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Providing comprehensive regional- and local-scale information on changes observed in the climate system plays a vital role in planning effective and efficient climate change adaptation options, specifically over resource-limited regions. Here, we assess changes in temperature and heat waves over different regions of the African continent, with a focus on spatiotemporal trends and the time of emergence of change in hot extremes from natural variability. We analyse absolute and relative threshold indices. Data sets include temperatures from observations (CRUTS4.03 and BEST) and from three representative state-of-the-art reanalyses (ERA5, MERRA2 and JRA-55) for the common period 1980–2018. Statistically significant warming is observed over all regions of Africa in temperature time series from CRU observations and reanalysis data, although the trend strength varies between data sets. Also, extreme temperatures and heat wave indices from BEST observations and all reanalysis data sets reveal increasing trends over all regions of the African continent. However, there are differences in both trend strength and time evolution of heat wave indices between different reanalysis data sets. Most data sets agree in identifying 2010 as a peak heat year over Northern and Western Africa while Eastern and Southern Africa experienced the highest heat wave occurrence in 2016. Our results clearly reveal that heat wave occurrences have emerged from natural climate variability in Africa. The earliest time of emergence takes place in the Northern Africa region in the early 2000s while in the other African regions emergence over natural variability is found mainly after 2010. This also depends on the respective index metrics, where indices based on more consecutive days show later emergence of heat wave trends. Overall, significant warming and an increase in heat wave occurrence is found in all regions of Africa and has emerged from natural variability in the past one or two decades.

The sensitivity of global tropical cyclone (TC) activity to changes in a zonally symmetric sea surface temperature (SST) distribution and the associated large-scale atmospheric circulation are investigated. High-resolution (~50-km horizontal grid spacing) atmospheric general circulation model simulations with maximum SST away from the equator are presented. Simulations with both fixed-SST and slab ocean lower boundary conditions are compared. The simulated TCs that form on the poleward flank of the intertropical convergence zone (ITCZ) are tracked and changes in the frequency and intensity of those storms are analyzed between the different experiments. The total accumulated cyclone energy (ACE) increases as the location of the maximum SST shifts farther away from the equator. The location of the ITCZ also shifts in conjunction with changes to the SST profile, and this plays an important role in mediating the frequency and intensity of the TCs that form within this modeling framework.

Future tropical cyclone activity is a topic of great scientific and societal interest. In the absence of a climate theory of tropical cyclogenesis, general circulation models are the primary tool available for investigating the issue. However, the identification of tropical cyclones in model data at moderate resolution is complex, and numerous schemes have been developed for their detection. The influence of different tracking schemes on detected tropical cyclone activity and responses in the Hurricane Working Group experiments is examined herein. These are idealized atmospheric general circulation model experiments aimed at determining and distinguishing the effects of increased sea surface temperature and other increased CO2 effects on tropical cyclone activity. Two tracking schemes are applied to these data and the tracks provided by each modeling group are analyzed. The results herein indicate moderate agreement between the different tracking methods, with some models and experiments showing better agreement across schemes than others. When comparing responses between experiments, it is found that much of the disagreement between schemes is due to differences in duration, wind speed, and formation-latitude thresholds. After homogenization in these thresholds, agreement between different tracking methods is improved. However, much disagreement remains, accountable for by more fundamental differences between the tracking schemes. The results indicate that sensitivity testing and selection of objective thresholds are the key factors in obtaining meaningful, reproducible results when tracking tropical cyclones in climate model data at these resolutions, but that more fundamental differences between tracking methods can also have a significant impact on the responses in activity detected.

The decay of underdense meteor trails in the polar mesopause region is thought to be predominantly due to ambipolar diffusion, a process governed by the ambient temperature and pressure. Hence, observations of meteor decay times have been used to indirectly measure the temperature of the mesopause region. Using meteor observations from a SKiYMET radar in northern Sweden during 2005, this study found that weaker meteor trails have shorter decay times (on average) than relatively stronger trails. This suggests that processes other than ambipolar diffusion can play a significant role in trail diffusion. One particular mechanism, namely electron-ion recombination, is explored. This process is dependent on the initial electron density within the meteor trail, and can lead to a disproportionate reduction in decay time, depending on the strength of the meteor.

A season of operational cell and track data from Darwin, Australia, has been analyzed to explore the statistical characteristics of the convective cell heights. The statistics for the monsoon and break regimes are significantly different with the break season cells being higher for a given reflectivity threshold. The monsoon cells produce more rain, but there are fewer intense cells and there is a much larger contribution from stratiform rain. The monsoon cells are also slightly larger, but shorter lived than the breaks. The shorter lifetime may reflect a more rapid transition to a longer-lived stratiform character. The monsoon regime is shown to be associated with large-scale ascent and higher humidity that may lead to more frequent, but weaker cells. Within regimes, the subset of intense cells generally reach near the tropopause or overshoot. However, there is little evidence in the data for a multimodal distribution of cell heights, except perhaps for the intense monsoon cases. Instead, the picture is a continuous distribution of cell heights with the peak of the distribution shifting to higher values as the distributions are conditioned on higher reflectivity.

Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. To provide these estimates, different approaches to constrain, filter, or weight climate model projections into probabilistic distributions have been proposed. However, an assessment of multiple such methods to, for example, expose cases of agreement or disagreement, is often hindered by a lack of coordination, with methods focusing on a variety of variables, time periods, regions, or model pools. Here, a consistent framework is developed to allow a quantitative comparison of eight different methods; focus is given to summer temperature and precipitation change in three spatial regimes in Europe in 2041–60 relative to 1995–2014. The analysis draws on projections from several large ensembles, the CMIP5 multimodel ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods’ key features are summarized, assumptions are discussed, and resulting constrained distributions are presented. Method agreement is found to be dependent on the investigated region but is generally higher for median changes than for the uncertainty ranges. This study, therefore, highlights the importance of providing clear context about how different methods affect the assessed uncertainty—in particular, the upper and lower percentiles that are of interest to risk-averse stakeholders. The comparison also exposes cases in which diverse lines of evidence lead to diverging constraints; additional work is needed to understand how the underlying differences between methods lead to such disagreements and to provide clear guidance to users.