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A procedure for correcting systematic biases across multiple variables is presented. This procedure operates in the frequency domain, using the cross-spectrum across variables to correct bias across each frequency band. The proposed approach is termed multivariate frequency bias correction or MFBC. The approach is illustrated using global climate model (GCM) simulations of multiple atmospheric variables, with variables selected based on recommended usage in downscaling applications. Results indicate clear benefits of using MFBC in representing both intra- and inter-variable dependence in corrected simulations. This has important implications in applications which require multiple atmospheric variables, and a need to correctly simulate both inter- and intra-variable dependence attributes. MFBC offers a mean to correct raw GCM atmospheric variables prior to downscaling or correct dynamically or statistically downscaled simulations prior to derived simulations of other variables of interest. Use of MFBC can have significant implications on derived hydrologic simulations, such as in sizing of storage reservoirs, or devising water sharing plans for the future. [ABSTRACT FROM AUTHOR]

This work investigates the performance of four bias correction alternatives for representing persistence characteristics of precipitation across 37 General Circulation Models (GCMs) from the CMIP5 data archive. The first three correction approaches are the Simple Monthly Bias Correction (SMBC), Equidistance Quantile Mapping (EQM), and Nested Bias Correction (NBC), all of which operate in the time domain, with a focus on representing distributional and moment attributes in the observed precipitation record. The fourth approach corrects for the biases in high- and low-frequency variability or persistence of the GCM time series in the frequency domain and is named as Frequency-based Bias Correction (FBC). The Climatic Research Unit (CRU) gridded precipitation data covering the global land surface is used as a reference dataset. The assessment focusses on current and future means, variability, and drought-related characteristics at different temporal and spatial scales. For the current climate, all bias correction approaches perform reasonably well at the global scale by reproducing the observed precipitation statistics. For the future climate, focus is drawn on the agreement of the attributes across the GCMs considered. The inter-model difference/spread of each attribute across the GCMs is used as a measure of this agreement. Our results indicate that out of the four bias correction approaches used, FBC provides the lowest inter-model spreads, specifically for persistence attributes, over most regions/ parts over the global land surface. This has significant implications for most hydrological studies where the effect of low-frequency variability is of considerable importance. [ABSTRACT FROM AUTHOR]

Summary Bias correction is considered as a critical post-processing step to remove systematic errors and improve the quality of General Circulation Model (GCM) simulations before their use in climate change impact assessment applications. A majority of the bias correction approaches correct for biases either at a single time scale or at multiple pre-specified time scales. An inappropriate or insufficient selection of time scales may lead to improper or sub-optimal bias corrected outputs, especially when persistence attributes across a range of scales are of interest. In this paper, we present a new bias correction approach that works in the frequency space and is independent of specific time scales. The approach is named as frequency-based bias correction (FBC). The usefulness of the approach is demonstrated by applying it to the monthly rainfall simulations of MIROC5 GCM over Australia and comparing the results with two other approaches, namely, empirical quantile mapping and recursive nesting bias correction, in cross validation. The comparison is based on the reproduction of various observed distribution and persistence attributes. Cross-validation results indicate that the proposed approach shows similar performance in terms of reproducing the first- and second-order moments of observed precipitation time series, however, outperforms with regard to persistence attributes. The approach shows high potential for use in downscaling and other climate change impact assessment studies, especially for the planning and design of hydrologic systems that are sensitive to the characterisation of persistence in the hydrologic time series. [ABSTRACT FROM AUTHOR]

• Highlights the limitation of existing NFFA approaches in differentiating the mechanism of frequent versus rare flood quantiles. • Proposes a novel NRFFA approach to capture the variation of flood quantiles with different return periods in a changing climate. • Use regional information to ascertain the design flood quantiles and provide plausible changes in design flood extremes in the year 2100. There now exists clear evidence of the impact of climate change on flooding, creating need for new methodologies for design flood estimation that account for warming induced nonstationarity. Current alternatives for nonstationary flood frequency analysis require the specification of a nonstationary probability model (often with time-varying parameters) that requires sufficient data to be stable. Reliance on the trend in observed data often ignores the dissimilar behaviour of frequent and rare quantiles under a warming climate. This, in turn, results in the direction of change to be consistent across both rare and frequent projected flood quantiles. However, results of multiple recent studies suggest that rarer floods are likely to increase while more frequent floods may decrease in magnitude into the future. In this study, we highlight this limitation of existing nonstationary flood frequency approaches and propose a novel nonstationary regional flood frequency analysis approach that captures the differing behaviour of more frequent and rare flood quantiles under a warming climate. The need for longer flood data to model nonstationarity is accommodated by pooling regional information which makes the projections more precise. Data for 105 Australian catchments are used to validate our proposed approach. Results show the effectiveness of the proposed method in capturing the variation of flood quantiles with varying average recurrence intervals in a changing climate. Finally, a few important statistics of future projections are also presented. [ABSTRACT FROM AUTHOR]

• Highlights the limitation of existing NFFA approaches in differentiating the mechanism of frequent versus rare flood quantiles. • Proposes a novel NRFFA approach to capture the variation of flood quantiles with different return periods in a changing climate. • Use regional information to ascertain the design flood quantiles and provide plausible changes in design flood extremes in the year 2100. There now exists clear evidence of the impact of climate change on flooding, creating need for new methodologies for design flood estimation that account for warming induced nonstationarity. Current alternatives for nonstationary flood frequency analysis require the specification of a nonstationary probability model (often with time-varying parameters) that requires sufficient data to be stable. Reliance on the trend in observed data often ignores the dissimilar behaviour of frequent and rare quantiles under a warming climate. This, in turn, results in the direction of change to be consistent across both rare and frequent projected flood quantiles. However, results of multiple recent studies suggest that rarer floods are likely to increase while more frequent floods may decrease in magnitude into the future. In this study, we highlight this limitation of existing nonstationary flood frequency approaches and propose a novel nonstationary regional flood frequency analysis approach that captures the differing behaviour of more frequent and rare flood quantiles under a warming climate. The need for longer flood data to model nonstationarity is accommodated by pooling regional information which makes the projections more precise. Data for 105 Australian catchments are used to validate our proposed approach. Results show the effectiveness of the proposed method in capturing the variation of flood quantiles with varying average recurrence intervals in a changing climate. Finally, a few important statistics of future projections are also presented. [ABSTRACT FROM AUTHOR]

A novel multivariate quantile-matching nesting bias correction approach is developed to remove systematic biases in general circulation model (GCM) outputs over multiple time scales. This is a significant advancement over typical quantile-matching alternatives available for bias correction, as they implicitly assume that correction of individual variable attributes will lead to correction of dependence biases between multiple variables. Furthermore, existing approaches perform bias correction at a given time scale (e.g., daily), whereas applications often require biases to be addressed at more than one time scale (such as annual in the case of most water resources planning projects). The proposed approach addresses all these issues, and additionally attempts to correct for lag-1 dependence (and cross-dependence) attributes across multiple time scales. The approach is called multivariate recursive quantile nesting bias correction (MRQNBC). The fidelity of the approach is demonstrated by applying it to a vector of CSIRO Mk3 GCM atmospheric variables and comparing the results with the commonly used quantile-matching approach. Following this, the implications of the approach in hydrology- and water resources-related applications are demonstrated by feeding the bias-corrected data to a rainfall downscaling model and comparing the downscaled rainfall attributes for current and future climate. The proposed approach is shown to represent the variability and persistence related attributes better and can thus be expected to have important consequences for the simulation of occurrence and intensity of extreme events such as floods and droughts in downscaled simulations, of importance in various climate impact assessment applications. [ABSTRACT FROM AUTHOR]

Summary Climate change has the potential to intensify the hydrological cycle, leading to more intense precipitation with associated changes in the intensity, frequency and severity of floods. Climate variability and change beyond a few years to a few decades ahead have significant social, economic, and environmental implications. It is believed that some aspects of this decadal variability could be predictable for a decade or longer in advance. Keeping this in mind, phase five of the Coupled Model Intercomparison Project (CMIP5), for the first time, provides 10–30 years predictions obtained from the General Circulation Models (GCMs). This study aims to analyse the CMIP5 decadal predictions for precipitation over five sub-basins of river Brahmaputra. Daily precipitation data of five GCMs, namely F-GOALS-g2, BCC-CSM1-1, IPSL-CM5A, CanCM4 and MRI-CGCM3 are used for this assessment. Empirical relationships between the basin averaged rainfall wet spell (storm) properties and the characteristics of the floods are formulated for storms which lead to significant short-term flood response. Following this, the changes in the flood behaviour in the future are derived on the basis of changes in the characteristics of wet rainfall spells in 2010–2020. The results suggest an increase in the number of spells with higher rainfall and longer duration which can lead to increase in peak flood and the total flood volume. [ABSTRACT FROM AUTHOR]

Many problems in hydrology and agricultural science require extensive records of rainfall from multiple locations. Temporal and/or spatial coverage of rainfall data is often limited, so that stochastic models may be required to generate long synthetic rainfall records. This study describes a multi-site rainfall simulator (MRS) to stochastically generate daily rainfall at multiple locations. The MRS is available as an open-source software package in the R statistical computing environment. The software includes statistical analysis and graphics functions, and can display statistics and graphs at multiple time scales, including from individual sites and areal averages. The MRS thus provides a detailed set of modelling functions to simulate and analyse daily rainfall. The capabilities of the package are demonstrated using 30 gauges located in Sydney, Australia, and the results show that the model preserves observed year-to-year variability, interannual persistence and various daily distributional and space–time dependence attributes. [ABSTRACT FROM AUTHOR]

Summary Many hydro-climatological applications require use of General Circulation Models (GCMs) outputs. However, the raw information as available from GCMs often contains significant systematic biases when compared with observations. This necessitates some kind of statistical adjustment to be carried out on the GCM fields before their use in any application. It is common to correct the GCM simulations by removing the systematic biases at the time scale of interest, usually individually for each GCM variable that is needed. The outcome is a set of bias corrected variables that are not assessed for bias in their joint dependence structure, nor biases in all attributes at other time scales (such as daily and monthly and annual) except the one under consideration. In this paper, we present a bias correction approach that simultaneously adjusts for the biases in multiple variables across multiple time scales. The proposed Multivariate Recursive Nesting Bias Correction (MRNBC) approach simultaneously corrects many GCM variables and repeats the procedure across different levels of temporal aggregation to impart observed distributional and persistence properties at multiple time scales. The bias corrected series exhibits improvements across all variables and over all the time scales considered. The use of the approach in hydrology and water resources related downscaling applications is expected to deliver better results. [ABSTRACT FROM AUTHOR]

Summary This study explores the potential skill of Coupled Model Intercomparison Project (CMIP5) decadal hindcasts from 9 GCMs and their ensembles over the period 1960–2010 from a hydrological perspective. This assessment is first conducted using GCM derived rainfall and subsequently extended to the other climate variables such as air temperature and geopotential height, with the intention of assessing the possible benefit of using these variables for formulation of appropriate precipitation modelling alternatives. A total of 78 ensemble members for rainfall, and 22 ensemble members for air temperature and geopotential height, are used in performing this decadal predictability assessment. The results suggest that while precipitation predictions show very limited skill when assessed at annual and multi-annual time-scales, air temperature and geopotential height exhibit greater predictive skill. The major part of the predictability of these variables comes from the long term trend. However, the predictability is not enough to drive impact models at decadal timescales and to influence the policy and decision making. The results indicate possible benefits of using a suitably structured modelling approach to derive precipitation using atmospheric predictors exhibiting better skills than the raw simulated rainfall. [ABSTRACT FROM AUTHOR]

With the availability of hindcasts or real-time forecasts from a number of coupled climate models, multimodel ensemble forecasting systems have gained popularity in recent years. However, many models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Assigning equal weights to each model in space and time may result in a biased forecast with narrower confidence limits than is appropriate. Although methods for combining forecasts that take into consideration differences in model accuracy over space and time exist, they suffer from a lack of consideration of the intermodel dependence that may exist. This study proposes an approach that considers the dependence among models while combining multimodel ensemble forecast. The approach is evaluated by combining sea surface temperature (SST) forecasts from five climate models for the period 1960-2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude-longitude grid, is predicted three months in advance using the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for all the seasons over the majority of grid points compared to the case in which the dependence among the models is ignored. Consequently, the proposed approach of combining multiple models, taking into account the interdependence that exists, provides an attractive strategy to develop improved SST forecasts. [ABSTRACT FROM AUTHOR]

Systematic biases in General Circulation Model (GCM) simulations require some adjustment before their use in change assessment and adaptation management studies. GCM simulations of the Coupled Model Intercomparison Project 6, although outperform the previous generations of GCMs, exhibit persistent biases in magnitude, variability, and frequency across a range of variables of interest. Here, we propose a novel continuous wavelet‐based bias correction (CWBC) approach to address such biases in the time‐frequency domain. The correction focuses on the magnitude and frequency of the modeled time series, as interpreted via the time‐varying spectrum ascertained using the continuous wavelet transform. The approach is applied to correct systematic biases in the time series of Niño 3.4 sea surface temperature and Arctic sea‐ice extent. The application of CWBC successfully reproduces observed attributes in the bias‐corrected time series of both climate variables for the current climate simulation along with providing a sensible projection for the future. Plain Language Summary: The latest generation of General Circulation Models outperforms older versions in simulating the current and future climate. However, biases in magnitude, frequency, variability, and persistence related characteristics still remain in model simulations. The nested time based and other frequency analysis‐based bias correction approaches, for example, a fast Fourier transform or a discrete wavelet‐based bias correction, offer a good alternative to correct the biases. However, they cannot capture the underlying components of climate variables in the non‐dyadic spectrum. Keeping this in mind, we present here a continuous wavelet transform based bias correction approach to obtain the underlying information of climate variables at a finer spectrum resolution. The applications of continuous wavelet‐based bias correction algorithm show remarkable improvement in the amplitude and frequency of two example datasets, namely, a Coupled Model Intercomparison Project 6 (CMIP6) sea surface temperature simulation and the ensemble mean of CMIP5 Arctic sea‐ice extent simulations, for the current climate and more sensible correction for future climate simulations. Key Points: Systematic biases exist in the magnitude and frequency of climate events simulated by the modelsA continuous wavelet transform‐based method for identifying and correcting biases in the time‐frequency domain is presentedTime series of Niño 3.4 sea surface temperature and Arctic sea‐ice extent is used to demonstrate the usefulness of the approach [ABSTRACT FROM AUTHOR]

• A new basis for presenting illustrations of spatial dependence related to extreme rain. • Extreme rainfall spatial connectivity increases under ENSO phases. • RC weakens at warmer temperatures, with localized lower relative connectivity. The frequency and severity of extreme weather events have been noted to be changing in both time and space as a result of rising global temperatures. In this regard, the analysis of joint occurrences of extreme climate events has gained considerable importance for planning and emergency management as such events often result in system failures that extend beyond a single location in the region impacted. By their very definition, extremes cannot be sampled frequently, necessitating approaches that are robust and efficient and can take advantage of the Big Data movement we are now a part of. This study introduces a new concept for characterizing such dependence, termed relative connectivity, formulated using the theory of empirical copulas which represent a non-parametric method to describe the joint dependence of random variables. The spatial relative connectivity of extreme rainfall events across 2,708 rainfall stations in Australia is ascertained and conclusions drawn. The extreme rainfall events in Western Australia, southeast South Australia, and southwest Victoria exhibit higher relative connectivity than other regions, suggesting a stronger spatial dependence and higher possibility of concurrence when compared to other regions. The overall variation of relative connectivity is also examined under different low-frequency climatic anomalies (i.e. El Nino and La Nina events) and temperature states. We observe that spatial dependence across raingauges increases under both El Nino and La Nina periods, while it weakens at warmer temperatures, with localized lower relative connectivity at target raingauges. [ABSTRACT FROM AUTHOR]

Bias correction of General Circulation Model (GCM) is now an essential part of climate change studies. However, the climate change trend has been overlooked in majority of bias correction approaches. Here, a novel signal processing‐based approach for correcting systematic biases in the time‐varying trend of GCM simulations is proposed. The approach corrects for systematic deviations in spectral attributes of raw GCM simulations using discrete wavelet transforms. The order one and two moments of the underlying trend represented by the lowest frequency of wavelet component are corrected to ensure continuity in the corrected time series from the current to the future simulation period. The approach is applied to correct two data sets that exhibit opposite time‐varying trends representing the global mean sea level (GMSL) and the Arctic sea‐ice extent. Results indicate that bias in trend is corrected, while continuity in time and observed variability at all frequencies in current climate simulations are maintained. Plain Language Summary: GCMs are an essential tool to assess climate change impacts. However, they exhibit systematic biases which restrict their direct use. A wide range of alternatives for correcting biases has been proposed. Most such approaches are unable to correct biases in trend and variability attributes together. When a variability correction is applied, it reduces biases in trend, however, introduces a discontinuity in trend between current and future climate bias‐corrected simulations. We present an approach for correcting systematic biases in trend and variability to ensure continuity of GCM simulations from the current to the future. The application of our approach to two data sets exhibiting opposite trends ‐ the GMSL and the Arctic sea ice extent ‐ shows a remarkable improvement that maintains continuity overtime, corrects bias in trends, and preserves observed variability across the frequency spectrum in current period GCM simulations. Key Points: Tradition alternatives for bias correction distort continuity in corrected seriesA discrete wavelet transform based signal processing alternative for correcting trend and variability bias is proposedThe approach is demonstrated to correct trends in global mean sea level and Arctic sea ice extent simulations for the future [ABSTRACT FROM AUTHOR]

Bias correction of General Circulation Model (GCM) is now an essential part of climate change studies. However, the climate change trend has been overlooked in majority of bias correction approaches. Here, a novel signal processing‐based approach for correcting systematic biases in the time‐varying trend of GCM simulations is proposed. The approach corrects for systematic deviations in spectral attributes of raw GCM simulations using discrete wavelet transforms. The order one and two moments of the underlying trend represented by the lowest frequency of wavelet component are corrected to ensure continuity in the corrected time series from the current to the future simulation period. The approach is applied to correct two data sets that exhibit opposite time‐varying trends representing the global mean sea level (GMSL) and the Arctic sea‐ice extent. Results indicate that bias in trend is corrected, while continuity in time and observed variability at all frequencies in current climate simulations are maintained. Plain Language Summary: GCMs are an essential tool to assess climate change impacts. However, they exhibit systematic biases which restrict their direct use. A wide range of alternatives for correcting biases has been proposed. Most such approaches are unable to correct biases in trend and variability attributes together. When a variability correction is applied, it reduces biases in trend, however, introduces a discontinuity in trend between current and future climate bias‐corrected simulations. We present an approach for correcting systematic biases in trend and variability to ensure continuity of GCM simulations from the current to the future. The application of our approach to two data sets exhibiting opposite trends ‐ the GMSL and the Arctic sea ice extent ‐ shows a remarkable improvement that maintains continuity overtime, corrects bias in trends, and preserves observed variability across the frequency spectrum in current period GCM simulations. Key Points: Tradition alternatives for bias correction distort continuity in corrected seriesA discrete wavelet transform based signal processing alternative for correcting trend and variability bias is proposedThe approach is demonstrated to correct trends in global mean sea level and Arctic sea ice extent simulations for the future [ABSTRACT FROM AUTHOR]

One way to reduce model uncertainty in climate predictions is to combine forecasts from several models. Recent multi‐model combination approaches combine model forecasts by pooling data for a time period, common across all the models, thus ignoring the additional data available or discarding altogether the models with the shorter time period. This results in the loss of some information which could otherwise be used while combining the models to possibly improve forecast skill. Our research explores this issue in the context of multi‐model sea surface temperature (SST) models predictions and proposes a novel concept that allows a framework for combining models with unequal time period. Here, the unequal time periods imply different range of start and end dates of available model forecasts. A qualitative standpoint of our multi‐model forecasting strategy is to reduce the uncertainty and improve the forecast skill. The utility of the approach is demonstrated by combining the global seasonal NDJ (November–January) SST predictions of two models and also as many as eight models, obtained using both equal and unequal time periods. The proposed approach shows improvement over 62–69% grid cells around the entire globe over the case when the common period of data length across the models is considered. [ABSTRACT FROM AUTHOR]

Summary: This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems. [Copyright &y& Elsevier]

Summary: This paper evaluates three distinct approaches for disaggregating daily rainfall to sub-daily sequences: (1) random multiplicative cascades (microcanonical and canonical versions), (2) point process (randomized Bartlett–Lewis model – RBLM), and (3) resampling (method of fragments). These methods are used to perform disaggregation of daily rainfall to hourly rainfall at four point locations across Australia (Sydney, Perth, Cairns, and Hobart), which are associated with different climatic regimes. The methods are evaluated based on parameter estimation procedures applied (including introduction of the sequential Monte Carlo sampler in RBLM), the capability of the resulting sequences to reproduce standard validation statistics, and the representation of observed rainfall variability and intermittency, within-day wet spells, and extreme rainfall percentiles. The results generally indicate that the method of fragments outperforms the other models. While all the models are found to simulate reasonably well the commonly used statistical measures (e.g. mean and dry proportions) of rainfall at the hourly timestep, the microcanonical model is found to significantly overestimate the hourly rainfall variance. With respect to extreme value characteristics, the resampling approach is found to match well the observed intensity–frequency relationship at an hourly scale, with the cascade models underestimating (canonical) and overestimating (microcanonical) extreme rainfall. The point process model’s performance is poor in Cairns but reasonably good at other locations. An analysis of the empirical within-day wet- and dry-spell distributions further reveals that the cascade-based models are not robust for observed wet and dry spells. [Copyright &y& Elsevier]

The occurrence of algal bloom results in deterioration of water quality, undesirable sights, tastes and odors, and the possibility of infections to humans and fatalities to livestock, wildlife and pets. Earlier studies have identified a range of factors including water temperature, flow, and nutrient concentrations that could affect cyanobacterial proliferation. Lack of enough data, independence in data across multiple sampling time steps, as well as the presence of more than one causative factors, each with different levels of influence on the response, has resulted in limited progress in the development of generalized prediction frameworks for cyanobacteria. In this study, a prediction model for cyanobacteria occurrences was developed using only three dominant environmental variables; water temperature, velocity and phosphorus concentration. These environmental variables were selected due to not only direct or joint contribution to algal bloom but also the ease of their availability either through direct measurements or as modelled responses in the river location of interest. In order to apply bacterial growth dynamic to the model, weight functions which quantify the importance assigned to the three variables depending on the cell number at the preceding time, were formulated. An extensive dataset spanning from 2013 to 2018 at 16 representative locations across the four major rivers in South Korea was used to develop and validate the model. Through cross-validation, this model was shown to have more than 75% forecasting accuracy despite the use of a relatively simple predictive algorithm. As the developed model makes use of commonly available environmental variables, it can easily be extended to locations across the country where very limited or no prior information about cyanobacteria bloom is available. • A model developed to predict binary occurrences of cyanobacteria bloom in rivers. • The model uses only water temperature (T), velocity (V) and phosphorus (P). • The model weights T , V and P by the cyanobacteria cell number at the preceding time. • Through cross-validation over South Korea, the model showed encouraging results. [ABSTRACT FROM AUTHOR]

A large-scale mode of sea level pressure variability between the Pacific and Atlantic basins-referred to as the tropical trans-basin variability (TBV)-has shown multi-year predictability beyond that normally associated with El Niño Southern Oscillation (ENSO). Here we examine the relationship between the TBV and regional rainfall, which, if significant, would imply that the TBV predictability could be used to improve rainfall forecasts. We find TBV to be significantly related to rainfall, a relationship that is strongest over Australia during spring. We find Australia to experience drier conditions during the positive phase of TBV and wetter conditions during the negative phase, a pattern similar to that of ENSO. Furthermore, TBV is associated with rainfall responses that are of similar magnitude to ENSO, but there is some degree of independence between TBV and the Pacific modes defined by sea surface temperature indices. The global teleconnections related to the different TBV phases suggest that though many TBV and ENSO events overlap, pure ENSO events are primarily driven by the Pacific whereas pure TBV events are driven by the Atlantic Ocean. An El Niño coupled with a positive TBV event leads to significant drying over the east coast of Australia while the combined negative phase leads to widespread wetting over most of the country, which can be attributed to an enhancement of the anomalous Walker Circulation and the resulting moisture convergence and wind patterns. [ABSTRACT FROM AUTHOR]

Lateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias-corrected WRF simulations are, however, found to be marginally better than mean only bias-corrected WRF simulations and raw ERA-I reanalysis-driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted. [ABSTRACT FROM AUTHOR]