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Modes of climate variability can drive significant changes to regional climate affecting extremes such as droughts, floods, and bushfires. The need to forecast these extremes and expected future increases in their intensity and frequency motivates a need to better understand the physical processes that connect climate modes to regional precipitation. Focusing on east Australia, where precipitation is driven by multiple interacting climate modes, this study provides a new perspective into the links between large-scale modes of climate variability and precipitation. Using a Lagrangian back-trajectory approach, we examine how El Niño–Southern Oscillation (ENSO) modifies the supply of evaporative moisture for precipitation, and how this is modulated by the Indian Ocean dipole (IOD) and southern annular mode (SAM). We demonstrate that La Niña modifies large-scale moisture transport together with local thermodynamic changes to facilitate local precipitation generation, whereas below-average precipitation during El Niño stems predominantly from increased regional subsidence. These dynamic–thermodynamic processes were often more pronounced during co-occurring La Niña/negative IOD and El Niño/positive IOD periods. As the SAM is less strongly correlated with ENSO, the impact of co-occurring ENSO and SAM largely depended on the state of ENSO. La Niña–related processes were exacerbated when combined with +SAM and dampened when combined with −SAM, and vice versa during El Niño. This new perspective on how interacting climate modes physically influence regional precipitation can help elucidate how model biases affect the simulation of Australian climate, facilitating model improvement and understanding of regional impacts from long-term changes in these modes. Significance Statement How climate modes modulate the oceanic and terrestrial sources of moisture for rainfall in east Australia is investigated. East Australia is wetter during La Niña because more moisture is transported into the region and is more easily turned into rainfall when it arrives, whereas drier conditions during El Niño are because local conditions inhibit the conversion of moisture into rainfall. Distant atmospheric changes over the Indian and Southern Oceans can intensify these changes. Our results can be used to better understand and predict the regional impact of long-term changes in these modes of climate variability, which are potentially altered under climate change.

Improving modeling capacities requires a better understanding of both the physical relationship between the variables and climate models with a higher degree of skill than is currently achieved by Global Climate Models (GCMs). Although Regional Climate Models (RCMs) are commonly used to resolve finer scales, their application is restricted by the inherent systematic biases within the GCM datasets that can be propagated into the RCM simulation through the model input boundaries. Hence, it is advisable to remove the systematic biases in the GCM simulations prior to downscaling, forming improved input boundary conditions for the RCMs. Various mathematical approaches have been formulated to correct such biases. Most of the techniques, however, correct each variable independently leading to physical inconsistencies across the variables in dynamically linked fields. Here, we investigate bias corrections ranging from simple to more complex techniques to correct biases of RCM input boundary conditions. The results show that substantial improvements in model performance are achieved after applying bias correction to the boundaries of RCM. This work identifies that the effectiveness of increasingly sophisticated techniques is able to improve the simulated rainfall characteristics. An RCM with multivariate bias correction, which corrects temporal persistence and inter-variable relationships, better represents extreme events relative to univariate bias correction techniques, which do not account for the physical relationship between the variables.

The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of regional climate model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX domains with few archived simulations and performing quality control. It also addressed the lack of comprehensive documentation by compiling information from all contributing regional models, allowing for an informed use of data. In addition to presenting the worldwide CORDEX dataset, we assess here its consistency for precipitation and temperature by comparing climate change signals in regions with overlapping CORDEX domains, obtaining overall coincident regional climate change signals. The C3S CORDEX dataset has been used for the assessment of regional climate change in the IPCC AR6 (and for the interactive Atlas) and is available through the Copernicus Climate Data Store (CDS)., Bulletin of the American Meteorological Society, 103 (12), ISSN:0003-0007, ISSN:1520-0477

Short-duration rainfall extremes can cause flash flooding with associated impacts. Previous studies of climate impacts on extreme precipitation have focused mainly on daily rain totals. Subdaily extremes are often generated in small areas that can be missed by gauge networks or satellites and are not resolved by climate models. Here, we show a robust positive trend of at least 20% per decade in subhourly extreme rainfall near Sydney, Australia, over 20 years, despite no evidence of trends at hourly or daily scales. This trend is seen consistently in storms tracked using multiple independent ground radars, is consistent with rain-gauge data, and does not appear to be associated with known natural variations. This finding suggests that subhourly rainfall extremes may be increasing substantially faster than those on more widely reported time scales.

Climate change is typically modeled using sophisticated mathematical models (climate models) of physical processes that range in temporal and spatial scales. Multi-model ensemble means of climate models show better correlation with the observations than any of the models separately. Currently, an open research question is how climate models can be combined to create an ensemble mean in an optimal way. We present a novel stochastic approach based on Markov chains to estimate model weights in order to obtain ensemble means. The method was compared to existing alternatives by measuring its performance on training and validation data, as well as model-as-truth experiments. The Markov chain method showed improved performance over those methods when measured by the root mean squared error in validation and comparable performance in model-as-truth experiments. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods that address the issues of finding optimal model weight for constructing ensemble means.

Violent fire-driven convection can manifest as towering pyrocumulus (pyroCu) or pyrocumulonimbus (pyroCb) clouds, which can have devastating impacts on the environment and society. Their associated fire spread is erratic, unpredictable and not generally suppressible. Research into large pyroconvective events has mainly focused on the atmospheric processes involved in normal atmospheric convection, or on surface fire weather and associated fuel conditions. There has been comparatively less attention paid to the role of the fire itself in these coupled fire–atmosphere events. This paper draws on recent insights into dynamic fire propagation and extreme wildfire development to investigate how the fire influences the occurrence of violent pyroconvective events. A static heat source of variable dimension and intensity is used. This is accompanied by a companion paper that extends the analysis by including the effect of fire geometry on the pyroconvective plume. The analyses indicate that the spatial expanse and intensity of large fires are critical factors driving the development of pyroconvective plumes and can override the influence of the stability of the atmosphere. These findings provide motivation for further investigation into the effect of the fire’s attributes on the immediate atmosphere and have the potential to improve forecasting of blow-up fire events.

Fire spread associated with violent pyrogenic convection is highly unpredictable and difficult to suppress. Wildfire-driven convection may generate cumulonimbus (storm) clouds, also known as pyrocumulonimbus (pyroCb). Research into such phenomena has tended to treat the fire on the surface and convection in the atmosphere above as separate processes. We used a numerical model to examine the effect of fire geometry on the height of a pyroconvective plume, using idealised model runs in a neutral atmosphere. The role of geometry was investigated because large areal fires have been associated with the development of pyroCb. Complementary results (detailed in Part I) are extended by considering the effect that fire shape can have on plume height by comparing circular, square, and rectangular fires of varying length and width, representing the difference between firelines and areal fires. Results reveal that the perimeter/area ratio influenced the amount of entrainment that the plume experiences and therefore the height to which the plume rises before it loses buoyancy. These results will aid in the prediction of blow-up fires (whereby a fire exhibits a rapid increase in rate of spread or rate of spread) and may therefore be useful in determining where fire agencies deploy their limited resources.

The relative importance of atmospheric advection and local land–atmosphere coupling to Australian precipitation is uncertain. Identifying the evaporative source regions and level of precipitation recycling can help quantify the importance of local and remote marine and terrestrial moisture to precipitation within the different hydroclimates across Australia. Using a three-dimensional Lagrangian back-trajectory approach, moisture from precipitation events across Australia during 1979–2013 was tracked to determine the source of moisture (the evaporative origin) and level of precipitation recycling. We show that source regions vary markedly for precipitation falling in different regions. Advected marine moisture was relatively more important than terrestrial contributions for precipitation in all regions and seasons. For Australia as a whole, contributions from precipitation recycling varied from ~11% in winter up to ~21% in summer. The strongest land–atmosphere coupling was in the northwest and southeast where recycled local land evapotranspiration accounted for an average of 9% of warm-season precipitation. Marine contributions to precipitation in the northwest of Australia increased in spring and, coupled with positive evaporation trends in the key source regions, suggest that the observed precipitation increase is the result of intensified evaporation in the Maritime Continent and Indian and Pacific Oceans. Less clear were the processes behind an observed shift in moisture contribution from winter to summer in southeastern Australia. Establishing the climatological source regions and the magnitude of moisture recycling enables future investigation of anomalous precipitation during extreme periods and provides further insight into the processes driving Australia’s variable precipitation.

A new regional climate projection ensemble has been created for the Australasia region as part of the World Climate Research Programs Coordinated Regional Downscaling Experiment (CORDEX). The CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia.

An accurate description of changes in extreme rainfall events requires high resolution simulations. Regional climate models (RCMs), where GCM data are used to provide input boundary conditions, are widely used as a way to resolve finer spatial scale phenomena. A problem with this, however, is that the inherent systematic biases within the GCM simulation are transferred to the RCM through the model boundaries. In this work we focus on the impact of bias correction of lateral and lower boundary conditions on simulated extreme rainfall events. Here three bias correction approaches are investigated. In increasing order of complexity, these are corrections for the mean, mean and variance, and the nested bias correction (NBC) approach that also corrects for lag-1 autocorrelations at nested timescales. These corrections are implemented on six-hourly GCM data taken from the GCM simulations which are used to drive the RCM along the RCM lateral boundaries. To evaluate the performance of bias correction on simulation of extreme rainfall events, daily precipitation extremes indices from the World Meteorological Organization (WMO) Expert Team on Climate Risk and Sectoral Climate Indicators (ET-CRSCI) are used. The results show that bias correction on the boundary conditions produce the results in significant improvement in extremes indices. It is clear that sea surface temperature (SST) plays an important role in driving the simulation. The results indicate that within the domain (far from boundaries) the errors in precipitation extremes are strongly dependent on the RCM, with a smaller effect coming from changes in the lateral boundary conditions.

Regional climate models (RCM) are an important tool for simulating atmospheric information at finer resolutions often of greater relevance to local scale climate change impact assessment studies. The lateral and lower boundary conditions, which form the inputs to the RCM downscaling application, are outputs from the global climate model (GCM). These boundary variables are known to be biased in GCMs, providing the potential to use a statistical approach that corrects these biases before use in downscaling. An array of bias correction techniques have been developed to remove these biases before being used to drive the RCM, but questions remain on their efficacy in terms of the final downscaled output. This study assesses the impact of these bias correction strategies by focussing on how these corrections are translated as one proceeds from the lateral boundaries into the model interior. Of specific interest is the change in the correction from generation of the lateral boundary conditions as well as how correction information moves through the relaxation zone and into the interior of the model. Here we show that bias correction information passing into the regional climate model is limited by interpolations required to generate lateral boundary conditions and dominant outflow wind conditions in the boundaries. This work suggests that these limitations should be addressed in order for bias correction of lateral boundary conditions to robustly influence RCM simulations of climate in the interior of the model domain.

The vertical temperature profile in the atmosphere reflects a balance between radiative and convective processes and interactions with the oceanic and land surfaces. Changes in vertical temperature profiles can affect atmospheric stability, which in turn can impact various aspects of weather systems. In this study, we analyzed recent-past trends of temperature over the Australian region using a homogenized monthly upper-air temperature dataset and four reanalysis datasets (NCEP, ERA-Interim, JRA-55 and MERRA). We also used outputs of 12 historical and future regional climate model (RCM) simulations from the NSW/ACT (New South Wales/Australian Capital Territory) Regional Climate Modelling (NARCliM) project and 6 RCM simulations from the CORDEX (Coordinated Regional Downscaling Experiment) Australasian project to investigate projected changes in vertical temperature profiles. The results show that the currently observed positive trend in the troposphere and negative trend in the lower stratosphere will continue in the future with significant warming over the whole troposphere and largest over the middle to upper troposphere. The increasing temperatures are found to be latitude-dependent with clear seasonal variations, and a strong diurnal variation for the near surface layers and upper levels in tropical regions. Changes in the diurnal variability indicate that near surface layers will be less stable in the afternoon leading to conditions favoring convective systems and more stable in the early morning which is favorable for temperature inversions. The largest differences of future changes in temperature between the simulations are associated with the driving GCMs, suggesting that large-scale circulation plays a dominant role in regional atmospheric temperature change.

Evaluation of global gridded precipitation datasets typically entails using the in situ or satellite-based data used to derive them, so that out-of-sample testing is usually not possible. Here we detail a methodology that incorporates the physical balance constraints of the surface water and energy budgets to evaluate gridded precipitation estimates, providing the capacity for out-of-sample testing. Performance conclusions are determined by the ability of precipitation products to achieve closure of the linked budgets using adjustments that are within their prescribed uncertainty bounds. We evaluate and compare five global gridded precipitation datasets: IMERG, GPCP, GPCC, REGEN, and MERRA-2. At the spatial level, we show that precipitation is best estimated by GPCC over the high latitudes, by GPCP over the tropics, and by REGEN over North Africa and the Middle East. IMERG and REGEN appear best over Australia and South Asia. Furthermore, our results give insight into the adequacy of prescribed uncertainties of these products and shows that MERRA-2, while being less competent than the other four products in estimating precipitation, has the best representation of uncertainties in its precipitation estimates. The spatial extent of our results is not only limited to grid cells with in situ observations. Therefore, the approach enables a robust evaluation of precipitation estimates and goes some way to addressing the challenge of validation over observation scarce regions.

Coarse resolution global climate models (GCMs) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.

Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models’ ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes. CLASS variable estimates are freely available viahttps://doi.org/10.25914/5c872258dc183.

Made available in DSpace on 2022-04-28T19:51:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-02-01 Stichting Combined Ophthalmic Research Rotterdam Deutsche Kniegesellschaft European Society of Gastrointestinal Endoscopy We describe the first effort within the Coordinated Regional Climate Downscaling Experiment-Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of twenty-first-century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over nine CORDEX domains at ~25-km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, and energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors, and climate service activities. The future of the CORDEX-CORE initiative is also discussed. Abdus Salam International Centre for Theoretical Physics Climate Service Center Germany (GERICS) Oak Ridge National Laboratory University of Innsbruck National Center for Atmospheric Research, Boulder Centro de Investigación Científica y de Educación Superior de Ensenada National Institute of Oceanography and Applied Geophysics (INOGS) Abdus Salam International Centre for Theoretical Physics University of São Paulo Climate Change Research Centre School of Biological Earth and Environmental Science University of New South Wales Institute of Atmospheric Physics Chinese Academy of Sciences Hong Kong University of Science and Technology São Paulo State University Federal University of Itajuba University of Augsburg São Paulo State University

The climate is warming and this is changing some aspects of storms, but we have relatively little knowledge of storm characteristics beyond intensity, which limits our understanding of storms overall. In this study, we apply a cell-tracking algorithm to 20 years of radar data at a mid-latitude coastal-site (Sydney, Australia), to establish a regional precipitation system climatology. The results show that extreme storms in terms of translation-speed, size and rainfall intensity usually occur in the warm season, and are slower and more intense over land between ~10am and ~8pm (AEST), peaking in the afternoon. Precipitation systems are more frequent in the cold season and often initiate over the ocean and move northward, leading to precipitation mostly over the ocean. Using clustering algorithms, we have found five precipitation system types with distinct properties, occurring throughout the year but peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some system types using a multivariate approach. This climatology for a variety of precipitation system characteristics will allow future study of any changes in these characteristics due to climate change.

We know the climate is warming and this is changing some aspects of storms, but we have little knowledge of storm characteristics beyond intensity, which limits our understanding of storms overall. In this study, we apply a cell-tracking algorithm to 20 years of radar data at a mid-latitude coastal-site (Sydney, Australia), to establish a regional storm climatology. The results show that extreme storms in terms of translation-speed, size and rainfall intensity usually occur in the warm season, and are slower and more intense over land between ~10am and ~8pm (AEST), peaking in the afternoon. Storms are more frequent in the cold season and often initiate over the ocean and move northward, leading to precipitation mostly over the ocean. Using clustering algorithms, we have found five storm types with distinct properties, occurring throughout the year but peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some storm types using a multivariate approach. This climatology for a variety of storm characteristics will allow future study of any changes in these characteristics due to climate change.

Quetiapine fumarate (QF) is an atypical antipsychotic used off-label for the treatment of delirium in critically-ill infants and children. For the treatment of pediatric populations or patient populations with trouble swallowing tablets, an oral suspension would be an ideal dosage formulation. However, there are no liquid formulations of QF commercially available. Therefore, a compounded oral suspension prepared from the commercial QF tablets is widely used in clinical settings. The extemporaneous preparation of QF compounded oral suspension changes the formulation from a solid form to a liquid form. Thus, the stability of QF compounded oral suspension should be critically evaluated to guide pharmacists for administration and storage of QF compounded oral suspensions. However, the stability of the nonaqueous oral QF suspension was not measured. The objective of this study was to develop QF compounded oral suspensions at 10 mg/mL by using commercial QF tablets in two readily available aqueous vehicles (Ora-Sweet and Ora-Blend) and measure their stability at both room temperature and under refrigeration. Physical stability of the QF compounded suspensions were evaluated by appearance and odor. Chemical stability of the QF compounded suspensions were evaluated based on pH, degradation, drug content and the amount of the drug dissolved in the vehicles. An HPLC method was validated and used to evaluate QF compounded suspensions over 60 days. In addition to the total drug in the suspensions, the dissolved drug in the vehicles was also measured during the stability testing and evaluated as a stability parameter. Overall, QF suspension prepared in Ora-Blend was preferable, demonstrating a superior 60-day stability at both room temperature and refrigerated storage.

The Community Atmosphere Biosphere Land Exchange (CABLE) model is a third‐generation land surface model (LSM). CABLE is commonly used as a stand‐alone LSM, coupled to the Australian Community Climate and Earth Systems Simulator global climate model and coupled to the Weather Research and Forecasting (WRF) model for regional applications. Here, we evaluate an updated version of CABLE within a WRF physics ensemble over the COordinated Regional Downscaling EXperiment (CORDEX) AustralAsia domain. The ensemble consists of different cumulus, radiation and planetary boundary layer (PBL) schemes. Simulations are carried out within the NASA Unified WRF modeling framework, NU‐WRF. Our analysis did not identify one configuration that consistently performed the best for all diagnostics and regions. Of the cumulus parameterizations the Grell‐Freitas cumulus scheme consistently overpredicted precipitation, while the new Tiedtke scheme was the best in simulating the timing of precipitation events. For the radiation schemes, the RRTMG radiation scheme had a general warm bias. For the PBL schemes, the YSU scheme had a warm bias, and the MYJ PBL scheme a cool bias. Results are strongly dependent on the region of interest, with the northern tropics and southwest Western Australia being more sensitive to the choice of physics options compared to southeastern Australia which showed less overall variation and overall better performance across the ensemble. Comparisons with simulations using the Unified Noah LSM showed that CABLE in NU‐WRF has a more realistic simulation of evapotranspiration when compared to GLEAM estimates.

Extreme wildfires have recently caused disastrous impacts in Australia and other regions of the world, including events with strong convective processes in their plumes (i.e., strong pyroconvection). Dangerous wildfire events such as these could potentially be influenced by anthropogenic climate change, however, there are large knowledge gaps on how these events might change in the future. The McArthur Forest Fire Danger Index (FFDI) is used to represent near-surface weather conditions and the Continuous Haines index (CH) is used here to represent lower to mid-tropospheric vertical atmospheric stability and humidity measures relevant to dangerous wildfires and pyroconvective processes. Projected changes in extreme measures of CH and FFDI are examined using a multi-method approach, including an ensemble of global climate models together with two ensembles of regional climate models. The projections show a clear trend towards more dangerous near-surface fire weather conditions for Australia based on the FFDI, as well as increased pyroconvection risk factors for some regions of southern Australia based on the CH. These results have implications for fields such as disaster risk reduction, climate adaptation, ecology, policy and planning, noting that improved knowledge on how climate change can influence extreme wildfires can help reduce future impacts of these events.