Conformal Cubic Atmospheric Model Research Articles

Evaluation of Dynamically Downscaled CMIP6‐CCAM Models Over Australia

AbstractHigh‐resolution climate change projections are increasingly necessary to inform climate policy and adaptation planning. Downscaling of global climate models (GCMs) is required to simulate the climate at the spatial scale relevant for local impacts. Here, we dynamically downscaled 15 CMIP6 GCMs to a 10 km resolution over Australia using the Conformal Cubic Atmospheric model (CCAM), creating the largest ensemble of high‐resolution downscaled CMIP6 projections for Australia. We compared the host CMIP6 models and downscaled simulations to the Australian Gridded Climate Data (AGCD) observational data and evaluated performance using the Kling‐Gupta efficiency and Perkins skill score. Downscaling improved performance over host GCMs for seasonal temperature and precipitation (10% and 43% respectively), and for annual cycles of temperature and precipitation (6% and 13% respectively). Downscaling also improved the fraction of dry days, reducing the bias for too many low‐rain days. The largest improvements were found in climate extremes, with enhancements to extreme minimum temperatures in all seasons varying from 142% to 201%, and to extreme precipitation of 52% in Austral winter and 47% in summer. The ensemble average integrated skill score improved by 16%. Temperature and precipitation biases were reduced in mountainous and coastal areas. CCAM downscaling outperformed host CMIP6 GCMs at multiple spatial scales and regions—continental Australia, Australian IPCC regions and Queensland's regions—with integrated added value ranging from 9% to 150% and higher over densely populated regions more exposed to climate impacts. This data set will be a valuable resource for understanding future climate changes in Australia.

Drought Potential in Borneo Based on the RCP 4.5 Scenario

One of the impacts of climate change is an increase in the frequency and intensity of hydrometeorological disasters such as prolonged droughts. Borneo is one of the areas threatened by drought due to climate change. Therefore, it is important to identify and implement appropriate mitigation and adaptation measures. This study used the dynamical downscaling method by the Conformal Cubic Atmospheric Model (CCAM) to evaluate the potential for drought events in Borneo based on the RCP 4.5 scenario. The annual rainfall in Borneo for 2021-2050 is projected to increase compared to 1991-2020. However, the increase in annual rainfall does not free Borneo from the possibility of drought events in the future. This study’s results indicate that areas in southern Borneo, such as Banjarmasin, Pangkalan Bun, and Pontianak, will have a higher frequency of meteorological drought events and are also expected to experience longer periods of consecutive dry days between 2021-2050 compared to 1991-2020.

High‐Resolution CCAM Simulations Over New Zealand and the South Pacific for the Detection and Attribution of Weather Extremes

AbstractDetection and attribution experiments are designed for the causal diagnosis of features in the climate system, including trends in mean climate and extreme events. While several detection and attribution data sets now exist, the coarse resolution of the climate models used (∼100‐km) often hinders their application to topographically complex regions like Aotearoa New Zealand and small island nations. The coarse atmospheric resolution may also be detrimental for simulating certain features of the atmospheric circulation, including the jets, blocking and cyclones. To address this, here we introduce a new set of climate model runs consisting of high‐resolution atmospheric simulations from the Conformal Cubic Atmospheric Model (CCAM) non‐hydrostatic global model. The variable‐resolution grid employed by CCAM enables targeted high‐resolution simulations over New Zealand (12‐km) and intermediate resolution over the wider South Pacific region (12–35‐km). Simulations from the historical experiment (years 1982–2021), consisting of ten initial condition ensemble members, are presented and evaluated here. The evaluation focuses on the representation of the large‐scale atmospheric circulation over the Southern Hemisphere including the jet streams, storm tracks, cyclones, blocking and teleconnections, as well as more localized temperature and precipitation variability and extremes specifically over New Zealand. While certain biases are highlighted and discussed for the large‐scale atmospheric circulation, CCAM is found to perform especially well for various precipitation and temperature‐based extreme indices at smaller scales across New Zealand, generally outperforming state‐of‐the‐art reanalysis and coarser resolution global atmospheric models. These results support further application of the CCAM ensemble for studying weather and climate extremes in attribution studies.

Simulation of the eThekwini Heat Island in South Africa

Abstract The study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) when simulating an urban heat island (UHI) over the city of eThekwini, located along the southeast coast of South Africa. The CCAM is applied at a grid length of 1 km on the panel with eThekwini, in a stretched-grid mode. The CCAM is coupled to the urban climate model called the Australian Town Energy Budget (ATEB). The ATEB incorporates measured urban parameters including building characteristics, emissions, and albedo. The ATEB incorporates the land-cover boundary conditions obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The CCAM configuration applied realistically captured the orientation of the city and land-cover types. Simulations of meteorological variables such as temperatures and longwave radiation reproduced the spatial distribution and intensity of the UHI. Results show that the UHI is stronger during summer and weaker in all other seasons. The UHI developed because of natural factors (e.g., distribution of longwave radiation) and human factors (e.g., urban expansion, an increase in anthropogenic emissions, and additional heating). Because of the city’s location along the coast, the UHI simulation could be weakened by atmospheric circulations resulting from land and sea breezes. Mitigation methods such as applying reflective paints and revegetation of the city may increase albedo and latent heat fluxes but reduce the sensible heat fluxes and weaken the UHI. However, the UHI may not be completely eliminated since natural factors and emissions constantly influence its development. Significance Statement The outcome of this study could be particularly valuable for municipalities in their disaster management planning since the occurrence of UHIs can cause heat-related diseases such as heatstrokes and even fatalities, especially for the elderly, in cities. Increases in temperatures also lead to higher demand for air conditioners, which in the long term lead to higher demand and pressure on the electricity grid system as well as increased costs for the individual. As higher temperatures increase heatwave events, increases in anthropogenic emissions also result in degraded air quality that impacts health. UHIs impact human lives and can cause deterioration in health when individuals experience high temperatures in summer. Warmer temperatures also reduce energy demand (and in the long term assist with global environmental restoration).

A precipitation downscaling method using a super-resolution deconvolution neural network with step orography

Abstract Coarse spatial resolution in gridded precipitation datasets, reanalysis, and climate model outputs restricts their ability to characterize the localized extreme rain events and limits the use of the coarse resolution information for local to regional scale climate management strategies. Deep learning models have recently been developed to rapidly downscale the coarse resolution precipitation to the high local scale resolution at a much lower cost than dynamic downscaling. However, these existing super-resolution deep learning modeling studies have not rigorously evaluated the model’s skill in producing fine-scale spatial variability, particularly over topographic features. These current deep-learning models also have difficulty predicting the complex spatial structure of extreme events. Here, we develop a model based on super-resolution deconvolution neural network (SRDN) to downscale the hourly precipitation and evaluate the predictions. We apply three versions of the SRDN model: (a) SRDN (no orography), (b) SRDN-O (orography only at final resolution enhancement), and (c) SRDN-SO (orography at each step of resolution enhancement). We assess the ability of SRDN-based models to reproduce the fine-scale spatial variability and compare it with the previously used deep learning model (DeepSD). All the models are trained and tested using the Conformal Cubic Atmospheric Model (CCAM) data to perform a 100 to 12.5 km of hourly precipitation downscaling over the Australian region. We found that SRDN-based models, including orography, deliver better fine-scale spatial structures of both climatology and extremes, and significantly improved the deep-learning downscaling. The SRDN-SO model performs well both qualitatively and quantitatively in reconstructing the fine-scale spatial variability of climatology and rainfall extremes over complex orographic regions.

Water availability and response of Tarbela Reservoir under the changing climate in the Upper Indus Basin, Pakistan

Agriculture is one of the major contributors to the Gross Domestic Product (GDP) of Pakistan which relies on the availability of water. Hydropower contributes approximately 35% to the national electricity gid of Pakistan. Indus River is the main river of the Indus River System (IRS) which provides water for agriculture, hydropower and other purposes. The outputs of the Conformal-Cubic Atmospheric Model (CCAM) are used to force the University of British Columbia Watershed Model (UBCWM) in the Upper Indus Basin (UIB), to investigate future water availability under the two IPCC emission scenarios (RCP4.5 and RCP8.5). Tarbela Reservoir which is the outlet of UIB is used as a measurement tool to assess water availability and response of the reservoir to climate change. The results show that maximum and minimum temperature are increasing in the future in comparison to the reference period. The largest increases in maximum temperature are projected for MAM (March–April–May) and JJA (June–July–August), with increases up to 2 °C in MAM and increases up to 6.4 °C in JJA under the RCP4.5 and RCP8.5, respectively, in the future. Minimum temperature has maximum increase (6.7 °C) in DJF (December–January–February) during 2071–2100 under RCP8.5. Precipitation shows a 5.1% decrease in DJF during 2011–2040 under RCP4.5. The statistics about water availability suggest that there is consistent increase in most of the months in the future, however, under the RCP4.5, there is decline in the river flow during 2071–2100 as compared to the 2041–2070. The findings of this study show that most of the time there will be more water available but in some months, there may be water scarcity under the RCP4.5, however, proper management and optimal utilization can reduce the water scarcity.

The Projections of Climate Change using Conformal Cubic Atmospheric Model (CCAM) in Bali - Indonesia

Research on climate change was conducted in Bali, Indonesia, using the Conformal Cubic Atmospheric Model (CCAM), which works with the Representative Concentration Pathway (RCP) 4.5. The history data means average data from 1971 to 2000, climate projections with RCP4.5 scenario means average data from 2040 to 2069, and anomaly (climate change) is RCP4.5 minus history. The results represent the history of temperature between 22.5-27.5 0C and the projection between 25.5-29.5 0C. Temperature anomalies can be observed in much of northern Bali, which has increased from about 1.6 to 2.9 0C. There is a trend towards reduced (drier) humidity in most parts of Bali, particularly in the northern part of Bali. In contrast, a small amount in the south increases the (wetter) humidity. The comfort index of the Bali region in history is still relatively comfortable (20-26 0C). The projection condition RCP 4.5, there is no comfortable area with an index of more than 26 0C (hot and dry).

Seasonal local rainfall and hydrological forecasting for Limpopo communities – A pragmatic approach

This paper describes the development and testing of a simple local seasonal forecast system of rainfall and hydrological conditions. The primary target group is agricultural extension officers who communicate forecasts to small-scale farmers at local level. Two pilot areas within the Limpopo river basin in South Africa were used, one in the Luvuvhu river basin in Vhembe district and the other in the Letaba river basin in Mopani district. Local rainfall and hydrological forecasts of runoff, soil moisture and evapotranspiration were produced, built on readily available deterministic seasonal meteorological forecasts for large-scale rainfall from CSIR (Council for Scientific and Industrial Research, South Africa), produced from an ensemble of seasonal forecasts using the CCAM (Conformal-Cubic Atmospheric Model) global forecast model. Hydrological forecasts were produced through a “proxy” approach, whereby outputs from the ACRU (Agricultural Catchment Research Unit) agrohydrological model provided expected hydrological responses from observed years that are representative of the rainfall anomalies predicted by the global seasonal forecast. Locally monitored soil moisture augmented the hydrological forecasts. The local seasonal forecast system does not require sophisticated calculations or a complex operational environment and complements coarser scale forecasts disseminated by the provincial departments of agriculture. Results of three rainfall seasons from 2013 to 2016 in the pilot areas showed the proxy approach to have relatively good matches between forecasts and available observations, showing better predictability for below normal rainfall seasons with exception for an extreme monthly rainfall event. The forecasts matched observed conditions best during the strong El Niño phase of ENSO (El Niño Southern Oscillation) for 2015/2016.

Multivariable Panel Data Cluster Analysis of Meteorological Stations in Thailand for ENSO Phenomenon

The purpose of this research is to study the spatial and temporal groupings of 124 meteorological stations in Thailand under ENSO. The multivariate climate variables are rainfall, relative humidity, temperature, max temperature, min temperature, solar downwelling, and horizontal wind from the conformal cubic atmospheric model (CCAM) in years of El Niño (1987, 2004, and 2015) and La Niña (1999, 2000, and 2011). Euclidean distance timed and spaced with average linkage for clustering and silhouette width for cluster validation were employed. Five spatial clusters (SCs) and three temporal clusters (TCs) in each SC with different average precipitation were compared by El Niño and La Niña. The pattern of SCs and TCs was similar for both events except in the case when severe El Niño occurred. This method could be applied using variables forecasted in the future to be used for planning and managing crop cultivation with the climate change in each area.

Simulation of daily variation of precipitation during anomalously-wet dry season event over the western Maritime Continent

Local seas play a significant role in causing anomalously wet of the dry season over the Indonesia Maritime Continent (10°S-8°N, 95-145°E). As a result, modeling the anomalously-wet dry season over Indonesia lead challenges due to several subregional processes over local seas could not be captured well in the regional climate model. This study explores subregions processes of sea-air interaction over the western Maritime Continent by simulating diurnal precipitation using Cubic Conformal Atmospheric Model (CCAM) with a spatial resolution of 32 km during the anomalously-wet dry season periods during May-to-September (MJJAS) 2020. The simulated results were confirmed by precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite observation. The results show anomalous circulation patterns induce anomalous regional precipitation over western MC is induced by anomalous circulation patterns over four keys of seas subregion, i.e., Indian Ocean, South China Sea, southern Sumatra (Lampung and Sunda strait), and the Java Sea. Furthermore, the anomalous circulation also modulates anomalous local circulation and enhances surface water vapor by an increased surface latent heat flux.

Climate Change Impacts on Temperature and Chill Unit Trends for Apple (Malus domestica) Production in Ceres, South Africa

Climate is an essential part of crop production, determining the suitability of a given region for deciduous fruit products such as apples (Malus domestica). It influences the yield and quality of fruits. There is strong evidence of global and regional-scale climate change since the advent of the industrial era. In South Africa, mean surface temperatures have revealed a warming trend over the last century. This study aimed to assess the impact of climate change on temperature and chill unit trends for apple production in Ceres, South Africa. The daily positive Utah chill units (DPCU) model was used as frequent high temperatures can lead to a high negation volume. Historically observed (1981–2010) and future projected (2011–2100) temperatures were obtained from the South African Weather Service (SAWS) and three ensemble members of the Cubic-Conformal Atmospheric Model (CCAM), respectively. The latter employed the RCP8.5 pathway. Linear trends were calculated for temperature and accumulated PCUs for the historical base period. The probability of accumulating specific threshold PCU values for both historical and future periods was assessed from cumulative distribution functions (CDFs). The historical change in minimum temperatures showed no significant trend. Ceres revealed a warming trend in maximum temperatures over the historical period. By the 2080s, the probability of not exceeding a threshold of 1600 PCUs was exceptionally high for all ensemble members. Future projections showed a decline in the accumulated PCUs of 2–5% by the 2020s, 7–17% by the 2050s, and 20–34% towards the end of the 20th century. Based on these results, it is clear that winter chill units are negatively influenced by climate change. The loss in yield and fruit quality of apples due to climate change can negatively impact the export market, leading to significant economic losses for apple production in the Ceres area.

Regional climate model performance and application of bias corrections in simulating summer monsoon maximum temperature for agro-climatic zones in India

The present study evaluates the performance of Conformal-Cubic Atmospheric Model (CCAM) simulations downscaled from six global climate models (GCMs) (i.e., ACCESS1.0, CNRM-CM5, CCSM4, GFDL-CM3, MPI-ESM-LR, and NorESM-M) and Max Plank’s Regional Model (REMO2009(MPI)) obtained from the South-Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) for analyzing the summer monsoon maximum temperature (Tmax) over the fifteen Agro-Climatic Zones (ACZs) in India. The model simulations are compared with the two sets of observed data obtained from the India Meteorology Department (IMD) and Climate Research Unit (CRU) for the period from 1981 to 2005. The results illustrate that the skill of CCAM regional climate models (RCMs) is higher than the REMO in simulating the Tmax over all the regions. The spatial patterns of Tmax in CCAM (CCSM) and CCAM (CNRM) are closer to IMD, while the Tmax distributions in CCAM (CNRM), CCAM (CCSM), and CCAM (BCCR) agree well with the CRU, and correlation coefficient (CC) is more than 0.6; however, large positive biases in all RCMs are depicted over the Himalayan regions. The inter-comparison among all the RCMs suggest that the CCAM (CNRM) and CCAM (CCSM) are rendering as the foremost models in simulating Tmax over different ACZs. Performances of these two models also infer the usefulness of the model products for impact studies over the individual ACZs. However, the existing systematic biases in the RCMs impeded the model performance and it is necessary to remove the model bias prior to some real-time application. In this study, two bias correction methods, i.e., linear scaling (LS) and distribution mapping (DM), have been used to correct RCM output bias. It is found that the model performance using DM correction is better than LS method. The performance validations are evaluated based on the probability density function (PDF), CC, and standard deviation (SD) with 95% confidence level. The model evaluation has also been justified using mean absolute error (MAE) index, Nash-Sutcliffe coefficient (NS) index, percent bias (Pbias), and the Willmott’s index of agreement (d) which confirm the research findings. The results are providing an effective guidance on the usefulness of bias corrected RCMs over a particular ACZs for impact assessment.

How an urban parameterization affects a high‐resolution global climate simulation

The impact of urban areas on the global and regional climate has been assessed using the global Conformal Cubic Atmospheric Model (CCAM) including an urban canyon parameterization at a global resolution of 50 km. Simulations were produced with and without urban areas to assess urban impacts for the historical period 1980–2000. Two different land cover and urban datasets (one based on IGBP‐DIS, the other on MODIS) were tested. In addition, simulations were performed for the end of the 21st century with the RCP8.5 scenario. Evaluation of the historical climate simulations indicates realistic local urban effects, such as higher daily minimum air temperatures (tasmin), higher sensible heat flux and lower latent heat flux at urban grid cells. In regions with large fractions of urban areas, some regional changes are also noted. In addition, there are significant regional effects far away from the main urban areas, which are similar in magnitude to the effects of the different non‐urban land cover input datasets. Under the projected warming at the end of the 21st century (with no land cover change), there is a decrease in anthropogenic heating, primarily during wintertime. There is a slightly smaller increase in daily maximum temperature and a slightly larger increase in tasmin in urban areas compared to rural areas. This leads to a smaller increase in the diurnal temperature range within urban areas. The tasmin changes also imply an increase in the urban heat island effect for larger cities. The results of this sensitivity study show that there is a detectable impact of urban areas on high‐resolution global climate simulations. Consequently, there is a need to include urban areas in global simulations, as well as in studies of land‐use change.

Heatwaves in the Future Warmer Climate of South Africa

Weather and climate extremes, such as heat waves (HWs), have become more frequent due to climate change, resulting in negative environmental and socioeconomic impacts in many regions of the world. The high vulnerability of South African society to the impacts of warm extreme temperatures makes the study of the effect of climate change on future HWs necessary across the country. We investigated the projected effect of climate change on future of South Africa with a focus on HWs using an ensemble of regional climate model downscalings obtained from the Conformal Cubic Atmospheric Model (CCAM) for the periods 2010–2039, 2040–2069, and 2070–2099, with 1983–2012 as the historical baseline. Simulations were performed under the Representative Concentration Pathway (RCP) 4.5 (moderate greenhouse gas (GHG) concentration) and 8.5 (high GHG concentration) greenhouse gas emission scenarios. We found that the 30-year period average maximum temperatures may rise by up to 6 °C across much of the interior of South Africa by 2070–2099 with respect to 1983–2012, under a high GHG concentration. Simulated HW thresholds for all ensemble members were similar and spatially consistent with observed HW thresholds. Under a high GHG concentration, short lasting HWs (average of 3–4 days) along the coastal areas are expected to increase in frequency in the future climate, however the coasts will continue to experience HWs of relatively shorter duration compared to the interior regions. HWs lasting for shorter duration are expected to be more frequent when compared to HWs of longer durations (over two weeks). The north-western part of South Africa is expected to have the most drastic increase in HWs occurrences across the country. Whilst the central interior is not projected to experience pronounced increases in HW frequency, HWs across this region are expected to last longer under future climate change. Consistent patterns of change are projected for HWs under moderate GHG concentrations, but the changes are smaller in amplitude. Increases in HW frequency and duration across South Africa may have significant impacts on human health, economic activities, and livelihoods in vulnerable communities.

Evaluation of soil moisture from CCAM-CABLE simulation, satellite-based models estimates and satellite observations: a case study of Skukuza and Malopeni flux towers

Abstract. Reliable estimates of daily, monthly and seasonal soil moisture are useful in a variety of disciplines. The availability of continuous in situ soil moisture observations in southern Africa barely exists; hence, process-based simulation model outputs are a valuable source of climate information, needed for guiding farming practices and policy interventions at various spatio-temporal scales. The aim of this study is to evaluate soil moisture outputs from simulated and satellite-based soil moisture products, and to compare modelled soil moisture across different landscapes. The simulation model consists of a global circulation model known as the conformal-cubic atmospheric model (CCAM), coupled with the CSIRO Atmosphere Biosphere Land Exchange model (CABLE). The satellite-based soil moisture data products include satellite observations from the European Space Agency (ESA) and satellite-observation-based model estimates from the Global Land Evaporation Amsterdam Model (GLEAM). The evaluation is done for both the surface (0–10 cm) and root zone (10–100 cm) using in situ soil moisture measurements collected from two study sites. The results indicate that both the simulation- and satellite-derived models produce outputs that are higher in magnitude range compared to in situ soil moisture observations at the two study sites, especially at the surface. The correlation coefficient ranges from 0.7 to 0.8 (at the root zone) and 0.7 to 0.9 (at the surface), suggesting that models mostly are in an acceptable phase agreement at the surface than at the root zone, and this was further confirmed by the root mean squared error and the standard deviation values. The models mostly show a bias towards overestimation of the observed soil moisture at both the surface and root zone, with the CCAM-CABLE showing the least bias. An analysis evaluating phase agreement using the cross-wavelet analysis has shown that, despite the models' outputs being in phase with the in situ observations, there are time lags in some instances. An analysis of soil moisture mutual information (MI) between CCAM-CABLE and the GLEAM models has successfully revealed that both the simulation and model estimates have a high MI at the root zone as opposed to the surface. The MI mostly ranges between 0.5 and 1.5 at both the surface and root zone. The MI is predominantly high for low-lying relative to high-lying areas.

Analysis of Drought Potential in Sumba Island until 2040 Caused by Climate Change

Climate Change is one of the events that can change climate components due to global warming events. One of the changes in climate components can cause changes in rainfall in an area. While changes in rainfall conditions in an area can cause increased vulnerability of a region to potential of drought. In this research, the effects of climate change on the drought potential in Sumbawa Island region was investigated, where this region is one of the areas prone to drought in Indonesia. Climate projection until 2040 was carried out using the Conformal Cubic Atmospheric Model (CCAM) model with 14 km spatial resolution and monthly temporal resolution. While the drought index is calculated by the SPI (Standardize Precipitation Index) method where positive numbers tend to mean wet, lift between -1 to 1 is neutral, and increasingly negative numbers tend to mean dry. It is known that in 2040 January was known that there would be no drought on Sumbawa Island with values ranging from -1 to 2. While in August of 2040, the potential for drought disaster should be watched out. This is because in this month the drought index value ranges from -2.5 to - 2. The potential for drought itself is known to increase in August 5 years until 2040 when compared with 5-year data, 2025 and 2030 in August in the Bima region. Meanwhile, based on time series analysis, it is estimated that there will be some extreme dry conditions, in August 2022, 2024, 2028, 2030, and 2033. Knowing these extreme conditions, it is hoped that this will become the basis for regional planning systems, starting from planning infrastructure and social systems to climate change mitigation and adaptation.

CALIBRATING WEATHER FORECASTING IN INDONESIA: THE GEOSTATISTICAL OUTPUT PERTURBATION METHOD

The Numerical Weather Prediction (NWP) was developed by the Meteorological, Climatological, and Geophysical Agency in Indonesia for the purpose of weather forecasting, however, it comes with a high level of bias. This purpose of this study therefore was to improve this model with the use of Geostatistical Output Perturbation (GOP), implemented in the conformal-cubic atmospheric model (CCAM) on NWP data from the eight meteorological stations in Indonesia, i.e. Kemayoran, Priok, Cengkareng, Pondok Betung, Curug, Dermaga, Tangerang and Citeko stations. The findings indicated exponential as the best distribution model for analyzing temperature in Indonesia using GOP. Also, locations which are considerably far away from other locations could have significant impact on the accuracy of the weather forecasts. In this case, Citeko station has quite different characteristics location considering the fact that it is located on higher elevation compared with other stations. Therefore, the exclusion of Citeko station produced better forecasting in terms of accuracy and precision, increasing to about twice the result when the station was included in the analysis.

Implementation of Tropical Cyclone Detection Scheme to CCAM model for Seasonel Tropical Cyclone Prediction over the Vietnam East Sea

Implementation of Tropical Cyclone Detection Scheme to CCAM model for Seasonel Tropical Cyclone Prediction over the Vietnam East Sea

Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison

The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s−1 mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons.

Rainfall Scenario of West Nusa Tenggara in 2040 Based on CCAM RCP 4.5

Climate change is one of the global issues with a broad impact to society. Because of its importance, there is a need to build a prediction system that can support mitigation, adaptation or prevention of climate change. West Nusa Tenggara (NTB) is an area where most of the population relies on agriculture for their livelihood so that the availability of rainwater becomes very important for daily survival. In this research, rainfall in NTB was predicted by CCAM (Conformal Cubic Atmospheric Model) model based on RCP (Recent Concentration Pathways) 4.5 scenario. Based on the verification results, the CCAM model was proven to perform fairly well in estimating rainfall in NTB in 2014-2016. This is evidenced by the high correlation coefficient between CCAM and observed rainfall data in several locations such as Kuripan, Kumbe, and Gunung Sari which reached 0.57, 0.51, and 0.51 respectively. In January 2040 places such as Bima, Sumbawa Besar and parts of Lombok Island are predicted to have a decrease in rainfall when compared to that in January 2018. The decrease ranges from 40-120 mm in one month. While in Kumbe and Kuripan, rainfall is predicted to increase, with an increase of about 0-40 mm in one month. In August 2040 rainfall is predicted to generally rise in NTB with an increase of up to 40 mm in one month.