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\"Since 1980, the number of climate-related disasters has been greatly increased glocally. Scientific consensus based on the IPCC fifth report suggested that global warming would bring more intense and frequent extreme climate events. These climate-related disasters hinder the achievement of sustainable economic growth and prosperity by disrupting supply chains, impeding production, destroying infrastructure, and necessitating high-cost rebuilding and recovery. To mitigate the climate extreme risks and possible losses, it is essential to maximize the utilization of scientific outputs and to share best practices in disaster risk management. Aligned with such purposes, Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) hosts the APEC Climate Symposium (APCS) every year. APCS focused on drought predction and management in 2013, climate extremes and hydrological disaster in 2014, and efficient use of climate information for disaster risk management in 2015. This book aims to compile some of the important results from the latest research in climate extreme prediction and services and its application studies with a focus on climate extremes such as typhoons, droughts, and floods based on the APCS presentations during 2013-2015\"-- Page 4 of cover.

Drought is known as a “creeping disaster” because drought impacts are usually noticed months or years after a drought begins. In the Pacific Island Countries and Territories (PICTs), there is almost no ability to tell when a drought will begin or end, especially for droughts other than meteorological droughts. Monitoring, forecasting and managing drought in the PICTs is complex due to the variety of different ways droughts occur, and the diverse direct and indirect causes and consequences of drought, across the PICT region. For example, the impacts of drought across the PICTs vary significantly depending on (i) the type of drought (e.g. meteorological drought or agricultural drought); (ii) the location (e.g. high islands versus atolls); (iii) socioeconomic conditions in the location affected by drought; and (iv) cultural attitudes towards the causes of drought (e.g. a punishment from God versus a natural process that is potentially predictable and something that can be managed). This paper summarises what is known and unknown about drought impacts in the PICTs and provides recommendations to guide future research and investment towards minimising the negative impacts of droughts when they inevitably occur in the PICTs.

A better understanding of precipitation dynamics in the Indian subcontinent is required since India's society depends heavily on reliable monsoon forecasts. We introduce a non-linear, multiscale approach, based on wavelets and event synchronization, for unravelling teleconnection influences on precipitation. We consider those climate patterns with the highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple timescales. We find substantial variation across India and across timescales. In particular, El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the south-east at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation, particularly in the northern regions. The effect of the Pacific Decadal Oscillation (PDO) stretches across the whole country, whereas the Atlantic Multidecadal Oscillation (AMO) influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improve precipitation forecasting.

The destruction caused by tropical cyclone (TC) Pam in March 2015 is considered one of the worst natural disasters in the history of Vanuatu. It has highlighted the need for a better understanding of TC impacts and adaptation in the Southwest Pacific (SWP) region. Therefore, the key aims of this study are to (i) understand local perceptions of TC activity, (ii) investigate impacts of TC activity and (iii) uncover adaptation strategies used to offset the impacts of TCs. To address these aims, a survey (with 130 participants from urban areas) was conducted across three SWP small island states (SISs): Fiji, Vanuatu and Tonga (FVT). It was found that respondents generally had a high level of risk perception and awareness of TCs and the associated physical impacts, but lacked an understanding of the underlying weather conditions. Responses highlighted that current methods of adaptation generally occur at the local level, immediately prior to a TC event (preparation of property, gathering of food, finding a safe place to shelter). However higher level adaptation measures (such as the modification to building structures) may reduce vulnerability further. Finally, we discuss the potential of utilising weather-related traditional knowledge and non-traditional knowledge of empirical and climate-model-based weather forecasts to improve TC outlooks, which would ultimately reduce vulnerability and increase adaptive capacity. Importantly, lessons learned from this study may result in the modification and/or development of existing adaptation strategies.

Rainfall estimation over the Pacific region is difficult due to the large distances between rain gauges and the high convection nature of many rainfall events. This study evaluates space-based rainfall observations over the South West Pacific Region from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP), the USA National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH), the Climate Hazards group Infrared Precipitation with Stations (CHIRPS), and the National Aeronautics and Space Administration’s (NASA) Integrated Multi-Satellite Retrievals for GPM (IMERG). The technique of collocation analysis (CA) is used to compare the performance of monthly satellite precipitation estimates (SPEs). Multi-Source Weighted-Ensemble Precipitation (MSWEP) was used as a reference dataset to compare with each SPE. European Centre for Medium-range Weather Forecasts’ (ECMWF) ERA5 reanalysis was also combined with Soil Moisture-2-Rain–ASCAT (SM2RAIN–ASCAT) to perform triple CA for the six sub-regions of Fiji, New Caledonia, Papua New Guinea (PNG), the Solomon Islands, Timor, and Vanuatu. It was found that GSMaP performed best over low rain gauge density areas, including mountainous areas of PNG (the cross-correlation, CC = 0.64), and the Solomon Islands (CC = 0.74). CHIRPS had the most consistent performance (high correlations and low errors) across all six sub-regions in the study area. Based on the results, recommendations are made for the use of SPEs over the South West Pacific Region.

In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) reanalysis, TEMP and precipitation (PRE) from Climate Research Unit (CRU), and soil moisture (SM) and evapotranspiration (ET) from the Global Land Evaporation Amsterdam Model (GLEAM). In addition, four teleconnection indices were considered: El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). This study used the Mann–Kendall test and Sen’s slope estimator to analyze trends, alongside multiple linear regression to investigate the relationships between TEMP, RH, and key climatic variables—namely evapotranspiration (ET), soil moisture (SM), and precipitation (PRE)—as well as large-scale teleconnection indices (e.g., IOD, ENSO, PDO, and NAO) on annual and seasonal scales. The key findings are as follows: (1) mean annual TEMP exceeding 30 °C and RH less than 30% were concentrated in arid regions of the Sahelian–Sudano belt in West Africa (WAF), Central Africa (CAF) and North East Africa (NEAF). Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade−1). Winter (0.27 °C decade−1) and spring (0.20 °C decade−1) exhibited the strongest warming, followed by autumn (0.18 °C decade−1) and summer (0.10 °C decade−1). (4) RH trends showed stronger seasonal decline compared to annual changes, with reduction ranging from 5 to 10% per decade in certain seasons, and about 2% per decade annually. (5) Pearson correlation analysis demonstrated a strong negative relationship between TEMP and RH with a correlation coefficient of r = − 0.60. (6) Significant associations were also observed between TEMP/RH and both climatic variables (ET, SM, PRE) and large scale-teleconnection indices (ENSO, IOD, PDO, NAO), indicating that surface conditions may reflect a combination of local response and remote climate influences. However, further analysis is needed to distinguish the extent to which local variability is independently driven versus being a response to large-scale forcing. Overall, this research highlights the physical mechanism linking TEMP and RH trends and their climatic drivers, offering insights into how these changes may impact different ecological and socio-economic sectors.

We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors.

The coasts of the Italian peninsula have been recently affected by frequent damaging hydrological events driven by intense rainfall and deluges. The internal climatic mechanisms driving rainfall variability that generate these hydrological events in the Mediterranean are not fully understood. We investigated the simulation skill of a soft-computing approach to forecast extreme rainfalls in Naples (Italy). An annual series of daily maximum rainfall spanning the period between 1866 and 2016 was used for the design of ensemble projections in order to understand and quantify the uncertainty associated with interannual to interdecadal predictability. A predictable structure was first provided, and then elaborated by exponential smoothing for the purposes of training, validation, and forecast. For the time horizon between 2017 and 2066, the projections indicate a weak increase of daily maximum rainfalls, followed by almost the same pace as it was in the previous three decades, presenting remarkable wavelike variations with durations of more than one year. The forecasted pattern is coupled with variations attributed to internal climate modes, such as the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO).

This study aims to predict the seasonal TC track density over the South Pacific by combining the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) dynamical prediction system with a statistical model. The hybrid dynamical-statistical model is developed for each of the three clusters that represent major groups of TC best tracks in the South Pacific. The cross validation result from the MME hybrid model demonstrates moderate but statistically significant skills to predict TC numbers across all TC clusters, with correlation coefficients of 0.4 to 0.6 between the hindcasts and observations for 1982/1983 to 2008/2009. The prediction skill in the area east of about 170°E is significantly influenced by strong El Niño, whereas the skill in the southwest Pacific region mainly comes from the linear trend of TC number. The prediction skill of TC track density is particularly high in the region where there is climatological high TC track density around the area 160°E–180° and 20°S. Since this area has a mixed response with respect to ENSO, the prediction skill of TC track density is higher in non-ENSO years compared to that in ENSO years. Even though the cross-validation prediction skill is higher in the area east of about 170°E compared to other areas, this region shows less skill for track density based on the categorical verification due to huge influences by strong El Niño years. While prediction skill of the developed methodology varies across the region, it is important that the model demonstrates skill in the area where TC activity is high. Such a result has an important practical implication—improving the accuracy of seasonal forecast and providing communities at risk with advanced information which could assist with preparedness and disaster risk reduction.

The assimilated coastal wave data are useful for wave climate study, coastal engineering, and design for marine disaster protection. However, the assimilated coastal wave data are few. Here, wave analysis data produced by the JMA (Japan Meteorological Agency) and ERA5 wave data were compared with GPS (Global Positioning System) buoy-measured wave data. In addition, the accuracy of ERA5 wave data for various conditions was investigated. The accuracy of JMA analysis wave height was better than that of ERA5 wave height. The ERA5 wave height was underestimated as the wave height increased. The accuracy of the ERA5 wave height was significantly different in fetch-unlimited and fetch-limited conditions. The difference of the skill metrics between fetch-unlimited and fetch-limited conditions was due to the overestimation of the fetch in the ERA5 grid. This result also applied to the wave period.