclimate change. [ABSTRACT FROM AUTHOR]

Extreme climate events are societally harmful and have increased in frequency and intensity in recent decades. Indices based on temperature and precipitation are a valuable way to quantify climate extremes. Certain indices are defined relative to percentiles, which are dependent on a climatological baseline period. In this study, indices computed using temperature and precipitation from the Modern Era Retrospective Analysis for Research and Applications, Version 2 are calculated using percentiles from three baseline periods: 1981–2010, 1991–2020 and 1981–2020. Updating the baseline period from 1981 to 2010 to 1991–2020 leads to significant changes in the quantification of temperature and precipitation extremes over the United States over 1980–2021. Using the later baseline period indicates more cold extremes, fewer warm extremes, and fewer but more intense precipitation extremes throughout the US, with regional variation. Changing the baseline period can mislead the public and decision makers, potentially undermining the appropriate response to climate‐related health risks. Plain Language Summary: Indices computed using 2‐m air temperature and precipitation are used to represent extreme climate events such as heat waves, cold waves, heavy precipitation, and drought. Some indices are defined relative to percentile‐based thresholds, which are computed using a baseline climatology period. The baseline climatology is typically a thirty‐year period and is updated every ten years. This study examines how updating the baseline climatology period from 1981–2010 to 1991–2020 affects the quantification of climate extremes in the United States over 1980–2021. In general, since the 1991–2020 period is warmer than 1981–2010 throughout the United States, there are fewer warm extremes detected and more cold extremes detected when it is used as the baseline. The differences are most notable in the southwest and northeast United States. The changes in the precipitation indices vary throughout the country, but in certain parts of the southern and central United States, updating the baseline period leads to the detection of fewer but heavier extreme precipitation events. It is important to communicate the choice of baseline climatology period to prevent misinterpretation of the extreme climate indices and the comparison of different studies. Key Points: Updating the baseline period from 1981–2010 to 1991–2020 leads to significant changes in percentile‐based extreme climate indices in the USTemperature indices show generally increased cold extremes and decreased warm extremes across the US when the baseline period is updatedFor precipitation indices, the later baseline period indicates fewer but more intense extreme events in the south and central US [ABSTRACT FROM AUTHOR]

Aquatic invasive species (AIS) present major ecological and economic challenges globally, endangering ecosystems and human livelihoods. Managers and policy makers thus need tools to predict invasion risk and prioritize species and areas of concern, and they often use native range climate matching to determine whether a species could persist in a new location. However, climate matching for AIS often relies on air temperature rather than water temperature due to a lack of global water temperature data layers, and predictive power of models is seldom evaluated. We developed 12 global lake (water) temperature‐derived "BioLake" bioclimatic layers for distribution modeling of aquatic species and compared "climatch" climate matching predictions (from climatchR package) from BioLake with those based on BioClim temperature layers and with a null model. We did this for 73 established AIS in the United States, training the models on their ranges outside of the United States and Canada. Models using either set of climate layers outperformed the null expectation by a similar (but modest) amount on average, but some species were occasionally found in locations with low climatch scores. Mean US climatch scores were higher for most species when using air temperature. Including additional climate layers in models reduced mean climatch scores, indicating that commonly used climatch score thresholds are not absolute but can be context specific and may require calibration based upon climate data used. Although finer resolution global lake temperature data would likely improve predictions, our BioLake layers provide a starting point for aquatic species distribution modeling. Climate matching was most effective for some species that originated at low latitudes or had small ranges. Climatch scores remain useful but limited for predicting AIS risk, perhaps because current ranges seldom fully reflect climatic tolerances (fundamental niches). Managers could consider climate matching as one of a suite of tools that can be used in AIS prioritization. [ABSTRACT FROM AUTHOR]

Streams and rivers are major sources of greenhouse gases (GHGs) to the atmosphere, as carbon and nitrogen are converted and outgassed during transport. Although our understanding of drivers of individual GHG fluxes has improved with numerous site‐specific studies and global‐scale compilations, our ability to parse out interrelated physical and biogeochemical drivers of gas concentrations is limited by a lack of consistently collected, temporally continuous samples of GHGs and their associated drivers. We present a first analysis of such a dataset collected by the National Ecological Observatory Network across 27 streams and rivers across ecoclimatic domains of the United States. Average concentrations of CO2 ranged from 36.9 ± 0.88 to 404 ± 33 μmol L−1, CH4 from 0.003 ± 0.0003 to 4.99 ± 0.72 μmol L−1, and N2O from 0.015 to 0.04 μmol L−1 and spanned ranges of previous global compilations. Both CO2 and CH4 were strongly affected by physical drivers including mean air temperature and stream slope, as well as by dissolved oxygen and total nitrogen concentrations. N2O was exclusively correlated with total nitrogen concentrations. Results suggested that potential for gas exchange dominated patterns in gas concentrations at the site level, but contributions of in‐stream aerobic and anaerobic metabolism, and groundwater also likely varied across sites. The highest gas concentrations as well as highest variability occurred in low‐gradient, warmer, and nonperennial systems. These results are a first step in providing unprecedented, continuous estimates of GHG flux constrained by temporally variable physical and biogeochemical drivers of GHG production. [ABSTRACT FROM AUTHOR]

Microclimatic data are required for many problems in pure and applied ecology. This data includes aboveground convective and radiative conditions as well as soil temperature and moisture. In cold regions, the connection between above‐ and belowground microclimates via snow cover is also critically important. Here I describe a data set of hourly microclimates for the continental United States, simulated from the years 1979 to 2017 across a grid of 2,287 locations approximately 60 km apart. The data were generated with the NicheMapR microclimate model, driven by 0.04° gridded daily meteorological forcing data (air temperature, wind speed, humidity, solar radiation, air pressure, and rainfall). The aboveground microclimate variables include horizontal plane solar radiation, solar zenith angle, sky temperature (from which downwelling longwave radiation can be computed), air temperature, relative humidity, and wind speed at 1 and 200 cm height, and snow depth. The belowground variables include soil temperature, pore humidity, soil moisture, and soil water potential for 0, 2.5, 5, 10, 15, 20, 30, 50, 100, and 200 cm belowground. The computations are for four shade levels (0%, 50%, 75%, and 90%). The predictions are validated against detailed soil temperature, soil moisture, and snow observations and show enhanced performance over existing microclimatic data for the United States. The data set can be used for a wide variety of applications, including the computation of heat and water budgets of organisms, the potential for vegetation growth, and the computation of stress and growth indices. The use of daily forcing data also allows assessments of the consequences of extreme events including heat waves and drought. Example applications are provided for computing plant growth potential, lizard egg development and body temperature, and mammalian energy and water requirements. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper. [ABSTRACT FROM AUTHOR]

We investigate the time trends in the maximum and minimum temperatures in the United States from 1895 to 2017 using techniques that allow for fractional integration in the detrended series. In doing so we get more accurate estimates of the trends than those obtained using standard methods that impose either stationarity I(0) or nonstationarity I(1). Our results reveal evidence of significant positive trends in both maximum and minimum temperatures, while the difference between them show a significant negative trend as a consequence of the higher increase in the minimum temperatures. Evidence of stationary long memory behavior is also found in the three series examined. [ABSTRACT FROM AUTHOR]

In this study, we investigate both the changes of tropical cyclone (TC) rainfall hazard in the United States under climate change and the relative importance of the factors that cause the changes. We find that under the SSP5 8.5 scenario, the 100‐year TC rainfall level can increase by up to 320% along the U.S. coastline by the end of this century. The influence of TC rainfall‐producing ability increase is more significant than the influence of TC frequency increase on the increase of the 100‐year TC rainfall level (up to 180% vs. 60% increase). Among the different physical drivers for the increase in storm rainfall‐producing ability, the increase of TC intensity is the leading factor, followed by changes in TC duration and atmospheric temperature. The projected increase of TC rainfall hazard is robust against the uncertainty in the TC frequency projection. Plain Language Summary: Rainfall associated with landfalling tropical cyclones (TC) can cause extreme flooding and landslides, which are hazardous for coastal and inland areas. We project that by the end of the 21st century, TC rainfall hazard in the United States will increase by as much as 320% compared to its current value. The increase in TC rainfall hazard can be caused by two factors: more TCs making impact and TCs becoming more capable of producing rainfall in the future. The increased ability for TCs to produce rainfall is linked to increased TC intensity and duration as well as a warmer and more humid atmosphere in the future. Though uncertainties exist in the projected trend of future TC landfall frequency, TC rainfall hazard is very likely to increase due to the significant increase in TC rainfall‐producing ability. Key Points: U.S. landfalling tropical cyclone (TC) rainfall hazard will increase in the future due to increased TC landfalling frequency and TC rainfall‐producing abilityStorm intensity change is the leading factor for TC rainfall‐producing ability to increaseThe projected increase of TC rainfall hazard is robust against the uncertainty in TC frequency projection [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are narrow bands of enhanced integrated water vapor transport, modulated by large-scale and synoptic-scale variability. Here, we investigate how ARs and snowpack are shaped by large-scale variability such as arctic oscillation (AO) by examining the synoptic conditions and characteristics of ARs and snowpack in the different phases of AO. Using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data, Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) reanalysis data, and in-situ observation data over the eastern Pacific and western United States. we found that more precipitation is observed in lower latitudes (35° N–45° N) during negative AO months and farther north (north of 45° N) in latitude during positive AO months. These are associated with wavelike synoptic patterns in negative AO months and more straight-line type synoptic patterns in positive AO months. The different phases of AO also modulate the AR characteristics: 2.6% less intense (5.3% more intense) integrated water vapor transport and total precipitation, and 16.0% shorter (21.1% longer) duration of ARs than the climatological mean (1980–2019) for positive AO (negative AO) phase. AR frequency is also higher (~50.4%) than the climatological mean for negative AO phase, but there is no statistically significant difference between either negative AO or positive AO phase, especially in southern California. In addition, the snow water equivalent (SWE) tends to be reduced in the positive AO phase and under high-temperature conditions, especially in recent years (2010s). The similar relationships are found in the early 1990s and 2000s, but their statistical significances are low. Considering that lower atmospheric temperature keeps increasing over the eastern Pacific and the western U.S., and SWE tends to be reduced in the positive AO phase in recent years, SWE may decrease over northern California if the warming condition persists. These findings highlight how the characteristics of local extreme weather can be shaped by large-scale climate variability. [ABSTRACT FROM AUTHOR]

The formula used in the U.S. and Canada to express the combined effect of wind and low temperature on how cold it feels was changed in November 2001. Many had felt that the old formula for equivalent temperature, derived in the 1960s from Siple and Passel's flawed but quite useful Wind Chill Index, unnecessarily exaggerated the severity of the weather. The new formula is based on a mathematical model of heat flow from the upwind side of a head-sized cylinder moving at walking speed into the wind. The paper details the assumptions that were made in generating the new wind chill charts. It also points out weaknesses in the concept of wind chill equivalent temperature, including its steady-state character and a seemingly paradoxical effect of the internal thermal resistance of the cylinder on comfort and equivalent temperature. Some improvements and alternatives are suggested. [ABSTRACT FROM AUTHOR]

Soil moisture (W) helps control evapotranspiration (ET), and ET variations can in turn have a distinct impact on 2-m air temperature (T2M), given that increases in evaporative cooling encourage reduced temperatures. Soil moisture is accordingly linked to T2M, and realistic soil moisture initialization has, in previous studies, been shown to improve the skill of subseasonal T2M forecasts. The relationship between soil moisture and evapotranspiration, however, is distinctly nonlinear, with ET tending to increase with soil moisture in drier conditions and to be insensitive to soil moisture variations in wetter conditions. Here, through an extensive analysis of subseasonal forecasts produced with a state-of-the-art seasonal forecast system, this nonlinearity is shown to imprint itself on T2M forecast error in the conterminous United States in two unique ways: (i) the T2M forecast bias (relative to independent observations) induced by a negative precipitation bias tends to be larger for dry initializations, and (ii) on average, the unbiased root-mean-square error (ubRMSE) tends to be larger for dry initializations. Such findings can aid in the identification of forecasts of opportunity; taken a step further, they suggest a pathway for improving bias correction and uncertainty estimation in subseasonal T2M forecasts by conditioning each on initial soil moisture state. Significance Statement: Not all forecasts are created equal. Even before a given forecast is produced, the nature of its initial conditions may indicate that it will prove more accurate than corresponding forecasts started at other times. We address here how the character of the soil moisture at the beginning of a forecast may provide such information. We find that under certain conditions, when the initial state of the soil is wet, the bias in the forecast is reduced and, to a lesser extent, the random error in the forecast is also reduced. Knowing ahead of time when to put more trust into a forecast should be of substantial benefit to forecast end-users. [ABSTRACT FROM AUTHOR]

The article invites papers which discuss how ecologists should respond to the concept of environmental change in the U.S. The papers, which will be published in "Ecological Applications," should be a good example of how ecologists are viewing a changing world. Papers should use the archaeological and evolutionary information from the past to develop an informed guide to the future. In addition, papers using climatic and biological scenarios are also welcome given the fact that the publication will be publishing papers dealing with the changing climate, nitrogen cycle, and patters of development in the coming year.

Increased heat-wave frequency across the United States has led to the need for improved predictability of heat-wave events. A detailed understanding of land–atmosphere interactions and the relationship between soil moisture and temperature extremes could provide useful information for prediction. This study identifies, for many locations, a threshold of soil moisture below which there is an increase in the sensitivity of atmospheric temperature to declining soil moisture. This shift to a hypersensitive regime causes the atmosphere to be more susceptible to atmospherically driven heat-wave conditions. The soil moisture breakpoint where the regime shift occurs is estimated using segmented regression applied to observations and reanalysis data. It is shown that as the soil gets drier, there is a concomitant change in the rate of decrease in latent heat flux and increase in sensible heat flux leading to a strong positive feedback of increased air temperature near the surface, which further dries out the soil. Central, southwestern, and southeastern parts of the United States seem to have regions of clear regime shifts, while the eastern part of the United States generally does not get dry enough to reveal significant breakpoints. Sensible heat flux is seen to be a primary driver of this increased temperature sensitivity aided by the drop in latent heat flux. An investigation of flux tower sites verifies the breakpoint–flux relationships found in reanalysis data. Accurate estimation of these breakpoints can contribute to improved heat-wave prediction. [ABSTRACT FROM AUTHOR]

Climate change is causing rapid warming and altered precipitation patterns in mountain watersheds, both of which influence the timing of ice breakup in mountain lakes. To enable predictions of ice breakup in the future, we analyzed a dataset of mountain lake ice breakup dates derived from remote sensing and historical downscaled climate data. We evaluated drivers of ice breakup, constructed a predictive statistical model, and developed projections of mountain lake ice breakup date with global climate models. Using Random Forest analysis, we determined that winter and spring cumulative snow fraction (portion of precipitation falling as snow) and air temperature are the strongest predictors of ice breakup on mountain lakes. Interactions between precipitation, cumulative winter air temperature and lake surface area indicate that shifts in air temperature and precipitation affect smaller lakes (< 2 km²) more than larger lakes (> 2-10 km²). A linear mixed effects model (RMSE of 18 d), applied with an ensemble of 15 global climate models, projected that end-of-century ice breakup in mountain lakes will be earlier by 25 ± 4 and 61 ± 5 (mean ± SE) days for representative concentration pathways 4.5 and 8.5, respectively. [ABSTRACT FROM AUTHOR]

The term "flash drought" is frequently invoked to describe droughts that develop rapidly over a relatively short timescale. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. Instead, diverse definitions have been proposed, supporting wide-ranging studies of flash drought but creating the potential for confusion as to what the term means and how to characterize it. Use of different definitions might also lead to different conclusions regarding flash drought frequency, predictability, and trends under climate change. In this study, we compared five previously published definitions, a newly proposed definition, and an operational satellite-based drought monitoring product to clarify conceptual differences and to investigate the sensitivity of flash drought inventories and trends to the choice of definition. Our analyses indicate that the newly introduced Soil Moisture Volatility Index definition effectively captures flash drought onset in both humid and semi-arid regions. Analyses also showed that estimates of flash drought frequency, spatial distribution, and seasonality vary across the contiguous United States depending upon which definition is used. Definitions differ in their representation of some of the largest and most widely studied flash droughts of recent years. Trend analysis indicates that definitions that include air temperature show significant increases in flash droughts over the past 40 years, but few trends are evident for definitions based on other surface conditions or fluxes. These results indicate that "flash drought" is a composite term that includes several types of events and that clarity in definition is critical when monitoring, forecasting, or projecting the drought phenomenon. [ABSTRACT FROM AUTHOR]

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the −15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models. [ABSTRACT FROM AUTHOR]

The frequency of heat waves (defined as daily temperature exceeding the local 90th percentile for at least three consecutive days) during summer in the United States is examined for daily maximum and minimum temperature and maximum apparent temperature, in recent observations and in 10 CMIP5 models for recent past and future. The annual average percentage of days participating in a heat wave varied between approximately 2% and 10% in observations and in the model's historical simulations during 1979-2005. Applying today's temperature thresholds to future projections, heatwave frequencies rise to more than 20% by 2035-40. However, given the models' slight overestimation of frequencies and positive trend rates during 1979-2005, these projected heat-wave frequencies should be regarded cautiously. The models' overestimations may be associated with their higher daily autocorrelation than is found in observations. Heat-wave frequencies defined using apparent temperature, reflecting both temperature and atmospheric moisture, are projected to increase at a slightly (and statistically significantly) faster rate than for temperature alone. Analyses show little or no changes in the day-to-day variability or persistence (autocorrelation) of extreme temperature between recent past and future, indicating that the future heat-wave frequency will be due predominantly to increases in standardized (using historical period statistics) mean temperature and moisture content, adjusted by the local climatological daily autocorrelation. Using nonparametric methods, the average level and spatial pattern of future heat-wave frequency is shown to be approximately predictable on the basis of only projected mean temperature increases and local autocorrelation. These model-projected changes, even if only approximate, would impact infrastructure, ecology, and human well-being. [ABSTRACT FROM AUTHOR]

Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool for quantifying the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the examples of global mean near-surface air temperature, local temperature and precipitation, and variability in the El Niño–Southern Oscillation (ENSO) region and central United States for the Max Planck Institute Grand Ensemble (MPI-GE). Estimating the required ensemble size is relevant not only for designing or choosing a large ensemble but also for designing targeted sensitivity experiments with a model. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. We show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity. [ABSTRACT FROM AUTHOR]

Monthly tropical sea surface temperature (SST) data are used as predictors to make statistical forecasts of cold season (November–March) precipitation and temperature for the contiguous United States. Through the use of the combined-lead sea surface temperature (CLSST) model, predictive information is discovered not just in recent SSTs but also from SSTs up to 18 months prior. We find that CLSST cold season forecast anomaly correlation skill is higher than that of the North American Multimodel Ensemble (NMME) and the SEAS5 model from the European Centre for Medium-Range Weather Forecasts (ECMWF) when averaged over the United States for both precipitation and 2-m air temperature. The precipitation forecast skill obtained by CLSST in parts of the Intermountain West is of particular interest because of its implications for water resources. In those regions, CLSST dramatically improves the skill over that of the dynamical model ensembles, which can be attributed to a robust statistical response of precipitation in this region to SST anomalies from the previous year in the tropical Pacific. [ABSTRACT FROM AUTHOR]

Hydrologic intensity is often quantified using precipitation without directly incorporating atmospheric water demand. We develop a hydrologic intensity index called the surplus deficit intensity (SDI) index that accounts for variation in supply and demand. SDI is the standardized sum of standardized surplus intensity (mean of daily surplus when supply > demand) and deficit time (mean of consecutive days when demand > supply). Using an observational ensemble of global daily precipitation and atmospheric water demand during 1979–2017, we document widespread hydrologic intensification (SDI; +0.11 z‐score per decade) driven primarily by increased surplus intensity. Using a climate model ensemble of the United States, hydrologic intensification is projected for the mid‐21st century (+0.86 in z‐score compared to 1971–2000), producing greater apparent intensification when compared to an index that does not explicitly incorporate demand. While incorporating demand had a minor effect on observed hydrologic intensification, it doubles hydrological intensification for the mid‐21st century. Plain Language Summary: Increasing air temperatures have resulted in an intensification, or acceleration, of the terrestrial hydrologic cycle, which is defined as an increase of the water fluxes (precipitation and evapotranspiration) between the surface and the atmosphere. Efforts to quantify hydrologic intensification have traditionally only considered "supply" variables such as precipitation rates without considering "demand" that varies over space and time. For this work we develop a method to quantify hydrologic intensification using supply and demand. This approach shows widespread hydrologic intensification from 1979–2017 across much of the global land surface, which is expected to continue into the future. We additionally compare this new method to a previously developed method that uses only supply without explicitly incorporating demand. Incorporating demand results in a notable hydrologic intensification compared to methods that only use supply or a simplified representation of supply and demand. This work suggests that demand needs to be considered when quantifying hydrologic intensification. Key Points: We develop a hydrologic intensity metric that incorporates daily supply and demand using precipitation and reference evapotranspirationWidespread hydrologic intensification occurred over the past four decades and will continue into the futureAdding demand couples intensity metrics to ecohydrologic conditions and produces greater increases than metrics with static demand [ABSTRACT FROM AUTHOR]

The optimal ranking regime (ORR) method was applied to mean summer maximum (TMXS) and mean summer minimum (TMNS) temperature and to cumulative summer cooling degree-days (CDDS) calculated from U.S. climate-division data during 1895-2015. CDDS is proposed as a proxy for growing degree-days for summer corn given their high rank correlation in station data during 1950-2014. The TMXS and CDDS ORR analyses show similar climate-regime patterns. Western and northeastern divisions experienced multidecadal cool periods before 1930 and warm periods after 1990. The 1930s drought appears as decadal warm regimes over the Midwest and Great Plains. Multidecadal TMXS and CDDS temperature cycles are evident over the Southeast, but TMXS and CDDS variation over the Midwest's Corn Belt agricultural region has been regime free since the early 1940s. By contrast, TMNS regimes consistent with centennial-scale warming trends are found over most divisions outside the Southeast. From the multidecadal regime patterns detected by the ORR analyses, the TMXS, TMNS, and CDDS series of each climate division were tested for significant linear trends during 1910-2015 and 1970-2015. Significant positive TMNS trends during 1910-2015 are found in 48 of the 102 divisions, with some western trend magnitudes being greater than 15% of the twentieth-century climatological mean. During 1970-2015, positive TMXS trends are detected over 39 western and northeastern divisions, but warming TMNS trends are evident nationally. In some cooler western divisions, positive 1970-2015 CDDS trend magnitudes exceed 90% of the climatological mean. Consistent with the ORR analyses, Corn Belt TMXS and CDDS trends are insignificant during 1970-2015. [ABSTRACT FROM AUTHOR]

The goal of this study is to reframe the analysis and discussion of extreme heat projections to improve communication of future extreme heat risks in the United States. We combine existing data from 31 of the Coupled Model Intercomparison Project Phase 5 models to examine future exposure to extreme heat for global average temperatures of 1.5, 2, 3, and 4 °C above a preindustrial baseline. We find that throughout the United States, historically rare extreme heat events become increasingly common in the future as global temperatures rise and that the depiction of exposure depends in large part on whether extreme heat is defined by absolute or relative metrics. For example, for a 4 °C global temperature rise, parts of the country may never see summertime temperatures in excess of 100 °F, but virtually all of the country is projected to experience more than 4 weeks per summer with temperatures exceeding their historical summertime maximum. All of the extreme temperature metrics we explored become more severe with increasing global average temperatures. However, a moderate climate scenario delays the impacts projected for a 3 °C world by almost a generation relative to the higher scenario and prevents the most extreme impacts projected for a 4 °C world. Plain Language Summary: Extreme heat events or heat waves are public health threats and can also cause damages or disruptions to infrastructure, transportation, agriculture, and water or energy resources. Extreme heat events are already more frequent and more intense than they were a generation ago, and they are expected to become even more frequent and more intense as global average temperatures continue to increase. The way most climate scientists describe future changes in the risk of extreme temperatures can be confusing to nontechnical audiences, who may find it challenging to relate to the projections. In addition, the metric most relevant to what any one person, location, or sector cares about can vary. For example, an urban planner may be concerned about surpassing the temperature threshold at which asphalt softens or rails warp, while a water manager may want to track the number of consecutive days in a heat wave to prepare for changes in water supply, and a public health official may care about high minimum (usually elevated nighttime) temperatures to plan appropriate health adaptation responses. To help characterize risk of extreme heat events to multiple audiences, this analysis uses notable heat waves from the recent past as a benchmark and shows how the frequency and severity of events like these are projected to change in the future for specific levels of global warming (1.5, 2, 3, or 4 °C) above preindustrial levels. With this method, an individual or community can use their own experience as context for understanding how future changes in the frequency, intensity, or duration of such events may differ from current conditions where they live, or from their recollection of a recent heat wave. For instance, someone who remembers the Atlanta heat wave on 10 August 2007 will learn that peak temperatures similar to that event will occur on average 19 times each year, or approximately once every 8 days each summer, if global temperatures rise 4 °C. Having a better and more intuitive understanding of the risks of extreme heat events can inform mitigation, adaptation, or other decision‐making activities. Key Points: We used CMIP5 data to characterize extreme heat risk in the United States for global average temperature changes ranging from 1.5 to 4 degrees CelsiusAs global temperatures warm, historically rare extreme heat events in the United States are projected to become increasingly commonA moderate climate scenario delays impacts by almost a generation relative to the higher scenario and prevents the most extreme impacts [ABSTRACT FROM AUTHOR]

Background. Changes in climate are predicted to influence parasite and pathogen infection patterns in terrestrial and marine environments. Increases in temperature in particular may greatly alter biological processes, such as host-parasite interactions. For example, parasites could differentially benefit from increased reproduction and transmission or hosts could benefit from elevated immune responses that may mediate or even eliminate infections. In the southeastern United States, the Eastern oyster, Crassostrea virginica, is infected by the lethal protozoan parasite, Perkinsus marinus. Under field conditions, intertidal (air-exposed) oysters have been found to have significantly higher P. marinus infection intensity and marginally higher infection prevalence than subtidal (submerged) oysters. During summer, air temperatures are much warmer than water and this exposure of intertidal oysters to higher temperatures is a suggested mechanism for increased infection intensity. Methods. We simulated intertidal exposure using controlled laboratory experiments to determine how host traits (survival and immune response) and parasite infection intensity will respond to elevated air temperature ranging from 27 °C to 53 °C during emersion at low tide. In Georgia, where our work was conducted, the average summer water temperature is 29 °C and the average maximum high air temperature in July is 33 °C (though oysters have been shown to survive at much higher air temperatures). Results. Host survival declined as temperature increased, with a definitive drop-off between 39-43 °C. Negative effects of air temperature on host immune response (phagocytic activity) were detectable only at extremely high temperatures (47-50 °C) when hosts were suffering acute mortality. Parasite infection intensity peaked at 35 °C. Discussion. Our results suggest that an increase in average summer air temperature to 35 °C or higher could affect oyster survival directly through temperature-related impacts in the short-term and indirectly through increased P. marinus infection intensity over the long-term. [ABSTRACT FROM AUTHOR]

Work published in 2012 revealed that annual minimum temperatures over the coterminous United States (USA) have increased faster than mean temperatures, causing a pronounced poleward shift in the positions of hardiness zones defined by the expected annual minimum temperature. Here, estimates of increases in annual minimum temperatures are updated and extended to other land areas where station temperature records are available. Annual minimum temperatures have increased faster than mean temperatures in seasonally cold regions globally, but have warmed at about the same rate as mean temperatures in tropical climates. The mean increase in annual minimum temperature across the available weather stations was 2.0 °C between 1970 and 2016 (or almost 0.5 °C per decade), as compared to an increase of 1.2 °C in mean temperature. Recent cold winters in regions such as Eastern North America did not clearly break with this trend and were within the range of variability seen in past decades. Overall, annual minimum temperatures appear to be increasing steadily, though with considerable inter-annual variability. [ABSTRACT FROM AUTHOR]

Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980-2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices. [ABSTRACT FROM AUTHOR]

Atmospheric temperature time series for the nine climate regions of the contiguous U.S. are accurately reproduced by the superposition of oscillatory modes, representing the Atlantic multidecadal oscillation (AMO) and the Pacific decadal oscillation (PDO), on a monotonic mode representing, at least in part, the effect of radiant forcing due to increasing atmospheric CO2. The relative importance of the different modes varies among the nine climate regions, grouping them into three mega-regions: Southeastern comprising the South, Southeast and Ohio Valley; Central comprising the Southwest, Upper Midwest, and Northeast; and Northwestern comprising the West, Northwest, and Northern Rockies & Plains. The defining characteristics of the mega-regions are: Southeastern – dominated by the AMO, no PDO influence; Central – influenced by the AMO, no PDO influence, Northwestern – influenced by both the AMO and PDO. Temperature vs. time curves calculated by combining the separate monotonic and oscillatory modes agree well with the measured temperature time series, indicating that the 1938-1974 small decrease in contiguous U.S. temperature was caused by the superposition of the downward-trending oscillatory mode on the upward-trending monotonic mode while the 1980-2000 large increase in temperature was caused by the superposition of the upward-trending oscillatory mode on the upward-trending monotonic mode. The oscillatory mode, mostly representing the AMO, was responsible for about 72% of the entire contiguous U.S. temperature increase over that time span with the contribution varying from 86 to 42% for individual climate regions. [ABSTRACT FROM AUTHOR]

In order to plan for global changing climate experiments are being conducted in many countries, but few have monitored the effects of the climate change treatments (warming, elevated CO2) on the experimental plot microclimate. During three years of an eight year study with year-round feedback-controlled infra-red heater warming (1.5/3.0°C day/night) and growing season free-air CO2 enrichment (600 ppm) in the mixed-grass prairie of Wyoming, USA, we monitored soil, leaf, canopy-air, above-canopy-air temperatures and relative humidity of control and treated experimental plots and evaluated ecologically important temperature differentials. Leaves were warmed somewhat less than the target settings (1.1 & 1.5°C day/night) but soil was warmed more creating an average that matched the target settings extremely well both during the day and night plus the summer and winter. The site typically has about 50% bare or litter covered soil, therefore soil heat transfer is more critical than in dense canopy ecosystems. The Wyoming site commonly has strong winds (5 ms-1 average) and significant daily and seasonal temperature fluctuations (as much as 30°C daily) but the warming system was nearly always able to maintain the set temperatures regardless of abiotic variation. The within canopy-air was only slightly warmed and above canopy-air was not warmed by the system, therefore convective warming was minor. Elevated CO2 had no direct effect nor interaction with the warming treatment on microclimate. Relative humidity within the plant canopy was only slightly reduced by warming. Soil water content was reduced by warming but increased by elevated CO2. This study demonstrates the importance of monitoring the microclimate in manipulative field global change experiments so that critical physiological and ecological conclusions can be determined. Highly variable energy demand fluctuations showed that passive IR heater warming systems will not maintain desired warming for much of the time. [ABSTRACT FROM AUTHOR]

Epidemics of wheat stripe rust, caused by Puccinia striiformis f. sp. tritici ( Pst), are more frequent in the regions where Pst can oversummer and overwinter. Regions for potential oversummering and overwintering of Pst were determined in the contiguous United States using a survival index (SI) ranging from 0 (most unfavorable) to 10 (most favorable) developed based on long-term weather data. The pathogen can survive in cool summer in the most regions north of latitude 40°N, particularly Washington, Idaho, Montana, Oregon and California. Due to limiting high temperatures, it survives marginally during summer in Arkansas, Delaware, Georgia, Iowa, Illinois, Indiana, Kansas, Kentucky, Massachusetts, Missouri, Ohio, Oklahoma, Rhode Island and Texas. Similarly, unfavorable hot summer restricts summer survival of the pathogen in the most regions south of 40°N except for highlands in the Rocky or Appalachian Mountains. Warm winters favor fungal survival in most regions south of 40°N and the Pacific Coast, including Alabama, Arkansas, Arizona, California, Florida, Georgia, Idaho, Louisiana, Mississippi, New Mexico, Nevada, Oregon, South Carolina, Texas and Washington. Severe winters do not allow survival in most regions north of 40°N and east of the Rocky Mountains, whereas less severe winter in Delaware, Illinois, Indiana, Kansas, Kentucky, Massachusetts, Maryland, Michigan, Missouri, North Carolina, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Rhode Island, Tennessee, Utah and Virginia permits marginal survival of Pst. Most wheat-growing regions have climatic suitability for either oversummering or overwintering. Both oversummering and overwintering can occur in the Pacific Northwest (Idaho, Oregon and Washington), Arizona, California, North Carolina, New Mexico, Pennsylvania, Virginia and West Virginia. These regions may provide primary inoculum for stripe rust epidemics in their own and surrounding regions. [ABSTRACT FROM AUTHOR]

Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications. [ABSTRACT FROM AUTHOR]

Reliable indicators of rapid drought onset can help to improve the effectiveness of drought early warning systems. In this study, the evaporative stress index (ESI), which uses remotely sensed thermal infrared imagery to estimate évapotranspiration (ET), is compared to drought classifications in the U.S. Drought Monitor (USDM) and standard precipitation-based drought indicators for several cases of rapid drought development that have occurred across the United States in recent years. Analysis of meteorological time series from the North American Regional Reanalysis indicates that these events are typically characterized by warm air temperature and low cloud cover anomalies, often with high winds and dewpoint depressions that serve to hasten evaporative depletion of soil moisture reserves. Stan-dardized change anomalies depicting the rate at which various multiweek ESI composites changed over different time intervals are computed to more easily identify areas experiencing rapid changes in ET. Overall, the results demonstrate that ESI change anomalies can provide early warning of incipient drought impacts on agricultural systems, as indicated in crop condition reports collected by the National Agricultural Statistics Service. In each case examined, large negative change anomalies indicative of rapidly drying conditions were either coincident with the introduction of drought in the USDM or lead the USDM drought depiction by several weeks, depending on which ESI composite and time-differencing interval was used. Incorporation of the ESI as a data layer used in the construction of the USDM may improve timely depictions of moisture conditions and vegetation stress associated with flash drought events. [ABSTRACT FROM AUTHOR]

This study shows that, since 1979 when outgoing longwave radiation (OLR) observations became reliably available, most of the useful U.S. seasonal weather impact of El Niño events is associated with the few events identified by the behavior of outgoing longwave radiation (OLR) over the eastern equatorial Pacific ('OLR-El Niño events'). These events produce composite seasonal regional weather anomalies that are 95% statistically significant and robust (associated with almost all events). Results also show that there are very few statistically significant seasonal weather anomalies, even at the 80% level, associated with the non-OLR-El Niño events. A major enhancement of statistical seasonal forecasting skill over the contiguous United States appears possible by incorporating these results. It is essential to respect that not all events commonly labeled as El Niño events lead to statistically useful U.S. seasonal forecast skill. [ABSTRACT FROM AUTHOR]

Numerical simulations using a version of the GFDL/NOAA Modular Ocean Model (MOM 3) are analyzed to demonstrate interdecadal pathway changes from the subtropics to the tropics in the South Pacific Ocean. After the 1976-77 climate shift, the subtropical gyre of the South Pacific underwent significant changes, characterized by a slowing down in its circulation and a southward displacement of its center by about 5°-10° latitude on the western side. The associated circulation altered its flow path in the northwestern part of the subtropical gyre, changing from a direct pathway connecting the subtropics to the tropics before the shift to a more zonal one after. This effectively prevented some subtropical waters from directly entering into the western equatorial Pacific. Since waters transported onto the equator around the subtropical gyre are saline and warm, such changes in the direct pathway and the associated reduction in equatorward exchange from the subtropics to the tropics affected water mass properties downstream in the western equatorial Pacific, causing persisted freshening and cooling of subsurface water as observed after the late 1970s. Previously, changes in gyre strength and advection of temperature anomalies have been invoked as mechanisms for linking the subtropics and tropics on interdecadal time scales. Here we present an additional hypothesis in which geographic shifts in the gyre structure and location (a pathway change) could play a similar role. [ABSTRACT FROM AUTHOR]

The projected rise in temperature in the 21st century will alter forest ecosystem functioning and carbon dynamics. To date, the acclimation of plant photosynthesis to rising temperature has not been adequately considered in earth system models. Here we present a study on regional ecosystem carbon dynamics under future climate scenarios incorporating temperature acclimation effects into a large-scale ecosystem model, the terrestrial ecosystem model (TEM). We first incorporate a general formulation of the temperature acclimation of plant photosynthesis into TEM, and then apply the revised model to the forest ecosystems of the conterminous United States for the 21st century under the future Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) climate scenarios A1FI, A2, B1 and B2. We find that there are significant differences between the estimates of carbon dynamics from the previous and the revised models. The largest differences occur under the A1FI scenario, in which the model that considers acclimation effects predicts that the region will act as a carbon sink, and that cumulative carbon in the 21st century will be 35 Pg C higher than the estimates from the model that does not consider acclimation effects. Our results further indicate that in the region there are spatially different responses to temperature acclimation effects. This study suggests that terrestrial ecosystem models should take temperature acclimation effects into account so as to more accurately quantify ecosystem carbon dynamics at regional scales. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration (ET0) is needed for determining agricultural water demand and reservoir losses and driving hydrologic simulation models. This study was conducted to explore the application of the National Centers for Environmental Prediction's (NCEP's) Global Forecast System (GFS) retrospective forecast (reforecast) dataset combined with the NCEP-U.S. Department of Energy (DOE) Reanalysis 2 dataset (R2) to forecast ET0 in the southeastern United States using a forecast analog approach. Seven approaches of estimating ET0 using the Penman-Monteith (PM) and Thornthwaite equations were evaluated by substitution of climatological mean values of variables or by bias correcting variables including solar radiation, maximum temperature, and minimum temperature using the R2 dataset. The skill of both terciles and extremes (10th and 90th percentiles) were evaluated. Overall, for the ET0 forecast approaches that combined R2 solar radiation with temperature, relative humidity, and wind speed from GFS, the reforecasts produced higher skill than methods that estimated parameters using GFS the reforecasts data only. The primary increase in skill was due to the use of relative humidity from the GFS reforecasts and long-term climatological mean values of solar radiation from the R2 dataset, indicating its importance in forecasting ET0 in the region. While the five categorical forecasts were skillful, the skill of upper and lower tercile forecasts was greater than that of lower and upper extreme forecasts and middle tercile forecasts. Most of the forecasts were skillful in the first 5 lead days. [ABSTRACT FROM AUTHOR]

Long-term (1901-2002) diurnal temperature range (DTR) data are evaluated to examine their spatial and temporal variability across the United States; the early century origin of the DTR declines; and the relative regional contributions to DTR variability among cloud cover, precipitation, soil moisture, and atmosphere/ocean teleconnections. Rotated principal component analysis (RPCA) of the Climatic Research Unit (CRU) Time Series (TS) 2.1 dataset identifies five regions of unique spatial U.S. DTR variability. RPCA creates regional orthogonal indices of cloud cover, soil moisture, precipitation, and the teleconnections used subsequently in stepwise multiple linear regression to examine their regional impact on DTR, maximum temperature (Tmax), and minimum temperature (Tmin). The southwestern United States has the smallest DTR and cloud cover trends as both Tmax and Tmin increase over the century. The Tmin increases are the primary influence on DTR trend in other regions, except in the south-central United States, where downward Tmax trend largely affects its DTR decline. The Tmax and DTR tend to both exhibit simultaneous decadal variations during unusually wet and dry periods in response to cloud cover, soil moisture, and precipitation variability. The widely reported post-1950 DTR decline began regionally at various times ranging from around 1910 to the 1950s. Cloud cover alone accounts for up to 63.2% of regional annual DTR variability, with cloud cover trends driving DTR in northern states. Cloud cover, soil moisture, precipitation, and atmospheric/oceanic teleconnection indices account for up to 80.0% of regional variance over 1901-2002 (75.4% in detrended data), although the latter only account for small portions of this variability. [ABSTRACT FROM AUTHOR]