Abstract. Correlation and correlation-based measures (e.g., the coefficient of determination) have been widely used to evaluate the "goodness-of-fit" of hydrologic and hydroclimatic models.

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Warming experiments are increasingly relied on to estimate plant responses to global climate change. For experiments to provide meaningful predictions of future responses, they should reflect the empirical record of responses to temperature variability and recent warming, including advances in the timing of flowering and leafing. We compared phenology (the timing of recurring life history events) in observational studies and warming experiments spanning four continents and 1,634 plant species using a common measure of temperature sensitivity (change in days per degree Celsius). We show that warming experiments underpredict advances in the timing of flowering and leafing by 8.5-fold and 4.0-fold, respectively, compared with long-term observations. For species that were common to both study types, the experimental results did not match the observational data in sign or magnitude. The observational data also showed that species that flower earliest in the spring have the highest temperature sensitivities, but this trend was not reflected in the experimental data. These significant mismatches seem to be unrelated to the study length or to the degree of manipulated warming in experiments. The discrepancy between experiments and observations, however, could arise from complex interactions among multiple drivers in the observational data, or it could arise from remediable artefacts in the experiments that result in lower irradiance and drier soils, thus dampening the phenological responses to manipulated warming. Our results introduce uncertainty into ecosystem models that are informed solely by experiments and suggest that responses to climate change that are predicted using such models should be re-evaluated.

Annual minimum, median, and maximum daily streamflow for 400 sites in the conterminous United States (U.S.), measured during 1941–1999, were examined to identify the temporal and spatial character of changes in streamflow statistics. Results indicate a noticeable increase in annual minimum and median daily streamflow around 1970, and a less significant mixed pattern of increases and decreases in annual maximum daily streamflow. These changes in annual streamflow statistics primarily occurred in the eastern U.S. In addition, the streamflow increases appear as a step change rather than as a gradual trend and coincide with an increase in precipitation.

Changing patterns of correlations between the historical average June–November Southern Oscillation Index (SOI) and October–March precipitation totals for 84 climate divisions in the western US indicate a large amount of variability in SOI/precipitation relations on decadal time scales. Correlations of western US precipitation with SOI and other indices of tropical El Niño–Southern Oscillation (ENSO) processes were much weaker from 1920 to 1950 than during recent decades. This variability in teleconnections is associated with the character of tropical air–sea interactions as indexed by the number of out‐of‐phase SOI/tropical sea surface temperature (SST) episodes, and with decadal variability in the North Pacific Ocean as indexed by the Pacific Decadal Oscillation (PDO). ENSO teleconnections with precipitation in the western US are strong when SOI and NINO3 are out‐of‐phase and PDO is negative. ENSO teleconnections are weak when SOI and NINO3 are weakly correlated and PDO is positive. Decadal modes of tropical and North Pacific Ocean climate variability are important indicators of periods when ENSO indices, like SOI, can be used as reliable predictors of winter precipitation in the US. Copyright © 1999 Royal Meteorological Society

No abstract

Weather and climate extremes have been varying and changing on many different time scales. In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country. While flood magnitudes in the Southwest have been decreasing, flood magnitudes in the Northeast and north-central United States have been increasing. Confounding the analysis of trends in river flooding is multiyear and even multidecadal variability likely caused by both large-scale atmospheric circulation changes and basin-scale “memory” in the form of soil moisture. Droughts also have long-term trends as well as multiyear and decadal variability. Instrumental data indicate that the Dust Bowl of the 1930s and the drought in the 1950s were the most significant twentieth-century droughts in the United States, while tree ring data indicate that the megadroughts over the twelfth century exceeded anything in the twentieth century in both spatial extent and duration. The state of knowledge of the factors that cause heat waves, cold waves, floods, and drought to change is fairly good with heat waves being the best understood.

Severity of rain on snow depends on a number of factors, and an overall decrease in these events appears to be driven, in part, by changes in El Nino-Southern Oscillation. Rain-on-snow floods are a fascinating hydrometeorological phenomenon. Their severity depends not only on the magnitude of the precipitation, but also on the elevation of the freezing level and the water equivalent and areal extent of the antecedent snowpack. The necessary juxtaposition of these causative factors creates interesting challenges for both flood prediction and flood risk assessment.Flood forecasting involves running a hydrologic model up to the start of the forecast period to estimate basin initial conditions (e.g., snowpack, soil moisture), and then running the model into the future with an ensemble of meteorological forecasts to produce probabilistic forecasts of streamflow (Day 1985;Clark and Hay 2004). The major uncertainties associated with predicting