The state of knowledge regarding trends and an understanding of their causes is presented for a specific subset of extreme weather and climate types. For severe convective storms (tornadoes, hailstorms, and severe thunderstorms), differences in time and space of practices of collecting reports of events make using the reporting database to detect trends extremely difficult. Overall, changes in the frequency of environments favorable for severe thunderstorms have not been statistically significant. For extreme precipitation, there is strong evidence for a nationally averaged upward trend in the frequency and intensity of events. The causes of the observed trends have not been determined with certainty, although there is evidence that increasing atmospheric water vapor may be one factor. For hurricanes and typhoons, robust detection of trends in Atlantic and western North Pacific tropical cyclone (TC) activity is significantly constrained by data heterogeneity and deficient quantification of internal variability. Attribution of past TC changes is further challenged by a lack of consensus on the physical link- ages between climate forcing and TC activity. As a result, attribution of trends to anthropogenic forcing remains controversial. For severe snowstorms and ice storms, the number of severe regional snowstorms that occurred since 1960 was more than twice that of the preceding 60 years. There are no significant multidecadal trends in the areal percentage of the contiguous United States impacted by extreme seasonal snowfall amounts since 1900. There is no distinguishable trend in the frequency of ice storms for the United States as a whole since 1950.

This paper describes an improved edition of the climate division dataset for the conterminous United States (i.e., version 2). The first improvement is to the input data, which now include additional station networks, quality assurance reviews, and temperature bias adjustments. The second improvement is to the suite of climatic elements, which now includes both maximum and minimum temperatures. The third improvement is to the computational approach, which now employs climatologically aided interpolation to address topographic and network variability. Version 2 exhibits substantial differences from version 1 over the period 1895–2012. For example, divisional averages in version 2 tend to be cooler and wetter, particularly in mountainous areas of the western United States. Division-level trends in temperature and precipitation display greater spatial consistency in version 2. National-scale temperature trends in version 2 are comparable to those in the U.S. Historical Climatology Network whereas version 1 exhibits less warming as a result of historical changes in observing practices. Divisional errors in version 2 are likely less than 0.5°C for temperature and 20 mm for precipitation at the start of the record, falling rapidly thereafter. Overall, these results indicate that version 2 can supersede version 1 in both operational climate monitoring and applied climatic research.

A coherent picture of global surface temperature change since the late nineteenth century ennerges from a statistical reconstruction of an integrated collection of historical temperature observations over the land and ocean.T he most widely recognized measure of observed climate change is the century-scale trend in globally averaged surface temperature. The global average is a simple theoretical concept, but its computation in practice is far from trivial. The complexity stems mainly from the idiosyncrasies of historical weather observations, most of which were collected for operational purposes, such as aviation and agriculture, rather than climate change detection. In particular, certain practices that are of little operational significance such as relocating a station or changing its instrumentation, may profoundly impact the integrity of the climate record (Aguilar et al.

High quality data sources are critical to scientists, engineers, and decision makers alike. The models that scientists develop and test with quality-assured data eventually become used by a wider community, from policy makers' long-term strategies based upon weather and climate predictions to emergency managers' decisions to deploy response crews. The process of developing high quality data in one network, the Oklahoma Mesonetwork (Mesonet) is detailed in this manuscript. The Oklahoma Mesonet quality-assurance procedures consist of four principal components: an instrument laboratory, field visits, automated computer routines, and manual inspection. The instrument laboratory ensures that all sensors that are deployed in the network measure up to high standards established by the Mesonet Steering Committee. Routine and emergency field visits provide a manual inspection of the performance of the sensors and replacement as necessary. Automated computer routines monitor data each day, set data flags as appropriate, and alert personnel of potential errors in the data. Manual inspection provides human judgment to the process, catching subtle errors that automated techniques may miss. The quality-assurance (QA) process is tied together through efficient communication links. A QA manager serves as the conduit through whom all questions concerning data quality flow. The QA manager receives daily reports from the automated system, issues trouble tickets to guide the technicians in the field, and issues summary reports to the broader community of data users. Technicians and other Mesonet staff remain in contact through cellular communications, pagers, and the World Wide Web. Together, these means of communication provide a seamless system: from identifying suspicious data, to field investigations, to feedback on action taken by the technician.

Editor’s note: For easy download the posted pdf of the State of the Climate for 2016 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.