Central-Eastern China persistent heat waves: Evaluation of the AMIP models.

Abstract Large-scale and persistent heat waves affecting central-eastern China are investigated in 40 different simulations of sea surface temperature driven global atmospheric models. The different models are compared with results from reanalysis and ground station datasets. It is found that the dynamics of heat-wave events is well reproduced by the models. However, they tend to produce too-persistent heat-wave events (lasting more than 20 days), and several hypotheses were tested to explain this bias. The daily variability of the temperatures or the seasonal signal did not explain the persistence. However, interannual variability of the temperatures in the models, and especially the sharp transition in the mid-1990s, has a large impact on the duration of heat waves. A filtering method was applied to select the models closest to the observations in terms of events persistence. The selected models do not show a significant difference from the other models for the long-term trends. Thus, the bias on the duration of the events does not impact the reliability of the model positive trends, which is mainly controlled by the changes in mean temperatures.</jats:p

Human influence strengthens the contrast between tropical wet and dry regions

Climate models predict a strengthening contrast between wet and dry regions in the tropics and subtropics (30 °S–30 °N), and data from the latest model intercomparison project (CMIP6) support this expectation. Rainfall in ascending regions increases, and in descending regions decreases in climate models, reanalyses, and observational data. This strengthening contrast can be captured by tracking the rainfall change each month in the wettest and driest third of the tropics and subtropics combined. Since wet and dry regions are selected individually every month for each model ensemble member, and the observations, this analysis is largely unaffected by biases in location of precipitation features. Blended satellite and in situ data from 1988–2019 support the CMIP6-model-simulated tendency of sharpening contrasts between wet and dry regions, with rainfall in wet regions increasing substantially opposed by a slight decrease in dry regions. We detect the effect of external forcings on tropical and subtropical observed precipitation in wet and dry regions combined, and attribute this change for the first time to anthropogenic and natural forcings separately. Our results show that most of the observed change has been caused by increasing greenhouse gases. Natural forcings also contribute, following the drop in wet-region precipitation after the 1991 eruption of Mount Pinatubo, while anthropogenic aerosol effects show only weak trends in tropic-wide wet and dry regions consistent with flat global aerosol forcing over the analysis period. The observed response to external forcing is significantly larger ( p > 0.95) than the multi-model mean simulated response. As expected from climate models, the observed signal strengthens further when focusing on the wet tail of spatial distributions in both models and data

Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century

Global warming is expected to not only impact mean temperatures but also temperature variability, substantially altering climate extremes. Here we show that human-caused changes in internal year-to-year temperature variability are expected to emerge from the unforced range by the end of the 21(st) century across climate model initial-condition large ensembles forced with a strong global warming scenario. Different simulated changes in globally averaged regional temperature variability between models can be explained by a trade-off between strong increases in variability on tropical land and substantial decreases in high latitudes, both shown by most models. This latitudinal pattern of temperature variability change is consistent with loss of sea ice in high latitudes and changes in vegetation cover in the tropics. Instrumental records are broadly in line with this emerging pattern, but have data gaps in key regions. Paleoclimate proxy reconstructions support the simulated magnitude and distribution of temperature variability. Our findings strengthen the need for urgent mitigation to avoid unprecedented changes in temperature variability

Interpreting self-organizing maps through space--time data models

Self-organizing maps (SOMs) are a technique that has been used with high-dimensional data vectors to develop an archetypal set of states (nodes) that span, in some sense, the high-dimensional space. Noteworthy applications include weather states as described by weather variables over a region and speech patterns as characterized by frequencies in time. The SOM approach is essentially a neural network model that implements a nonlinear projection from a high-dimensional input space to a low-dimensional array of neurons. In the process, it also becomes a clustering technique, assigning to any vector in the high-dimensional data space the node (neuron) to which it is closest (using, say, Euclidean distance) in the data space. The number of nodes is thus equal to the number of clusters. However, the primary use for the SOM is as a representation technique, that is, finding a set of nodes which representatively span the high-dimensional space. These nodes are typically displayed using maps to enable visualization of the continuum of the data space. The technique does not appear to have been discussed in the statistics literature so it is our intent here to bring it to the attention of the community. The technique is implemented algorithmically through a training set of vectors. However, through the introduction of stochasticity in the form of a space--time process model, we seek to illuminate and interpret its performance in the context of application to daily data collection. That is, the observed daily state vectors are viewed as a time series of multivariate process realizations which we try to understand under the dimension reduction achieved by the SOM procedure.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS174 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org