The Impact of Obliquity Change on El Niño Southern Oscillation (ENSO) and La Niña Climate Episodes

An alternative physical mechanism is proposed to describe the occurrence of the episodic El Nino Southern Oscillation (ENSO) and La Nina climatic phenomena. This is based on the earthquake-perturbed obliquity change (EPOCH) model previously discovered as a major cause of the global climate change problem. Massive quakes impart a very strong oceanic force that can move the moon which in turn pulls the earth’s axis and change the planetary obliquity. Analysis of the annual geomagnetic north-pole shift and global seismic data revealed this previously undiscovered force. Using a higher obliquity in the global climate model EdGCM and constant greenhouse gas forcing showed that the seismic-induced polar motion and associated enhanced obliquity could be the major mechanism governing the mysterious climate anomalies attributed to El Nino and La Nina cycles.

Understanding the Extratropical Liquid Water Path Feedback in Mixed-Phase Clouds with an Idealized Global Climate Model

A negative shortwave cloud feedback associated with higher extratropical liquid water content in mixed-phase clouds is a common feature of global warming simulations, and multiple mechanisms have been hypothesized. A set of process-level experiments performed with an idealized global climate model (a dynamical core with passive water and cloud tracers and full Rotstayn-Klein single-moment microphysics) show that the common picture of the liquid water path (LWP) feedback in mixed-phase clouds being controlled by the amount of ice susceptible to phase change is not robust. Dynamic condensate processes—rather than static phase partitioning—directly change with warming, with varied impacts on liquid and ice amounts. Here, three principal mechanisms are responsible for the LWP response, namely higher adiabatic cloud water content, weaker liquid-to-ice conversion through the Bergeron-Findeisen process, and faster melting of ice and snow to rain. Only melting is accompanied by a substantial loss of ice, while the adiabatic cloud water content increase gives rise to a net increase in ice water path (IWP) such that total cloud water also increases without an accompanying decrease in precipitation efficiency. Perturbed parameter experiments with a wide range of climatological LWP and IWP demonstrate a strong dependence of the LWP feedback on the climatological LWP and independence from the climatological IWP and supercooled liquid fraction. This idealized setup allows for a clean isolation of mechanisms and paints a more nuanced picture of the extratropical mixed-phase cloud water feedback than simple phase change.

Impact of weather factors on hospital admission of hand, foot and mouth disease under climate change scenarios in Ho Chi Minh City.

Hand, foot, and mouth disease (HFMD) is one of the most common communicable diseases in Vietnam. The present study aims to examine the association between weather factors and HFMD in association with hospitalisation. Daily and weekly weather and HFMD data from 2013 to 2018 in Ho Chi Minh City were deployed. Poisson regression model combined with a distributed lag non-linear model (DLNM) was applied to examine the relationship between weather factors and HFMD. The forecasting model for HFMD was performed by using the Global Climate Model (GCM) and Yasushi Honda model. The result showed that the average daily temperature induces an increase in the risk of HFDM hospitalisation was 26°C- 30.1°C. The average daily humidity also caused increasing the risk of hospitalisation of HFMD was 75% - 85%. However, the average daily humidity <60% reduced the risk of getting HFMD. The study provides quantitative evidence that the incidence of HFMD cases was associated with meteorological variables including average daily temperature and daily humidity in Ho Chi Minh City. This findings implies that there is a need for building a public health policy for eliminating and mitigating climate change impact on community health in a resilient approach.

The association between high temperature and hospitalisations for cardiovascular diseases under climate change scenarios in Ho Chi Minh City

Global warming is anticipated to induce an increase in the frequency and intensity of hot days and heatwaves, which ultimately have effects on public health. The study aimed to identify the impacts of high temperature and developing climate forecasting projections focused on cardiovascular causes in Ho Chi Minh city (HCMC). The projections were built up based on updated climate scenarios in HCMC. Poisson regression model combined with a distributed lag non-linear model (DLNM) was applied. The forecasting model for cardiovascular causes was performed by using the Global Climate Model (GCM) and Yasushi Honda model. Result showed that the average daily temperature induces an increase in the risk of hospitalisation, in which temperature below 25.7°C reduced number of patients due to cardiovascular disease, meanwhile temperature above 25.7°C has increased hospitalisations. Heat waves over 31°C had the strongest impact on the > 60 years old elderly people after 5 days lag and decreased its impact consecutively to age groups of 41 to 60, 16 to 40, and less than 15 years old. The incremental prediction for the hospitalised cardiovascular disease cases based on the RCP4.5 scenario was 79,713 cases and based on the RCP8.5 scenario was 81,362 cases, respectively.

Meteorological Drought Analysis Using Global Climate Model and Drought Indicators in West of Iran

Climate change and global warming impact the frequency of droughts and supply systems. Therefore, it is necessary to conduct appropriate studies to evaluate the impact of climate change on weather patterns and drought. For this purpose, data from 6 synoptic stations located in the wet and temperate areas in the Zagros region in western Iran were used to construct four general atmospheric models including BCC-CSM1, CANESM2, HADGEM2-ES, NORESM1-M under representative concentration pathways (RCPs) 2.6, 4.5, and 8.5, for three future periods (2010-2039), (2040-2069) and (2070-2099). Then, spatio-temporal variations of drought severity and frequency were studied in the study area using SPI and SPEI indices in different periods up to 2100. The results showed the spatial extent of areas classified as extremely dry will increase by 47.9% in the first period compared to the base period. In the second and third periods, however, the severely dry class covers more area. Analysis of SPEI showed that drought will be more severe in all future periods. According to SPEI, drought frequency will increase by 2% according to the first period (2010-2039) relative to the base period (1984-2013), and by 0.3% in the second and third periods by 2099. The results of this study indicate that the severity, frequency, and impacts of drought will increase in the study area until the end of the century. Therefore, appropriate measures should be taken to control and reduce its potential effects in the future.

On the parallelisation of weather and climate models

The numerical models used for weather forecasting and climate studies need very large computing resources. The current research in the field indicates that for accurate forecasts, one needs to use models at very high resolution, sophisticated data assimilation techniques and physical parameterisation schemes and multi-model ensemble integrations. In fact the spatial resolution required for accurate forecasts may demand computing power which is prohibitively high considering the processing power of a single processor of any supercomputer. During the last two decades, the developments in computing technology show the emergence of parallel computers with a number of processors which are capable of supplying enormously large computing power as against a single computer. Today, a cluster of workstations or personal computers can be used in parallel to integrate a global climate model for a long time. However, there are bottlenecks to be overcome in order to achieve maximum efficiency. Inter-processor communication is the key issue in case of global weather and climate models. The present paper aims at discussing the status of parallelisation of weather and climate models at leading centres of operational forecasting and research, the inherent parallelism in weather and climate models, the problems encountered in inter-processing communication and various ways of achieving maximum parallel efficiency.

Koreksi Bias Data Hujan Luaran GCM ECHAM5 Untuk Prediksi Curah Hujan Bulanan dan Musiman Pulau Lombok

This study aims to evaluate the ability of the ECHAM5 GCM model output data in estimating monthly rainfall on the island of Lombok. The data used in this study are ECHAM5 monthly rainfall data and automatic rainfall recorder (ARR) measurement rain data for 2000-2018 obtained from ARR Gunung Sari. Correction of bias is conducted by using the mean ratio method and the regression method. The method that produces the best approach is then used to obtain rain data projections and a simple regression method. Evaluation and validation used the Pearson correlation coefficient (r), Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) values. The results obtained are that the daily and monthly rainfall data from the ECHAM5 model cannot be directly used to replace the rain measurement data because of its very low accuracy. The downscaling technique performed on daily and monthly rainfall data using the average ratio method does not show satisfactory performance where the efficiency figures produced are still low even gave a slight increasing number. However, the ECHAM5 model data can be used to obtain rainfall projections on a monthly and seasonal scale with a good and satisfactory correlation. Key words: mean ratio method; global climate model; ECHAM5; monthly rainfall.