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Historical and projected trends in extreme precipitation events are examined in Coupled Model Intercomparison Project 5 (CMIP5) models and observations, over the contiguous United States (CONUS), using several approaches. This study updates earlier studies that have used the extreme precipitation index (EPI) to assess observations and goes further by using the EPI to evaluate available climate model simulations. An increasing trend over the CONUS was found in the EPI, with large differences among seven subregions of the United States. Median of CMIP5 simulations also finds an increasing trend in the EPI, but with a smaller magnitude than the observations. Model spread is large and in most cases bigger than the model signal itself. Statistically significant (95th confidence level) increasing trends in the observational-based EPI occur over the Midwest and Eastern regions, while most decreasing trends occur over Western regions. Some models give negative correlation coefficients relative to observations. However, some ensemble members, for most models, show correlation coefficients greater than 0.5. Projections of extreme precipitation event frequency, for representative concentration pathway (RCP) scenarios 4.5 and 8.5, show increasing trends over the CONUS. Both scenarios give a steady increase throughout the period but the RCP 4.5 signal is smaller in magnitude. Overall, the RCP scenarios show an increase across all regions with the exception of some variability between decades in some regions for RCP 4.5. For the CONUS model spread is smaller than the projected signal. Regional analyses show overall agreement among models of a future increase in extreme precipitation event frequency over most regions.

A multimodel evaluation of subseasonal‐to‐seasonal (S2S) hindcast skill of atmospheric rivers (ARs) out to 4‐week lead over the western United States is presented for three operational hindcast systems: European Centre for Medium‐Range Weather Forecasts (ECMWF; Europe), National Centers for Environmental Prediction (NCEP; U.S.), and Environment and Canada Climate Change (ECCC; Canada). Ensemble mean biases and Brier Skill Scores are examined for no, moderate, and high levels of AR activity (0, 1–2, and 3–7 AR days/week, respectively). All hindcast systems are more skillful in predicting no and high AR activity relative to moderate activity. There are isolated regions of skill at week‐3 over 150–125°W, 25–35°N for the no and high AR activity levels, with larger magnitude and spatial extent of the skill in ECMWF and ECCC compared to NCEP. The spatial pattern of this skill suggests that for high AR activity, a southwest‐to‐northeast orientation is more predictable at subseasonal lead times than other orientations, and for no AR activity, more skill exists in the subtropical North Pacific, upstream of central and southern California. AR hindcast skill along the western U.S. is most strongly increased in hindcasts initialized during Madden‐Julian Oscillation (MJO) Phases 1 and 8, and hindcast skill is substantially decreased over California in hindcasts initialized during MJO Phase 4. Skill modulations in the ECMWF hindcast system conditioned on El Niño‐Southern Oscillation phase are weaker than those conditioned on particular MJO phases. This work provides hindcast skill benchmarks and uncertainty quantification for experimental real‐time forecasts of AR activity during winters 2019–2021 as part of the S2S Prediction Project Real‐time Pilot Initiative in collaboration with the California Department of Water Resources.

Abstract. This study provides a new look at the observed and calculated long-term temperature changes from the lower troposphere to the lower stratosphere since 1958 over the Northern Hemisphere. The data sets include the NCEP/NCAR reanalysis, the Free University of Berlin (FU-Berlin) and the RICH radiosonde data sets as well as historical simulations with the CESM1-WACCM global model participating in CMIP5. The analysis is mainly based on monthly layer mean temperatures derived from geopotential height thicknesses in order to take advantage of the use of the independent FU-Berlin stratospheric data set of geopotential height data since 1957. This approach was followed to extend the records for the investigation of the stratospheric temperature trends to the earliest possible time. After removing the natural variability with an autoregressive multiple regression model our analysis shows that the period 1958–2011 can be divided into two distinct sub-periods of long-term temperature variability and trends: before and after 1980. By calculating trends for the summer time to reduce interannual variability, the two periods are as follows. From 1958 until 1979, a non-significant trend (0.06 ± 0.06 °C decade−1 for NCEP) and slightly cooling trends (−0.12 ± 0.06 °C decade−1 for RICH) are found in the lower troposphere. The second period from 1980 to the end of the records shows significant warming (0.25 ± 0.05 °C decade−1 for both NCEP and RICH). Above the tropopause a significant cooling trend is clearly seen in the lower stratosphere both in the pre-1980 period (−0.58 ± 0.17 °C decade−1 for NCEP, −0.30 ± 0.16 °C decade−1 for RICH and −0.48 ± 0.20 °C decade−1 for FU-Berlin) and the post-1980 period (−0.79 ± 0.18 °C decade−1 for NCEP, −0.66 ± 0.16 °C decade−1 for RICH and −0.82 ± 0.19 °C decade−1 for FU-Berlin). The cooling in the lower stratosphere persists throughout the year from the tropics up to 60° N. At polar latitudes competing dynamical and radiative processes reduce the statistical significance of these trends. Model results are in line with reanalysis and the observations, indicating a persistent cooling (−0.33 °C decade−1) in the lower stratosphere during summer before and after 1980; a feature that is also seen throughout the year. However, the lower stratosphere CESM1-WACCM modelled trends are generally lower than reanalysis and the observations. The contrasting effects of ozone depletion at polar latitudes in winter/spring and the anticipated strengthening of the Brewer–Dobson circulation from man-made global warming at polar latitudes are discussed. Our results provide additional evidence for an early greenhouse cooling signal in the lower stratosphere before 1980, which appears well in advance relative to the tropospheric greenhouse warming signal. The suitability of early warning signals in the stratosphere relative to the troposphere is supported by the fact that the stratosphere is less sensitive to changes due to cloudiness, humidity and man-made aerosols. Our analysis also indicates that the relative contribution of the lower stratosphere versus the upper troposphere low-frequency variability is important for understanding the added value of the long-term tropopause variability related to human-induced global warming.

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.