Links between central west western australian rainfall variability and large-scale climate drivers

Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western Australia (CWWA), however, has exhibited comparatively much weaker coastal trends, but a more prominent inland increase during the wet summer season. Analysis of seasonally averaged rainfall data from a group of stations, representative of both the coastal and inland regions of CWWA, revealed that rainfall trends during the 1958-2010 period in the wet months of November-April were primarily associated with El Niñ o-Southern Oscillation (ENSO), and with the southern annular mode (SAM) farther inland. During the wet months of May-October, the Indian Ocean dipole (IOD) showed the most robust relationships. Those results hold when the effects of ENSOor IOD are excluded, and were confirmed using a principal component analysis of sea surface temperature (SST) anomalies, rainfall wavelet analyses, and point-by-point correlations of rainfall with global SST anomaly fields. Although speculative, given their long-term averages, reanalysis data suggest that from 1958 to 2010 the increase inCWWAinland rainfall largely is attributable to an increasing cyclonic anomaly trend over CWWA, bringing onshore moist tropical flow to the Pilbara coast. During May-October, the flow anomaly exhibits a transition from an onshore to offshore flow regime in the 2001-10 decade, which is consistent with the observed weaker drying trend during this period. © 2013 American Meteorological Society

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

Defining Philippine Climate Zones Using Surface and High-Resolution Satellite Data

Philippine climate zones traditionally were classified from a rain-gauge network, using the Modified Coronas Classification (MCC). MCC uses average monthly rainfall totals to define four climate zones: Types I-IV. Types I and III have wet and dry seasons, whereas Types II and IV have wet seasons but no dry seasons. The present study redefines Philippine climate zones by applying cluster analysis to the average monthly rainfall amounts from surface-based rain-gauge observations, and dense, high-resolution satellite data from the Tropical Rainfall Monitoring Mission (TRMM). To determine the optimal number of climate type clusters, both single-linkage hierarchical and K-means cluster analysis algorithms were used, together with known characteristics of Philippine rainfall distributions and attributes. Employing single linkage hierarchical and K-means methods in tandem identified six different Philippine climate types, which is two climate types more than the currently accepted MCC climate classification. Due to the far greater number of TRMM observations compared with the rain gauge network, the study provides more clearly defined cluster characteristics including the spatial and temporal variability of climate divisions. This study uses known meteorological factors contributing to the identification of six distinct climate types. This paper is intended to assist agricultural stakeholders with planning and decision-making

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

Weather research and forecasting model simulations of extended warm-season heavy precipitation episode over the US southern great plains: Data assimilation and microphysics sensitivity experiments

This study examines eight microphysics schemes (Lin, WSM5, Eta, WSM6, Goddard, Thompson, WDM5, WDM6) in the Advanced Research Weather Research and Forecasting Model (WRF-ARW) for their reproduction of observed strong convection over the US Southern Great Plains (SGP) for three heavy precipitation events of 27-31 May 2001. It also assesses how observational analysis nudging (OBNUD), threedimensional (3DVAR) and four-dimensional variational (4DVAR) data assimilation (DA) affect simulated cloud properties relative to simulations with no DA (CNTRL). Primary evaluation data were cloud radar reflectivity measurements by the millimetre cloud radar (MMCR) at the Central Facility (CF) of the SGP site of the ARM Climate Research Facility (ACRF). All WRF-ARW microphysics simulations reproduce the intensity and vertical structure of the first two major MMCR-observed storms, although the first simulated storm initiates a few hours earlier than observed. Of three organised convective events, the model best identifies the timing and vertical structure of the second storm more than 50 hours into the simulation. For this wellsimulated cloud structure, simulated reflectivities are close to the observed counterparts in the mid- and upper troposphere, and only overestimate observed cloud radar reflectivity in the lower troposphere by less than 10 dBZ. Based on relative measures of skill, no single microphysics scheme excels in all aspects, although the WDM schemes show much-improved frequency bias scores (FBSs) in the lower troposphere for a range of reflectivity thresholds. The WDM6 scheme has improved FBSs and high simulated-observed reflectivity correlations in the lower troposphere, likely due to its large production of liquid water immediately below the melting level. Of all the DA experiments, 3DVAR has the lowest mean errors (MEs) and root mean-squared errors (RMSEs), although both the 3DVAR and 4DVAR simulations reduced noticeably the MEs for seven of eight microphysics schemes relative to CNTRL. Lower-tropospheric θe and convective available potential energy (CAPE) also are closer to the observations for the 4DVAR than CNTRL simulations. © 2013 Z. T. Segele et al

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

High-resolution modeling of typhoon morakot (2009): Vortex rossby waves and their role in extreme precipitation over Taiwan

A high-resolution nonhydrostatic numerical model, the Advanced Regional Prediction System (ARPS), was used to simulate Typhoon Morakot (2009) as it made landfall over Taiwan, producing record rainfall totals. In particular, the mesoscale structure of the typhoon was investigated, emphasizing its associated deep convection, the development of inner rainbands near the center, and the resultant intense rainfall over western Taiwan. Simulations at 15- and 3-km grid spacing revealed that, following the decay of the initial inner eyewall, a new, much larger eyewall developed as the typhoon made landfall over Taiwan. Relatively large-amplitude wave structures developed in the outer eyewall and are identified as vortex Rossby waves (VRWs), based on the wave characteristics and their similarity to VRWs identified in previous studies. Moderate to strong vertical shear over the typhoon system produced a persistent wavenumber-1 (WN1) asymmetric structure during the landfall period, with upward motion and deep convection in the downshear and downshear-left sides, consistent with earlier studies. This strong asymmetry masks the effects of WN1 VRWs. WN2 and WN3 VRWs apparently are associated with the development of deep convective bands in Morakot's southwestern quadrant. This occurs as the waves move cyclonically into the downshear side of the cyclone. Although the typhoon track and topographic enhancement contribute most to the recordbreaking rainfall totals, the location of the convective bands, and their interaction with the mountainous terrain of Taiwan, also affect the rainfall distribution. Quantitatively, the 3-km ARPS rainfall forecasts are superior to those obtained from coarser-resolution models. © 2013 American Meteorological Society