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Rain-on-snow (ROS) melt events reduce the amount of water stored in the snowpack while also exacerbating flooding. The hydrologic implications of changing ROS events in a warming climate, however, are still uncertain. This research used a calibrated and validated Soil and Water Assessment Tool (SWAT) hydrologic model, modified with energy budget equations to simulate ROS melt and forced with a climate model ensemble representing moderate greenhouse gas concentrations, to simulate changes to ROS melt in the North American Great Lakes Basin from 1960–2069. The changes to ROS events between the historic period (1960–1999) and mid-century (2040–2069) represent an approximately 30 % reduction in melt in warmer, southern subbasins but less than 5 % reduction in melt in colder, northern subbasins. Additionally, proportionally more rainfall reduces the formation of snowpacks, with area-weighted combined winter and spring rain-to-snow ratios rising from approximately 1.5 historically to 1.9 by the mid-21st century. Areas with historic mean combined winter and spring air temperatures lower than −2 ∘C have ROS regimes that are resilient to mid-21st century warming projections, but ROS occurrence in areas that have mean combined winter and spring temperatures near the freezing point are sensitive to changing air temperatures. Also, relationships between changes in the timing of ROS melt and water yield endure throughout spring but become weak by summer. As the influence of ROS melt events on hydrological systems is being altered in a changing climate, these conclusions are important to inform adaptive management of freshwater ecosystems and human uses in regions of the globe that are sensitive to changes in ROS events.

Understanding the response of tropical cyclone precipitation to ongoing climate change is essential to determine associated flood risk. However, instrumental records are short-term and fail to capture the full range of variability in seasonal totals of precipitation from tropical cyclones. Here we present a 473-year-long tree-ring proxy record comprised of longleaf pine from excavated coffins, a historical house, remnant stumps, and living trees in southern Mississippi, USA. We use cross-dating dendrochronological analyses calibrated with instrumental records to reconstruct tropical cyclone precipitation stretching back to 1540 CE. We compare this record to potential climatic controls of interannual and multidecadal tropical cyclone precipitation variability along the Gulf Coast. We find that tropical cyclone precipitation declined significantly in the two years following large Northern Hemisphere volcanic eruptions and is influenced by the behavior of the North Atlantic subtropical high-pressure system. Additionally, we suggest that tropical cyclone precipitation variability is significantly, albeit weakly, related to Atlantic multidecadal variability. Finally, we suggest that we need to establish a network for reconstructing precipitation from tropical cyclones in the Southeast USA if we want to capture regional tropical cyclone behavior and associated flood risks.

Monitoring land-cover change and understanding the dynamics of forest cover is critically important for the management of forest ecosystems. This study assesses land cover change (1990–2015) in the...

This study estimated the trends in the number of days that fall within the near-zero (0 °C) range of the temperature continuum. This narrow range has importance for potential transportation hazards and freeze-thaw cycles. While the tails of the air-temperature distribution and their trends often are closely examined under the climate change context, the frequency and trend of other portions of the air-temperature distribution can be equally important, as many societal impacts are caused by events in the non-tail region, such as near-zero °C temperatures (NZT). Examining the trend of the number of NZT days over the conterminous USA for the period of 1948–1949 through 2010–2011, we found three distinct spatial clusters. The most distinctive spatial clusters are found along the West Coast (positive temperature trends leading to a decrease in NZT days), the High Plains and Northern Rockies (positive temperature trends leading to an increase in NZT days), and the southeastern USA (negative temperature trends l...

Forest fires have profound impacts on the spatial distribution of vegetation type and density. This research analyzes the impacts of the 2002 Ponil Fire in New Mexico on landscape patterns using a moving-window analysis of landscape metrics. Categorically derived landscape metrics and a measure of fire severity—the Normalized Burn Ratio—are used to produce a quantitative, spatial distribution of landscape change. While gross land-cover change summaries and landscape-metric changes indicate a more heterogeneous landscape following the fire, the moving-window approach demonstrates the oversimplification of landscape-scale metrics and summaries. The moving-window approach indicates that the majority of areas in the landscape were unchanged in mean patch size, whereas mean (and median) patch size increased according to landscape-level measures. Contrary to expectations, average patch density and richness were also nearly unchanged. The moving-window approach is particularly helpful in analyzing large fires wi...

Commonly used sums-of-squares-based error or deviation statistics—like the standard deviation, the standard error, the coefficient of variation, and the root-mean-square error—often are misleading indicators of average error or variability. Sums-of-squares-based statistics are functions of at least two dissimilar patterns that occur within data. Both the mean of a set of error or deviation magnitudes (the average of their absolute values) and their variability influence the value of a sum-of-squares-based error measure, which confounds clear assessment of its meaning. Interpretation problems arise, according to Paul Mielke, because sums-of-squares-based statistics do not satisfy the triangle inequality. We illustrate the difficulties in interpreting and comparing these statistics using hypothetical data, and recommend the use of alternate statistics that are based on sums of error or deviation magnitudes.

Traditional box plots, described by Tukey in 1977, can be biased representations of geographic or spatial variables, since they do not take into account the areas associated with the elements of geographic variables. To help solve this problem, we propose and evaluate a spatially (areally) weighted box plot, a "geographic box plot," that can be used to describe a wide variety of geographic variables. Much of our discussion of box plots also applies to "traditional" and "geographic" histograms and percentile plots. Traditional and geographic box plots of one hypothetical and two real-world geographic variables are constructed and compared to illustrate our points, as well as to illustrate the often marked differences between traditional and geographic box plots of the same geographic variables. The real-world geographic variables considered here are human population density (in the year 2000) within the contiguous 48 United States and five-year-average (January 2001-December 2005) air temperature over the ...

In climatology, spatial representativeness can be measured as the degree to which an instrumental temperature record resolves the climatic variability across an area. Some station records may disproportionately resolve characteristics of their immediate surroundings and, therefore, have less utility in representing climate (or weather) over larger spatial scales. To evaluate spatial representativeness of historical air-temperature records, a modified geostatistical procedure was developed. The procedure measures the spatial representativeness of a station based on variogram parameter estimates—in this case the nugget. This application is novel for two reasons: (1) variogram models are fit to station-specific or "point-centered" semivariance and (2) variogram models are fit to semivariance computed at intervals in a time series. Fitting variograms to point-centered semivariance may be used to create time series of nuggets for each station in a network. Nugget time series may show subsets of a time series t...

We examine seasonal and spatial variations of stochastic-weather-generator (SWG) parameters and their impact on simulated weather sequences. Using daily weather observations from 29 stations across the contiguous United States, we estimate monthly station-specific parameters that are compared with the constant parameters that frequently are used in SWG applications. A WGEN- type SWG is then used to generate a 100 yr record of daily maximum and minimum air temperature and daily total solar radiation at each station. These sequences are compared to sequences generated with constant parameters. While the means and standard deviations of the generated sequences are in agreement, the SWG with station-specific parameters preserves relationships between variables. This is evident in both the lag-0 and lag-1 cross-correlations between generated variables and derived variables, such as diurnal temperature range. These results suggest that literature-based SWG parameters may be appropriate for applications where monthly values of the means and stan- dard deviations of generated variables are of interest. For applications that require proper simulation of relationships between variables, station-specific parameterizations are recommended.

Using data from the contiguous USA, the relationship between mean and standard devi- ation of daily air temperature was estimated on a monthly timescale from 1948 to 1997. In general, there is either an inverse relationship or a weak relationship between mean and standard deviation. For both daily maximum and daily minimum air temperature, the inverse relationship is spatially coherent for one-third to two-thirds of the contiguous USA for most months. The inverse relationship also is fairly strong, with typical reductions in standard deviation ranging from 0.2 to 0.5°C for every 1°C increase in mean temperature. A weaker, direct relationship between mean and standard devi- ation occurs in some northern states, primarily during spring and fall months. Using the predominant inverse and weak relationships as historical analogs for future climatic change suggests that interdi- urnal variability of air temperature should either decrease or remain unchanged under warming con- ditions. Although the variability of air temperature may decrease or remain unchanged at most loca- tions in the contiguous USA, the probability of extremely high air temperatures should still increase, depending on the magnitude of changes in mean air temperature and the nature of the variance response.

The focus of this research is the development, evaluation, and implementation of a mathematical/statistical time-series model to simulate daily total wind energy in the central United States. By modeling the annual cycle, sub-annual cycles, autocorrelation, and random component of the time series, a concise representation of daily total wind energy is provided. The model can be used to create scenarios and probability distributions for wind energy that are conditional on time of year and persistence (autocorrelation). As an example, probabilistic forecasts are generated to determine the probability of exceeding the 80th percentile wind-energy value for two days of the year, based on both a high and low wind-energy value for the previous day. These forecasts indicate that seasonality is a much more important factor in determining a particular day's wind-energy value (and probability distribution) than is persistence in the central United States. Scenarios of climatic change were simulated by altering the a...

When compiling climatological means of air temperature, station data usually are selected on the basis of whether they exist within a fixed base period (e.g. 1961 to 1990). Within such analyses, station records that do not contain sufficient data during the base period or only contain data from other base periods are excluded. If between-station variability is of interest (e.g. a map or gridded field is needed), then removing such stations assumes that spatial interpolation to the location of culled stations is more reliable than using a temporal mean from a shorter or different averaging period — the latter is a process that we call 'temporal substitution.' Data from the United States Historical Climate Network (HCN) are used to examine whether spatial interpolation or temporal substitution is more reliable for multiyear averages of monthly and annual mean air temperature. After exhaustively sam- pling all possible 5-, 10-, and 30-yr averaging periods from 1921 to 1994, spatially averaged inter- polation and substitution errors are estimated for all months and for annual averages. For all months, temporal substitution produces lower overall error than traditional spatial interpolation for both 10- and 30-yr averages. Maps of mean absolute error (for all averaging periods) show that spatial interpolation errors are largest in mountainous regions while temporal substitution errors are largest in the north- central and eastern USA, especially in winter. A spatial interpolation algorithm (topographically aided interpolation, TAI) that incorporates elevation data reduces interpolation error, but also produces larger errors than temporal substitution for all months when using 30-yr averages and for all months except January, February, and March when using 10-yr averages. For 5-yr averages, however, TAI produces lower errors than temporal substitution, especially in winter. For the USA, therefore, it is suggested that for averaging periods less than 10 yr in length, elevation-aided spatial interpolation is preferable to temporal substitution. Conversely, for averaging periods longer than 10 yr in length, temporal substitu- tion is preferable to spatial interpolation. Analysis of the 1961 to 1990 period using a wide range of network densities demonstrates that temporal substitution generally is more reliable than spatial inter- polation of 30-yr averages, regardless of network density.

Hourly wind data from a network of climate stations in the north-central United States (drawn from the states of Illinois, lowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota, and Wisconsin) are analyzed to evaluate the efficacy of spatial analyses of near-surface wind speed and power. Spatial autocorrelation functions (acfs) were calculated at a number of timescales: annual, monthly, daily, and hourly. Annual wind speeds have virtually no coherent distance-decay relationship; monthly data produce a more consistent relationship, but still exhibit a large amount of scatter. Both daily and hourly data have classical decay with increasing distance between stations and there appears to be an optimal level of temporal aggregation, near the daily timescale, for spatial analysis of wind. In general, however, spatial acfs overestimate the spatial coherence of both wind speed and power. Temporal nonstationarities in wind data (i.e., diurnal and annual cycles) bias spatial autocorrelation functi...

When observed air temperatures are analyzed spatially, irregularly sampled data are usually interpolated in some fashion. As a result, methods of spatial analysis clearly play a role in determining the size and variability of estimated air temperature changes, both spatially and temporally. Through graphical and statistical analysis, 3 spherically based interpolation methods inversedistance weighting, triangulated surface patches, and thin-plate splines are evaluated and compared using air temperature anomaly data. Analysis of errors resulting from spatial interpolation provides information about the strengths and weaknesses of historical station networks. Analysis of differences between the 3 interpolation methods suggests that similar spatial patterns are produced, but with some regional disparities. Mean absolute differences between interpolation methods can be over 0.4 "C for some sparse statlon networks and as low as O.l°C for dense station networks. When averaged spatially, however, the differences tend to offset one another, producing time series of terrestrial average air temperature anomalies that are largely independent of spatial interpolation method. Using cross validation to analyze spatial interpolation errors suggests that sparse station networks can produce nontrivial interpolation errors. Station networks from the late 1800s produce average interpolation errors of nearly 0.5"C, errors that are similar in magnitude to spatial standard deviations of air temperature anomalies. Denser station networks, typical of the 1950s and 1960s, produce average interpolation errors as low as 0.2OC. While these regional interpolation errors do not appear to influence estimates of terrestrial average air temperature, they do raise additional concerns regarding our ability to detect small climatic signals at regional scales.

Three statistical models that estimate daily maximum ozone (O3) concentrations in the lower Fraser Valley of British Columbia (BC) are specified using measured concentrations from two monitoring stations during the time period 1978–1985. The three models are (1) a univariate deterministic/stochastic model, (2) a univariate autoregressive integrated moving average (ARIMA) model, and (3) a bivariate temperature and persistence based regression model. The three models as well as a persistence forecast are tested by comparison with O3 concentrations observed during 1986; it is concluded that the bivariate model is superior to both unvariate models and persistence. The ARIMA model has nearly the same predictive capability as persistance while the mixed deterministic/stochastic model performs the worst. This suggests that the traditional time series technique of decomposing a series into a trend, a cycle and a stochastic component may not be appropriate for O3 air quality forecasting.