Classification of changes in extreme heat over Southeastern Australia

Over half of Australia's population lives in its southeastern quadrant. Temperature records for the 55-year period 1958-2013 indicate that hot summers have occurred increasingly since the 1990s with daily maximum temperatures reaching 10 oC above normal. The extreme nature of the change in monthly mean maximum temperatures (~1 to 1.5 oC above the long term mean) far exceeds the natural variability expected over a half century. Numerous maximum temperature records have been set and the extreme heat poses a major socioeconomic threat. This work examines changes in mean values of maximum daily temperatures for each summer month, in southeastern Australia. A 10-site dataset, for 1958-2013, was drawn and resampled to quantify temporal changes and uncertainty in decadal monthly maximum temperatures. Resampling methods documented the historical uniqueness of the maximum temperatures in recent decades. Results suggest strongly that, in recent decades, the maximum temperatures exceeding the upper quartile of the historical data is greater than expected by random chance. The findings confirm the regional nature of the warming. The increase in summer temperature is partly related to changes in atmospheric blocking. © 2013 The Authors. Published by Elsevier B.V

Attribution and prediction of maximum temperature extremes in SE Australia

© 2014 Published by Elsevier B.V. Over half of Australia's population occupy its southeastern quadrant. Temperature records for the 56-year period 1958-2013 reveal increasingly hot summers since the 1990s, with daily maximum temperatures reaching 10 °C above normal. The change in monthly mean maximum temperatures (∼1 °C to 1.5 °C above the long term mean) far exceeds the natural variability expected over a half-century. Numerous maximum temperature records have been set and the extreme heat poses a major socioeconomic threat. This work seeks climate drivers that are useful predictors of the warm mean monthly values of maximum daily temperatures for January, in southeastern Australia. The data for January 1958-2013 from one representative site, Tibooburra, is coded, in a binary sense (excessive heat-yes/no), and for actual temperature anomalies. One challenge in analyzing these data is the short records relative to the numerous possible climate drivers of excessive heat. The variables are a combination of ocean and atmospheric climate drivers plus their high and low frequency filtered values from wavelet analysis. Several feature selection methods are applied to produce a compact set of predictors exhibiting good generalization properties. Results of cross-validation of logistic regression, with and without threshold adjustment, show that cold air blocking, and teleconnection patterns, such as the Southern Annular Mode (SAM), have statistical skill (best classification Heidke skill score = 0.34) in forecasting extreme heat for binary forecasts, with correct forecasts exceeding 75% of cases. For predicting actual monthly anomalies, support vector regression and bagged trees explain anomaly temperatures with mean absolute error of 1.4 °C and 1.3 °C

Adaptive machine learning approaches to seasonal prediction of tropical cyclones

Tropical cyclones (TCs) are devastating phenomena that cause loss of life and catastrophic damage, owing to destructive winds, flooding rains and coastal inundation from storm surges. Accurate seasonal predictions of TC frequency and intensity are required, with a lead-time appropriate for preemptive action. Current schemes rely on linear statistics to generate forecasts of the TC activity for an upcoming season. Such techniques employ a suite of intercorrelated predictors; however, the relationships between predictors and TCs violate assumptions of standard prediction techniques. We extend tradition linear approaches, implementing support vector regression (SVR) models. Multiple linear regression (MLR) is used to create a baseline to assess SVR performance. Nine predictors for each calendar month (108 total) were inputs to MLR. MLR equations were unstable, owing to collinearity, requiring variable selection. Stepwise multiple regression was used to select a subset of three attributes adaptive to specific climatological variability. The R2 for the MLR testing data was 0.182. The SVR model used the same predictors with a radial basis function kernel to extend the traditional linear approach. Results of that model had an R2 of 0.255 (∼ 40% improvement over linear model). Refinement of the SVR to include the Quasi-Biennial Oscillation (QBO) improved the SVR predictions dramatically with an R2 of 0.564 (∼ 121% improvement over SVR without QBO). © 2012 Published by Elsevier B.V

Uniqueness and Causes of the California Drought

© 2015 The Authors. Published by Elsevier B.V. The current California drought, which is part of the abnormal to extreme drought conditions affecting much of southwest USA, has lasted for 4 years (2011/12 - 2014/15). It has intensified steadily to what at present is likely the worst Californian drought since reliable instrumental records began in 1895. The uniqueness of this drought is demonstrated by assessing the Oct. - Mar. wet seasons for instances 75th percentile in average temperature. Of the 8 seasons since 1895 that met these percentile conditions, only the present drought satisfied these criteria for more than one season. Predictions of California precipitation and temperature anomalies were made using linear regression (LR), and support vector regression (SVR) with several linear and non-linear kernels, applied to a range of climate drivers and local sea surface temperatures (SSTs). Cross-validated correlations were low (LR) to moderate (SVR) for precipitation, but were high (>0.7) for both temperature LR and SVR, with SVR marginally exceeding LR. The leading predictors were global warming and local SSTs near the California coast. Finally, the cool seasons were classified as dry/not-dry and hot/not-hot using logistic regressions and k-means classification clustering. Again, it was found that predictability was low for dry/not-dry classes but was high (>70% correct) for hot/not-hot classes. This research suggests that the climate system has warmed sufficiently so that drought can no longer be assessed solely by the lack of precipitation, but must consider the combination of low precipitation and abnormal warmth

The modulating influence of Indian Ocean sea surface temperatures on Australian region seasonal tropical cyclone counts

© 2017 American Meteorological Society. The Australian region seasonal tropical cyclone count (TCC) maintained a robust statistical relationship with El Niño-Southern Oscillation (ENSO), with skillful forecasts of above (below) average TCC during La Niña (El Niño) years from 1969 until about 1998, weakening thereafter. The current study identifies an additional climate driver that mitigates the loss of predictive skill for Australian TCC after about 1998. It is found that the seasonal Australian TCC is strongly modulated by a southwest-to-northeast-oriented dipole in Indian Ocean sea surface temperature anomalies (SSTAs), referred to here as the transverse Indian Ocean dipole (TIOD). The TIOD emerges as the leading mode of detrended Indian Ocean SSTAs in the Southern Hemisphere during late winter and spring. Active (inactive) TC seasons are linked to positive (negative) TIOD phases, most notably during August-October immediately preceding the TC season, when SSTAs northwest of Australia, in the northeast pole of the TIOD, are positive (negative). To provide a physical interpretation of the TIOD-TCC relationship, 850-hPa zonal winds, 850-hPa relative vorticity, and 600-hPa relative humidity are composited for positive and negative TIOD phases, providing results consistent with observed TCC modulation. Correlations between ENSO and TCC weaken from 1998 onward, becoming statistically insignificant, whereas the TIOD-TCC correlation remains statistically significant until 2003. Overall, TIOD outperforms Niño-4 SSTA as a TCC predictor (46% skill increase since about 1998), when used individually or with Niño-4. The combination of TIOD and Niño 4 provide a skill increase (up to 33%) over climatology, demonstrating reliably accurate seasonal predictions of Australian region TCC

Classifying Drought in Ethiopia Using Machine Learning

© 2016 The Authors. This study applies machine learning to the rapidly growing societal problem of drought. Severe drought exists in Ethiopia with crop failures affecting about 90 million people. The Ethiopian famine of 1983-85 caused a loss of ∼400,000-1,000,000 lives. The present drought was triggered by low precipitation associated with the current El Niño and long-term warming, enhancing the potential for a catastrophe. In this study, the roles of temperature, precipitation and El Niño are examined to characterize both the current and previous droughts. Variable selection, using genetic algorithms with 10-fold cross-validation, was used to reduce a large number of potential predictors (27) to a manageable set (7). Variables present in ≥ 70% of the folds were retained to classify drought (no drought). Logistic regression and Primal Estimated sub-GrAdient SOlver for SVM (Pegasos) using both hinge and log cost functions, were used to classify drought. Logistic regression (Pegasos) produced correct classifications for 81.14% (83.44%) of the years tested. The variable weights suggest that El Niño plays an important role but, since the region has undergone a steady warming trend of ∼1.6°C since the 1950s, the larger weights associated with positive temperature anomalies are critical for correct classification

Seasonal-to-interannual variability of ethiopia/horn of Africa monsoon. Part II: Statistical multimodel ensemble rainfall predictions

© 2015 American Meteorological Society. An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June-September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble. For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed-predicted correlation r (SSClim) being +0.81 (65%) for 1970-2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society

Cluster analysis of North Atlantic tropical cyclones

© 2014 Published by Elsevier B.V. Tropical cyclones (TCs) in the North Atlantic (NA) basin pose an annual risk to coastal regions, with hurricane Katrina (2005) the costliest TC in US history. This study employs K-means cluster analysis (CA) to detect the distinctive, important NA TC paths and lifecycles. Unlike previous TC cluster analyses, which examined TC tracks, the present work documents TC genesis and decay locations. Application of the silhouette coefficient provided an objective method to determine the optimal number of clusters (7 for genesis locations, 6 for preferred tracks, and 5 for decay locations). Additionally, silhouette coefficients provided the information necessary to remove storms that did not fit specific clusters, improving cluster cohesiveness. For TC genesis, K-means CA captured the separation between tropical and higher-latitude TCs. Clustering of genesis points identifies formative areas. The western NA cluster is the most active. TCs have distinct decay locations, notably in the western NA, Gulf of Mexico and western Caribbean Sea. Clustering TC tracks reveals that TCs moving to higher latitudes recurve generally, whereas Caribbean and Gulf coast TCs have straight-line tracks. Temporally, early season TC clusters form in the western Caribbean Sea, Gulf of Mexico, and western NA. Midseason TC clusters shift eastward, extending from the tropical NA to Africa. Late season TC clusters recur in the Caribbean Sea, Gulf of Mexico and western NA

Synoptic composites of tornadic and nontornadic outbreaks

Tornadic and nontornadic outbreaks occur within the United States and elsewhere around the world each year with devastating effect. However, few studies have considered the physical differences between these two outbreak types. To address this issue, synoptic-scale pattern composites of tornadic and nontornadic outbreaks are formulated over North America using a rotated principal component analysis (RPCA). A cluster analysis of the RPC loadings group similar outbreak events, and the resulting map types represent an idealized composite of the constituent cases in each cluster. These composites are used to initialize aWeather Research and Forecasting Model (WRF) simulation of each hypothetical composite outbreak type in an effort to determine the WRF's capability to distinguish the outbreak type each composite represents. Synoptic-scale pattern analyses of the composites reveal strikingly different characteristics within each outbreak type, particularly in the wind fields. The tornado outbreak composites reveal a strong low- and midlevel cyclone over the eastern Rockies, which is likely responsible for the observed surface low pressure system in the plains. Composite soundings from the hypothetical outbreak centroids reveal significantly greater bulk shear and storm-relative environmental helicity values in the tornado outbreak environment, whereas instability fields are similar between the two outbreak types. The WRF simulations of the map types confirm results observed in the composite soundings. © 2012 American Meteorological Society

An assessment of areal coverage of severe weather parameters for severe weather outbreak diagnosis

The areal extent of severeweather parameters favorable for significant severeweather is evaluated as a means of identifying major severe weather outbreaks. The first areal coverage method uses kernel density estimation (KDE) to identify severeweather outbreak locations. Aselected severeweather parameter value is computed at each grid point within the region identified by KDE. The average, median, or sum value is used to diagnose the event's severity. The second areal coverage method finds the largest contiguous region where a severe weather parameter exceeds a specified threshold that intersects theKDEregion. The severeweather parameter values at grid points within the parameter exceedance region are computed, with the average, median, or sumvalue used to diagnose the event's severity. A total of 4057 severe weather outbreaks from 1979 to 2008 are analyzed. An event is considered a major outbreak if it exceeds a selected ranking index score (developed in previous work), and is a minor event otherwise. The areal coverage method is also compared to Storm Prediction Center (SPC) day-1 convective outlooks from 2003 to 2008. Comparisons of the SPC forecasts and areal coverage diagnoses indicate the areal coverage methods have similar skill to SPC convective outlooks in discriminating major and minor severe weather outbreaks. Despite a seemingly large sample size, the rare-events nature of the dataset leads to sample size sensitivities. Nevertheless, the findings of this study suggest that areal coverage should be tested in a forecasting environment as a means of providing guidance in future outbreak scenarios. © 2012 American Meteorological Society