Sharp increase of Saharan dust intrusions over the Western Mediterranean and Euro-Atlantic region in winters 2020–2022 and associated atmospheric circulation [Discussion paper]

During the winters of the 2020–2022 period, several intense North African dust intrusions affected Europe. Some of them displayed a duration never recorded before. They were referred to as exceptional by several international operational and research institutions considering that wintertime is the season with minimum dust activity in the Mediterranean and Europe. These anomalous winter events with origin in North Africa largely affected western Mediterranean. The main objective of the present work is to analyse the atmospheric drivers (synoptic and large-scale environments) of wintertime (from January to March) dust events over the region covering North Africa, the Western Mediterranean and the Euro-Atlantic during the period 2003–2022. Overall, our results indicate large interannual variability over the study period. A dust catalogue of dust events identified by aerosols retrievals from satellite and aerosol reanalysis products shows a very irregular record and large differences between winter months. The analyses demonstrate a positive anomaly in dust concentration and maximum altitude during the dust events of 2020–2022 in comparison with those of previous years (2003–2019). Winter dust events over western Mediterranean are associated with enhanced blocking activity over the Euro-Atlantic sector, which favours the obstruction of the westerlies and the occurrence of cut-off lows at subtropical latitudes. However, these high-pressure systems can exhibit a large variety of configurations, including meridional dipole blocking patterns with poleward shifted jets or Mediterranean subtropical ridges with an intensified mid-latitude jet. The former was more frequent during the reference 2003–2019 period, whereas the latter was relatively common during the anomalous 2020–2022 period

Piece‐wise constant cluster modelling of dynamics of upwelling patterns

A comprehensive approach is presented to analyse season's coastal upwelling represented by weekly sea surface temperature (SST) image grids. Our three-stage data recovery clustering method assumes that the season's upwelling can be divided into shorter periods of stability, ranges, each to be represented by a constant core and variable shell parts. Corresponding clustering algorithms parameters are automatically derived by using the least-squares clustering criterion. The approach has been successfully applied to real-world SST data covering two distinct regions: Portuguese coast and Morocco coast, for 16 years each.LA/P/0101/2020info:eu-repo/semantics/publishedVersio

Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales

This Special Issue is expected to advance our understanding of these emerging patterns, teleconnections, and extreme events in a changing world for more accurate prediction or projection of their changes especially on different spatial–time scales

Statistical/climatic models to predict and project extreme precipitation events dominated by large-scale atmospheric circulation over the central-eastern China

Global warming has posed non-negligible effects on regional extreme precipitation changes and increased the uncertainties when meteorologists predict such extremes. More importantly, floods, landslides, and waterlogging caused by extreme precipitation have had catastrophic societal impacts and led to steep economic damages across the world, in particular over central-eastern China (CEC), where heavy precipitation due to the Meiyu-front and typhoon activities often causes flood disaster. There is mounting evidence that the anomaly atmospheric circulation systems and water vapor transport have a dominant role in triggering and maintaining the processes of regional extreme precipitation. Both understanding and accurately predicting extreme precipitation events based on these anomalous signals are hot issues in the field of hydrological research. In this thesis, the self-organizing map (SOM) and event synchronization were used to cluster the large-scale atmospheric circulation reflected by geopotential height at 500 hPa and to quantify the level of synchronization between the identified circulation patterns with extreme precipitation over CEC. With the understanding of which patterns were associated with extreme precipitation events, and corresponding water vapor transport fields, a hybrid deep learning model of multilayer perceptron and convolutional neural networks (MLP-CNN) was proposed to achieve the binary predictions of extreme precipitation. The inputs to MLP-CNN were the anomalous fields of GP at 500 hPa and vertically integrated water vapor transport (IVT). Compared with the original MLP, CNN, and two other machine learning models (random forest and support vector machine), MLP-CNN showed the best performance. Additionally, since the coarse spatial resolution of global circulation models and its large biases in extremes precipitation estimations, a new precipitation downscaling framework that combination of ensemble-learning and nonhomogeneous hidden Markov model (Ensemble-NHMM) was developed, to improve the reliabilities of GCMs in historical simulations and future projection. The performances of downscaled precipitation from reanalysis and GCM datasets were validated against the gauge observations and also compared with the results of traditional NHMM. Finally, the Ensemble-NHMM downscaling model was applied to future scenario data of GCM. On the projections of change trends in precipitation over CEC in the early-, medium- and late- 21st centuries under different emission scenarios, the possible causes were discussed in term of both thermodynamic and dynamic factors. Main results are enumerated as follows. (1) The large-scale atmospheric circulation patterns and associated water vapor transport fields synchronized with extreme precipitation events over CEC were quantitatively identified, as well as the contribution of circulation pattern changes to extreme precipitation changes and their teleconnection with the interdecadal modes of the ocean. Firstly, based on the nonparametric Pettitt test, it was found that 23% of rain gauges had significant abrupt changes in the annual extreme precipitation from 1960 to 2015. The average change point in the annual extreme precipitation frequency and amount occurred near 1989. Complex network analysis showed that the rain gauges highly synchronized on extreme precipitation events can be clustered into four clusters based on modularity information. Secondly, the dominant circulation patterns over CEC were robustly identified based on the SOM. From the period 1960–1989 to 1990–2015, the categories of identified circulation patterns generally remain almost unchanged. Among these, the circulation patterns characterized by obvious positive anomalies of 500 hPa geopotential height over the Eastern Eurasia continent and negative values over the surrounding oceans are highly synchronized with extreme precipitation events. An obvious water vapor channel originating from the northern Indian Ocean driven by the southwesterly airflow was observed for the representative circulation patterns (synchronized with extreme precipitation). Finally, the circulation pattern changes produced an increase in extreme precipitation frequency from 1960–1989 to 1990–2015. Empirical mode decomposition of the annual frequency variation signals in the representative circulation pattern showed that the 2–4 yr oscillation in the annual frequency was closely related to the phase of El Niño and Southern Oscillation (ENSO); while the 20–25 yr and 42–50 yr periodic oscillations were responses to the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. (2) A regional extreme precipitation prediction model was constructed. Two deep learning models-MLP and CNN were linearly stacked and used two atmospheric variables associated with extreme precipitation, that is, geopotential height at 500 hPa and IVT. The hybrid model can learn both the local-scale information with MLP and large-scale circulation information with CNN. Validation results showed that the MLP-CNN model can predict extreme or non-extreme precipitation days with an overall accuracy of 86%. The MLP-CNN also showed excellent seasonal transferability with an 81% accuracy on the testing set from different seasons of the training set. MLP-CNN significantly outperformed over other machine learning models, including MLP, CNN, random forest, and support vector machine. Additionally, the MLP-CNN can be used to produce precursor signals by 1 to 2 days, though the accuracy drops quickly as the number of precursor days increases. (3) The GCM seriously underestimated extreme precipitation over CEC but showed convincing results for reproducing large-scale atmospheric circulation patterns. The accuracies of 10 GCMs in extreme precipitation and large-scale atmospheric circulation simulations were evaluated. First, five indices were selected to measure the characteristics of extreme precipitation and the performances of GCMs were compared to the gauge-based daily precipitation analysis dataset over the Chinese mainland. The results showed that except for FGOALS-g3, most GCMs can reproduce the spatial distribution characteristics of the average precipitation from 1960 to 2015. However, all GCMs failed to accurately estimate the extreme precipitation with large underestimation (relative bias exceeds 85%). In addition, using the circulation patterns identified by the fifth-generation reanalysis data (ERA5) as benchmarks, GCMs can reproduce most CP types for the periods 1960–1989 and 1990–2015. In terms of the spatial similarity of the identified CPs, MPI-ESM1-2-HR was superior. (4) To improve the reliabilities of precipitation simulations and future projections from GCMs, a new statistical downscaling framework was proposed. This framework comprises two models, ensemble learning and NHMM. First, the extreme gradient boosting (XGBoost) and random forest (RF) were selected as the basic- and meta- classifiers for constructing the ensemble learning model. Based on the top 50 principal components of GP at 500 hPa and IVT, this model was trained to predict the occurrence probabilities for the different levels of daily precipitation (no rain, very light, light, moderate, and heavy precipitation) aggregated by multi-sites. Confusion matrix results showed that the ensemble learning model had sufficient accuracy (>88%) in classifying no rain or rain days and (>83%) predicting moderate precipitation events. Subsequently, precipitation downscaling was done using the probability sequences of daily precipitation as large-scale predictors to NHMM. Statistical metrics showed that the Ensemble-NHMM downscaled results matched best to the gauge observations in precipitation variabilities and extreme precipitation simulations, compared with the result from the one that directly used circulation variables as predictors. Finally, the downscaling model also performed well in the historical simulations of MPI-ESM1-2-HR, which reproduced the change trends of annual precipitation and the means of total extreme precipitation index. (5) Three climate scenarios with different Shared Socioeconomic Pathways and Representative Concentration Pathways (SSPs) were selected to project the future precipitation change trends. The Ensemble-NHMM downscaling model was applied to the scenario data from MPI-ESM1-2-HR. Projection results showed that the CEC would receive more precipitation in the future by ~30% through the 2075–2100 period. Compared to the recent 26-year epoch (1990–2015), the frequency and magnitude of extreme precipitation would increase by 21.9–48.1% and 12.3–38.3% respectively under the worst emission scenario (SSP585). In particular, the south CEC region is projected to receive more extreme precipitation than the north. Investigations of thermodynamic and dynamic factors showed that climate warming would increase the probability of stronger water vapor convergence over CEC. More wet weather states due to the enhanced water vapor transport, as well as the increased favoring large-scale atmospheric circulation and the strengthen pressure gradient would be the factors for the increased precipitation

Classes of low-frequency earthquakes based on inter-time distribution reveal a precursor event for the 2011 Great Tohoku Earthquake

Recently, slow earthquakes (slow EQ) have received much attention relative to understanding the mechanisms underlying large earthquakes and to detecting their precursors. Low-frequency earthquakes (LFE) are a specific type of slow EQ. In the present paper, we reveal the relevance of LFEs to the 2011 Great Tohoku Earthquake (Tohoku-oki EQ) by means of cluster analysis. We classified LFEs in northern Japan in a data-driven manner, based on inter-time, the time interval between neighboring LFEs occurring within 10 km. We found that there are four classes of LFE that are characterized by median inter-times of 24 seconds, 27 minutes, 2.0 days, and 35 days, respectively. Remarkably, in examining the relevance of these classes to the Tohoku-oki EQ, we found that activity in the shortest inter-time class (median 23 seconds) diminished significantly at least three months before the Tohoku-oki EQ, and became completely quiescent 30 days before the event (p-value = 0.00014). Further statistical analysis implies that this class, together with a similar class of volcanic tremor, may have served as a precursor of the Tohoku-oki EQ. We discuss a generative model for these classes of LFE, in which the shortest inter-time class is characterized by a generalized gamma distribution with the product of shape parameters 1.54 in the domain of inter-time close to zero. We give a possible geodetic interpretation for the relevance of LFE to the Tohoku-oki EQ

Detecting impacts of extreme events with ecological in situ monitoring networks

Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with  ≈  100 randomly placed sites in Europe yield a  ≥  90 % chance of detecting the eight largest (typically very large) extreme events; but only a  ≥  50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere

Atmospheric blocking and intercomparison of objective detection methods: flow field characteristics.

Objective methods for identifying and quantifying atmospheric blocking have been developed over recent decades, primarily targeting North Atlantic blocks. Differences arise from these methods, leading to changes in the resultant blocking climatology. To understand these differences, and better inform future assessments built on quantitative detection of blocks, this paper examines blocking properties produced by three different objective detection algorithms over the global extratropics. Blocking criteria examined include 500 hPa geopotential height anomaly ( Z∗ ), column-averaged potential vorticity anomaly ( PV∗ ), and 500 hPa geopotential height gradient (AGP). Results are analyzed for blocking climatologies and for instantaneous blocking patterns, as well as distributions of block size, speed, duration, and distance traveled. The results emphasize physical characteristics of the flow field and the subsequent blocking regions that emerge; overall, PV∗ and Z∗ blocked regions often have higher pattern correlation and spatial similarity, though these two methods also display high agreement with AGP in some instances. Z∗ finds the largest (and greatest number of) blocked regions, while PV∗ -detected regions are smallest in all instances except Southern Hemisphere winter. In some cases, PV∗ tracks a nearby jet streak, leading to differences with height-based algorithms. All three algorithms detect some questionable low-latitude blocks that are stationary and persist but do not impair zonal flow, although at different times. Therefore, careful consideration of the algorithm biases is important in future blocking studies. For example, linking extreme weather to detected blocking could vary substantially depending on the algorithm used