Alejandro Di Luca

Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations

Regional Climate Models (RCMs) constitute the most often used method to perform affordable high-resolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, the orographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions.

Challenges in the Quest for Added Value of Regional Climate Dynamical Downscaling

This paper summarises the current state of understanding with respect to the added value (AV) to be expected from one-way nested high-resolution regional climate simulations and projections. The reasons that lead to the development and the progress of regional climate models (RCMs) are first considered. The scientific basis sustaining the RCMs mission is then briefly reviewed. Based on recent publications of studies on the topic of AV, concepts related to the various definitions of AV are examined with the aim of clarifying their meaning and of bridging different schools of thought. The conditions under which AV can be expected, and in which variables and statistical moments, are also discussed.

Effects of City Expansion on Heat Stress under Climate Change Conditions

We examine the joint contribution of urban expansion and climate change on heat stress over the Sydney region. A Regional Climate Model was used to downscale present (1990–2009) and future (2040–2059) simulations from a Global Climate Model. The effects of urban surfaces on local temperature and vapor pressure were included. The role of urban expansion in modulating the climate change signal at local scales was investigated using a human heat-stress index combining temperature and vapor pressure. Urban expansion and climate change leads to increased risk of heat-stress conditions in the Sydney region, with substantially more frequent adverse conditions in urban areas. Impacts are particularly obvious in extreme values; daytime heat-stress impacts are more noticeable in the higher percentiles than in the mean values and the impact at night is more obvious in the lower percentiles than in the mean. Urban expansion enhances heat-stress increases due to climate change at night, but partly compensates its effects during the day. These differences are due to a stronger contribution from vapor pressure deficit during the day and from temperature increases during the night induced by urban surfaces. Our results highlight the inappropriateness of assessing human comfort determined using temperature changes alone and point to the likelihood that impacts of climate change assessed using models that lack urban surfaces probably underestimate future changes in terms of human comfort.

Potential for added value in temperature simulated by high-resolution nested RCMs in present climate and in the climate change signal

Regional Climate Models (RCMs) have been developed in the last two decades in order to produce high-resolution climate information by downscaling Atmosphere-Ocean General Circulation Models (AOGCMs) simulations or analyses of observed data. A crucial evaluation of RCMs worth is given by the assessment of the value added compared to the driving data. This evaluation is usually very complex due to the manifold circumstances that can preclude a fair assessment. In order to circumvent these issues, here we limit ourselves to estimating the potential of RCMs to add value over coarse-resolution data. We do this by quantifying the importance of fine-scale RCM-resolved features in the near-surface temperature, but disregarding their skill. The Reynolds decomposition technique is used to separate the variance of the time-varying RCM-simulated temperature field according to the contribution of large and small spatial scales and of stationary and transient processes. The temperature variance is then approximated by the contribution of four terms, two of them associated with coarse-scales (e.g., corresponding to the scales that can be simulated by AOGCMs) and two of them describing the original contribution of RCM simulations. Results show that the potential added value (PAV) emerges almost exclusively in regions characterised by important surface forcings either due to the presence of fine-scale topography or land-water contrasts. Moreover, some of the processes leading to small-scale variability appear to be related with relatively simple mechanisms such as the distinct physical properties of the Earth surface and the general variation of temperature with altitude in the Earth atmosphere. Finally, the article includes some results of the application of the PAV framework to the future temperature change signal due to anthropogenic greenhouse gasses. Here, contrary to previous studies centred on precipitation, findings suggest for surface temperature a relatively low potential of RCMs to add value over coarser resolution models, with the greatest potential located in coastline regions due to the differential warming occurring in land and water surfaces.

Considerations of Domain Size and Large-Scale Driving for Nested Regional Climate Models: Impact on Internal Variability and Ability at Developing Small-Scale Details

The premise of dynamical downscaling is that a high-resolution, nested Regional Climate Model (RCM), driven by large-scale atmospheric fields at its lateral boundary, generates fine scales that are dynamically consistent with the large scales. An RCM is hence expected to act as a kind of magnifying glass that will reveal details that could not be resolved on a coarse mesh. The small scales represent the main potential added value of a high-resolution RCM.

Impact of Identification Method on the Inferred Characteristics and Variability of Australian East Coast Lows

AbstractThe Australian east coast low (ECL) is both a major cause of damaging severe weather and an important contributor to rainfall and dam inflow along the east coast, and is of interest to a wide range of groups including catchment managers and emergency services. For this reason, several studies in recent years have developed and interrogated databases of east coast lows using a variety of automated cyclone detection methods and identification criteria. This paper retunes each method so that all yield a similar event frequency within the ECL region, to enable a detailed intercomparison of the similarities, differences, and relative advantages of each method. All methods are shown to have substantial skill at identifying ECL events leading to major impacts or explosive development, but the choice of method significantly affects both the seasonal and interannual variation of detected ECL numbers. This must be taken into consideration in studies on trends or variability in ECLs, with a subcategorization...

Projected changes in east Australian midlatitude cyclones during the 21st century

The east coast of Australia is regularly influenced by midlatitude cyclones known as East Coast Lows. These form in a range of synoptic situations and are both a cause of severe weather and an important contributor to water security. This paper presents the first projections of future cyclone activity in this region using a regional climate model ensemble, with the use of a range of cyclone identification methods increasing the robustness of results. While there is considerable uncertainty in projections of cyclone frequency during the warm months, there is a robust agreement on a decreased frequency of cyclones during the winter months, when they are most common in the current climate. However, there is a potential increase in the frequency of cyclones with heavy rainfall and those closest to the coast and accordingly those with potential for severe flooding.

Resolution Sensitivity of Cyclone Climatology over Eastern Australia Using Six Reanalysis Products

AbstractThe climate of the eastern seaboard of Australia is strongly influenced by the passage of low pressure systems over the adjacent Tasman Sea due to their associated precipitation and their potential to develop into extreme weather events. The aim of this study is to quantify differences in the climatology of east coast lows derived from the use of six global reanalyses. The methodology is explicitly designed to identify differences between reanalyses arising from differences in their horizontal resolution and their structure (type of forecast model, assimilation scheme, and the kind and number of observations assimilated). As a basis for comparison, reanalysis climatologies are compared with an observation-based climatology. Results show that reanalyses, specially high-resolution products, lead to very similar climatologies of the frequency, intensity, duration, and size of east coast lows when using spatially smoothed (about 300-km horizontal grid meshes) mean sea level pressure fields as input da...

Evaluating the representation of Australian East Coast Lows in a regional climate model ensemble

Due to their large influence on both severe weather and water security along the east coast of Australia, it is increasingly important to understand how East Coast Lows (ECLs) may change over coming decades. Changes in ECLs may occur for a number of reasons including changes in the general atmospheric circulation (e.g. poleward shift of storm tracks) and/or changes in local conditions (e.g. changes in sea surface temperatures). Numerical climate models are the best available tool for studying these changes however, in order to assess future projections, climate model simulations need to be evaluated on how well they represent the historical climatology of ECLs. In this paper, we evaluate the performance of a 15-member ensemble of regional climate model (RCM) simulations to reproduce the climatology of cyclones obtained using three high-resolution reanalysis datasets (ERAInterim, NASA-MERRA and JRA55). The performance of the RCM ensemble is also compared to results obtained from the global datasets that are used to drive the RCM ensemble (four general circulation model simulations and a low resolution reanalysis), to identify whether they offer additional value beyond the driving data. An existing cyclone detection and tracking algorithm is applied to derive a number of ECL characteristics and assess results at a variety of spatial scales. The RCM ensemble offers substantial improvement on the coarse-resolution driving data for most ECL characteristics, with results typically falling within the range of observational uncertainty, instilling confidence for studies of future projections. The study clearly highlights the need to use an ensemble of simulations to obtain reliable projections and a range of possible future changes.

Seasonal mean temperature changes control future heat waves

Increased temperature will result in longer, more frequent, and more intense heat waves. Changes in temperature variability have been deemed necessary to account for future heat wave characteristics. However, this has been quantified only in Europe and North America, while the rest of the globe remains unexplored. Using late century global climate projections, we show that annual mean temperature increases is the key factor defining heat wave changes in most regions. We find that commonly studied areas are an exception rather than the standard and the mean climate change signal generally outweighs any influence from variability changes. More importantly, differences in warming across seasons are responsible for most of the heat wave changes and their consideration relegates the contribution of variability to a marginal role. This reveals that accurately capturing mean seasonal changes is crucial to estimate future heat waves and reframes our interpretation of future temperature extremes.