The goal of the Tropospheric Ozone Assessment Report (TOAR) is to provide the research community with an up-to-date scientific assessment of tropospheric ozone, from the surface to the tropopause. While a suite of observations provides significant information on the spatial and temporal distribution of tropospheric ozone, observational gaps make it necessary to use global atmospheric chemistry models to synthesize our understanding of the processes and variables that control tropospheric ozone abundance and its variability. Models facilitate the interpretation of the observations and allow us to make projections of future tropospheric ozone and trace gas distributions for different anthropogenic or natural perturbations. This paper assesses the skill of current-generation global atmospheric chemistry models in simulating the observed present-day tropospheric ozone distribution, variability, and trends. Drawing upon the results of recent international multi-model intercomparisons and using a range of model evaluation techniques, we demonstrate that global chemistry models are broadly skillful in capturing the spatio-temporal variations of tropospheric ozone over the seasonal cycle, for extreme pollution episodes, and changes over interannual to decadal periods. However, models are consistently biased high in the northern hemisphere and biased low in the southern hemisphere, throughout the depth of the troposphere, and are unable to replicate particular metrics that define the longer term trends in tropospheric ozone as derived from some background sites. When the models compare unfavorably against observations, we discuss the potential causes of model biases and propose directions for future developments, including improved evaluations that may be able to better diagnose the root cause of the model-observation disparity. Overall, model results should be approached critically, including determining whether the model performance is acceptable for the problem being addressed, whether biases can be tolerated or corrected, whether the model is appropriately constituted, and whether there is a way to satisfactorily quantify the uncertainty.

Global climate models were used to assess changes in the mean, variability and extreme sea surface temperatures (SSTs) in northern oceans with a focus on large marine ecosystems (LMEs) adjacent to North America, Europe, and the Arctic Ocean. Results were obtained from 26 models in the Community Model Intercomparison Project Phase 5 (CMIP5) archive and 30 simulations from the National Center for Atmospheric Research Large Ensemble Community Project (CESM-LENS). All of the simulations used the observed greenhouse gas concentrations for 1976–2005 and the RCP8.5 “business as usual” scenario for greenhouse gases through the remainder of the 21 st century. In general, differences between models are substantially larger than among the simulations in the CESM-LENS, indicating that the SST changes are more strongly affected by model formulation than internal climate variability. The annual SST trends over 1976–2099 in the 18 LMEs examined here are all positive ranging from 0.05 to 0.5°C decade –1 . SST changes by the end of the 21 st century are primarily due to a positive shift in the mean with only modest changes in the variability in most LMEs, resulting in a substantial increase in warm extremes and decrease in cold extremes. The shift in the mean is so large that in many regions SSTs during 2070–2099 will always be warmer than the warmest year during 1976–2005. The SST trends are generally stronger in summer than in winter, as greenhouse gas heating is integrated over a much shallower climatological mixed layer depth in summer than in winter, which amplifies the seasonal cycle of SST over the 21 st century. In the Arctic, the mean SST and its variability increases substantially during summer, when it is ice free, but not during winter when a thin layer of ice reforms and SSTs remain near the freezing point.