Detectability of historical trends in station‐based precipitation characteristics over the continental United States

Abstract

The goal of this paper is to detect secular trends in observed, station-based precipitation variations and extreme event occurrences over the United States. By definition, detectable trends are those that are unlikely to have arisen from internal variability alone. To represent this internal variability, we use station-specific, seasonally varying, daily time scale stationary stochastic weather models-models in which the simulated interannual-to-multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period-to first estimate the trends in various means and extremes that can occur even with fixed, climatological daily precipitation characteristics. Detection of secular trends in the observed climate-whether naturally or anthropogenically induced-can then be defined relative to this stochastic variability, i.e., as trends in the means and/or extremes that are likely to have occurred only through a change in the underlying precipitation characteristics. The derived results have two important ramifications. First, they identify "hot spot" regions in which trends in precipitation characteristics are already emerging from within the envelope of stochastic variability, including (but not limited to) positive trends in annual occurrence across most of the U.S. and positive trends in annual intensity and heavy-event accumulations across the Interior Plains and around the Great Lakes. Further, they identify "sentinel" metrics which show the greatest detectability-e.g., annual precipitation occurrence and intensity-as well as those which show the least detectability and hence are unlikely to produce any statistically meaningful signals-e.g., seasonal total precipitation and extremes.

Precipitation trends in the island of Ireland using a dense, homogenized, observational dataset

“…Considering the low differences between the raw and homogenized data, and the likely minor improvement of accuracy by homogenization, the question remains as to whether it is necessary or not to homogenize precipitation total time series. In contemporary studies of precipitation climate, data are often subjected only to quality control, but not to homogenization (e.g., Anderson et al ., 2015), or only inhomogeneities known from metadata are considered (Mekis and Vincent, 2011). In studies related to precipitation observations, low frequency of significant inhomogeneities were found in several other studies (Domonkos, 2015 and references therein).…”

Analyses for High‐Resolution Projections Through the End of the 21st Century for Precipitation Extremes Over the United States

“…However, there are important regional differences in these changes. Several previous studies also showed that both hourly and daily extreme precipitation events have increased in intensity and spatial coverage across most of North America over the past several decades (Anderson et al, ; Janssen et al, , ; Min et al, ; Peterson et al, ; Prein et al, ). These studies also found that these changes are primarily due to anthropogenic forcing—not natural forcing—in the climate system (e.g., Zhang et al, ).…”

Terrestrial Evaporation and Moisture Drainage in a Warmer Climate

“…We assert that these perturbations are not intended to represent real forecasts of future climate. Forecasts of perturbations at local scale will depend on uncertain changes in atmospheric water vapor dynamics (Anderson et al, 2015;Byrne & O'Gorman, 2015;Dai et al, 2018;Muller et al, 2011;Prein & Pendergrass, 2019;Romps, 2011;Short Gianotti et al, 2014;Sohn & Park, 2010;Thackeray et al, 2018;Vecchi et al, 2006) and land-atmosphere-biosphere feedback (Greve et al, 2018;Greve & Seneviratne, 2015;Novick et al, 2016;Rigden et al, 2018) and must be estimated in fully coupled settings (Berg et al, 2016;Berg & Sheffield, 2018;Milly & Dunne, 2016;Swann, 2018). Results from this study can then be applied to variable fields of perturbations.…”

“…Fu & Feng, 2014), along with distributional changes in precipitation intensity (Greve et al, 2014;Hirabayashi et al, 2013;Schewe et al, 2014). The spatial patterns of these changes are driven by the coupling between radiative processes, cloud physics, and moisture advection and are affected to a large degree by unpredictable internal variability in global atmospheric dynamics (Anderson et al, 2015;Greve et al, 2018;Greve & Seneviratne, 2015;Hawkins & Sutton, 2011;Samset et al, 2016). The modeled land surface response to these changes is represented through parameterizations-as simple as single functions or as complex as a land surface biosphere model-and is rarely separated from the uncertain atmospheric drivers or confronted with observations (Berg et al, 2017).…”