Large-scale Circulation Regimes Research Articles

Ensemble Predictability of Week 3/4 Precipitation and Temperature over the United States via Cluster Analysis of the Large-Scale Circulation

Abstract Forecasting the week 3/4 period presents many challenges, resulting in a need for improvements to forecast skill. At this distance from initial conditions, numerical models struggle to present skillful forecasts of temperature, precipitation, and associated extremes. One approach to address this is to utilize more predictable large-scale circulation regimes to make forecasts of temperature and precipitation anomalies, using the association between the regimes and surface weather obtained from reanalysis products. This study explores the utility of k-means cluster analysis on geopotential heights and their ability to make skillful regime predictions in the week 3/4 period. Using 14-day running means of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) 500-hPa geopotential heights for the wintertime December–February (DJF) period, circulation regimes are identified using k-means clustering. Each period is assigned a cluster number, allowing the compositing of any reanalysis or observation variable to form cluster maps. Maps of 500-hPa height, 2-m temperature, precipitation, and storm-track anomalies are some of the variables composited. The utility of these relationships in a dynamical forecast setting is tested via Global Ensemble Forecast System v12 (GEFSv12) hindcasts and real-time ensemble suite forecasts. Week 3/4 deterministic and probabilistic experimental forecasts are then derived from cluster assignments using several methods. We find, via a conditional skill analysis, forecasts strongly correlated with a cluster exhibit greater skill for both dynamical model and cluster-derived forecasts. Our preliminary results represent a step forward to aid forecasters make more skillful assessments of the circulation regime and its associated surface weather for this challenging forecast time scale. Significance Statement Our paper links the local statistics of 14-day precipitation and temperature within the United States to very broad preferred patterns of winds and pressure over the wide Pacific–North American region. Such patterns, known as “circulation regimes,” provide the background that heavily influences local surface conditions. We find that forecasts for which midatmospheric anomalies are closely aligned with one or more circulation regimes are better at predicting the U.S. weather probabilities than other week 3–4 forecasts. A tool based on this finding identifies forecasts for which confidence is higher. Future work will be designed to further exploit the links between circulation regimes and weather to improve the accuracy of week 3–4 forecasts.

Shallow-water modelling of the atmospheric circulation regimes of brown dwarfs and their observable features

ABSTRACTObservations of time-varying thermal emission from brown dwarfs suggest that they have large-scale atmospheric circulation. The magnitude of this variability ranges from a few per cent to tens of per cent, implying a range of sizes of atmospheric perturbations. Periodograms of phase curves of the thermal emission reveal a range of peaks with different periods and widths, suggesting different atmospheric flow speeds and directions. This implies a variety of atmospheric circulations in the different brown dwarfs observed to date, but there is no general theoretical understanding of the circulation regimes these objects can support, and the resulting sizes and velocities of their atmospheric features. We therefore use an idealized 2D shallow-water model of a brown dwarf atmosphere to understand their potential large-scale circulation regimes. We non-dimensionalize the model to reduce the number of input parameters to two non-dimensional numbers: the thermal Rossby number and the non-dimensional radiative time-scale. This allows us to define a parameter space that bounds the entire range of brown dwarf behaviour possible in our model. We analyse the resulting height, velocity, and potential vorticity fields in this parameter space, and simulate observed phase curve and periodograms for comparison with real observations. We use our results to qualitatively define four circulation regimes, which we hope will be useful for interpreting observations and for guiding simulations with more detailed physical models.

Changes in temperature–precipitation correlations over Europe: are climate models reliable?

Inter-variable correlations (e.g., between daily temperature and precipitation) are key statistical properties to characterise probabilities of simultaneous climate events and compound events. Their correct simulations from climate models, both in values and in changes over time, is then a prerequisite to investigate their future changes and associated impacts. Therefore, this study first evaluates the capabilities of one 11-single run multi-model ensemble (CMIP6) and one 40-member single model initial-condition large ensemble (CESM) over Europe to reproduce the characteristics of a reanalysis dataset (ERA5) in terms of temperature–precipitation correlations and their historical changes. Next, the ensembles’ correlations for the end of the 21st century are compared. Over the historical period, both CMIP6 and CESM ensembles have season-dependent and spatially structured biases. Moreover, the inter-variable correlations from both ensembles mostly appear stationary. Thus, although reanalysis displays significant correlation changes, none of the ensembles can reproduce them, with internal variability representing only 30% on the inter-model variability. However, future correlations show significant changes over large spatial patterns. Yet, those patterns are rather different for CMIP6 and CESM, reflecting a large uncertainty in changes. In addition, for historical and future projections, an analysis conditional on atmospheric circulation regimes is performed. The conditional correlations given the regimes are found to be the main contributor to the biases in correlation over the historical period, and to the past and future changes of correlation. These results highlight the importance of the large-scale circulation regimes and the need to understand their physical relationships with local-scale phenomena associated to specific inter-variable correlations.

Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data

Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) and transfer learning to forecast the North Atlantic-European weather regimes during extended boreal winter for 1–15 days into the future. We apply state-of-the-art interpretation techniques from the machine learning literature to attribute particular regions of interest or potential teleconnections relevant for any given weather cluster prediction or regime transition. We demonstrate superior forecasting performance relative to several classical meteorological benchmarks, as well as logistic regression and random forests. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5–6 days. Finally, we find transfer learning to be of paramount importance, similar to previous data-driven atmospheric forecasting studies.

Summertime Moisture Sources and Transportation Pathways for China and Associated Atmospheric Circulation Patterns

Based on the Lagrangian particle dispersion model, HYSPLIT 4.9, this study analyzed the summertime atmospheric moisture sources and transportation pathways affecting six subregions across China. The sources were: Midlatitude Westerly (MLW), Siberian-Arctic regions (SibArc), Okhotsk Sea (OKS), Indian Ocean (IO), South China Sea (SCS), Pacific Ocean (PO), and China Mainland (CN). Furthermore, the relative contributions of these seven moisture sources to summertime precipitation in China were quantitatively assessed. Results showed that the CN precipitation source dominates the interannual and interdecadal variation of precipitation in most subregions, except Southwest and South China. The Northeast China vortex and Pacific–Japan (PJ) teleconnection, which transport water vapor from the MLW, OKS and PO sources, are crucial atmospheric systems and patterns for the variation of precipitation in Northeast China. The interannual variation of precipitation in Northwest and North China is mainly dominated by mid–high-latitude Eurasian wave trains, which provide the necessary dynamical conditions and associated moisture transport from the MLW and SibArc sources. In addition, an enhanced western North Pacific subtropical high (WNPSH) accompanied by the East Asian–western North Pacific summer monsoon and PJ teleconnection, transports extra moisture to North China from the SCS and PO sources, as well to the Yangtze River Valley and South China. The Indian summer monsoon (ISM) is also critically important for the interdecadal change in precipitation over the Yangtze River Valley and South China, via the southwesterly branch of moisture transport from the IO source. The interdecadal changes in precipitation over Southwest China are determined by the IO and SCS sources, via enhanced WNPSH coupling with a weakened ISM. These results suggest that the interdecadal and interannual variations of moisture sources contribute to the attendant variation of summertime precipitation in China via large-scale circulation regimes in both the mid–high and lower latitudes.

Increased persistence of large-scale circulation regimes over Asia in the era of amplified Arctic warming, past and future

Extreme weather events in Asia have been occurring with increasing frequency as the globe warms in response to rising concentrations of greenhouse gases. Many of these events arise from weather regimes that persist over a region for days or even weeks, resulting in disruptive heatwaves, droughts, flooding, snowfalls, and cold spells. We investigate changes in the persistence of large-scale weather systems through a pattern-recognition approach based on daily 500 hPa geopotential height anomalies over the Asian continent. By tracking consecutive days that the atmosphere resides in a particular pattern, we identify long-duration events (LDEs), defined as lasting longer than three days, and measure their frequency of occurrence over time in each pattern. We find that regimes featuring positive height anomalies in high latitudes are occurring more often as the Arctic warms faster than mid-latitudes, both in the recent past and in model projections for the twenty-first century assuming unabated greenhouse gas emissions. The increased dominance of these patterns corresponds to a higher likelihood of LDEs, suggesting that persistent weather conditions will occur more frequently. By mapping observed temperature and precipitation extremes onto each atmospheric regime, we gain insight into the types of disruptive weather events that will become more prevalent as particular patterns become more common.

Precipitation and Moisture in Four Leading CMIP5 Models: Biases across Large-Scale Circulation Regimes and Their Attribution to Dynamic and Thermodynamic Factors

As key variables in general circulation models, precipitation and moisture in four leading models from CMIP5 (phase 5 of the Coupled Model Intercomparison Project) are analyzed, with a focus on four tropical oceanic regions. It is found that precipitation in these models is overestimated in most areas. However, moisture bias has large intermodel differences. The model biases in precipitation and moisture are further examined in conjunction with large-scale circulation by regime-sorting analysis. Results show that all models consistently overestimate the frequency of occurrence of strong upward motion regimes and peak descending regimes of 500-hPa vertical velocity [Formula: see text]. In a given [Formula: see text] regime, models produce too much precipitation compared to observation and reanalysis. But for moisture, their biases differ from model to model and also from level to level. Furthermore, error causes are revealed through decomposing contribution biases into dynamic and thermodynamic components. For precipitation, the contribution errors in strong upward motion regimes are attributed to the overly frequent [Formula: see text]. In the weak upward motion regime, the biases in the dependence of precipitation on [Formula: see text] and the [Formula: see text] probability density function (PDF) make comparable contributions, but often of opposite signs. On the other hand, the biases in column-integrated water vapor contribution are mainly due to errors in the frequency of occurrence of [Formula: see text], while thermodynamic components contribute little. These findings suggest that errors in the frequency of [Formula: see text] occurrence are a significant cause of biases in the precipitation and moisture simulation.

The isotopic signature of monsoon conditions, cloud modes, and rainfall type

AbstractThis work provides a comprehensive physically based framework for the interpretation of the north Australian rainfall stable isotope record (δ18O and δ2H). Until now, interpretations mainly relied on statistical relationships between rainfall amount and isotopic values on monthly timescales. Here, we use multiseason daily rainfall stable isotope and high resolution (10 min) ground‐based C‐band polarimetric radar data and show that the five weather types (monsoon regimes) that constitute the Australian wet season each have a characteristic isotope ratio. The data suggest that this is not only due to changes in regional rainfall amount during these regimes but, more importantly, is due to different rain and cloud types that are associated with the large scale circulation regimes. Negative (positive) isotope anomalies occurred when stratiform rainfall fractions were large (small) and the horizontal extent of raining areas were largest (smallest). Intense, yet isolated, convective conditions were associated with enriched isotope values whereas more depleted isotope values were observed when convection was widespread but less intense. This means that isotopic proxy records may record the frequency of which these typical wet season regimes occur. Positive anomalies in paleoclimatic records are most likely associated with periods where continental convection dominates and convection is sea‐breeze forced. Negative anomalies may be interpreted as periods when the monsoon trough is active, convection is of the oceanic type, less electric, and stratiform areas are wide spread. This connection between variability of rainfall isotope anomalies and the intrinsic properties of convection and its large‐scale environment has important implications for all fields of research that use rainfall stable isotopes.

The large-scale circulation during air quality hazards in Bergen, Norway

In this study, we assessed the large-scale circulation anomalies leading to wintertime air pollution episodes in Bergen, Norway. Bergen is an example of a city, where a strong interplay between large-scale and local circulation features is relevant. Certain large-scale circulation regimes that are different from the usually assumed large-scale stagnation, lead to local air pollution episodes. We assessed these circulation regimes and their predictability. For this, we modified and applied a previously developed atmospheric circulation proxy for the identification of air pollution episodes. Use of this proxy on data from a high-resolution atmospheric general circulation model showed a good reproduction of the total number of potentially polluted days per month and their inter-monthly variability. We also found a link between the persistence of the flow above the Bergen valley and the occurrence and severity of the local air pollution episodes. Analysis of the large-scale circulation over the North Atlantic-European region, with respect to air pollution in Bergen, revealed that the persistence in the meteorological conditions connected to the air pollution episodes is not necessarily caused by large-scale anomalies of the atmospheric circulation over the Norwegian west coast. It is rather connected to anomalies further upstream as far away as Greenland.

Is Blocking a Circulation Regime?

Abstract The relationship between Pacific blocking and large-scale circulation regimes is investigated. The large-scale circulation regimes are obtained by cluster analysis using the k-means method and tested against significance and reproducibility. Pacific blocking is described using two different methods. In a direct approach, blocking is described by a recently developed blocking index, which is defined in terms of potential temperature anomaly on a surface of constant potential vorticity. In an indirect approach, the occurrence of extreme events is used as a proxy for blockings. Between the two methods there is a causal relationship; the direct one is an indication of the occurrence of the blocking, while the indirect one is a measure of some of the effects caused by the blocking. The results indicate that large-scale circulation regimes are related to but not necessarily tightly coupled to blocking and weather extremes in the Pacific–North America region.

Quasi-Stationary and Transient Periods in the Northern Hemisphere Circulation Series

In this paper, persistence characteristics of the Northern Hemisphere (NH) extratropical circulation have been studied. A simple method, based on the speed with which the atmosphere moves in the phase space (PSS, measured by 2-day lag distances), was adopted to partition the circulation data series into 5-day or longer quasi-stationary periods (QSP) and alternating transition periods (TP). The method is based on the assumptions that large-scale circulation regimes often develop abruptly and that during their development transient activity is either unchanged or enhanced. The partitioning results reveal that a whole cycle of QSP and TP on the hemisphere has an average duration of 20 days with considerable amount of variability. The average length of a QSP-TP cycle is not sensitive to changes of a relatively wide range in the PSS limit value employed in the method. In this range of limit values, the average length of the cycle changes less than 17%, while the ratio between the length of QSP and TP increases dramatically from 0.73 to 4.42. The partitioning results are statistically very similar for three complementary sectors of the hemisphere. However, we found very little synchronicity in the changes in the three sectors. The correlation between changes in any of the three sectors and on the whole hemisphere is at a much higher level, around 0.75. Although the length of the cycle on height values at individual grid points is in the range we can expect from a red-noise process, this cycle length is considerably (20%) shorter than that in a larger region or on the whole hemisphere. This is an indication that the persistence characteristics of larger-scale circulation, due to spatial interactions, show more persistence than, and cannot be well modeled by, a simple autoregressive process. Statistical tests indicate that the hemispheric QSPs are largely temporally uncorrelated and cannot be results of a random partitioning method. All these results suggest that the basic assumption about the regime-like behavior of the atmosphere is at least partially true: large-scale regime changes are indeed accompanied with higher speed of changes in the circulation phase space. Further evidence is presented that the circulation patterns in the phase space are distributed as a multivariate normal distribution in a phase-average sense (i.e., as a function of distance from the mean). Hence, the characteristic distance between neighboring circulation patterns is smaller close to the climate mean than farther away from it. As a consequence that had to be considered in this study, the day-to-day changes in the circulation (an inverse measure of persistence) are also smaller close to the climate mean. It is also argued that phase-average multinormality is the primary characteristic of the distribution of circulation patterns in the phase space and any secondary characteristic (local density maximum) should be searched for and interpreted in this context.