Climate Risk and Early Warning Systems (CREWS) for Papua New Guinea

Developing and least developed countries are particularly vulnerable to the impact of climate change and climate extremes, including drought. In Papua New Guinea (PNG), severe drought caused by the strong El Niño in 2015–2016 affected about 40% of the population, with almost half a million people impacted by food shortages. Recognizing the urgency of enhancing early warning systems to assist vulnerable countries with climate change adaptation, the Climate Risk and Early Warning Systems (CREWS) international initiative has been established. In this chapter, the CREWS-PNG project is described. The CREWS-PNG project aims to develop an improved drought monitoring and early warning system, running operationally through a collaboration between PNG National Weather Services (NWS), the Australian Bureau of Meteorology and the World Meteorological Organization that will enable better strategic decision-making for agriculture, water management, health and other climate-sensitive sectors. It is shown that current dynamical climate models can provide skillful predictions of regional rainfall at least 3 months in advance. Dynamical climate model-based forecast products are disseminated through a range of Web-based information tools. It is demonstrated that seasonal climate prediction is an effective solution to assist governments and local communities with informed decision-making in adaptation to climate variability and change

Food shortages are associated with droughts, floods, frosts and ENSO in Papua New Guinea

In Papua New Guinea extreme climate events have occasionally led to the collapse of normal subsistence food production systems causing large scale food shortages that threaten human health and survival (e.g. during the 1997 El Niño drought). Production of staple foods (e.g. sweet potato) and cash crops (e.g. coffee) are adversely affected by drought,water logging and frost.We investigated the association between El Nino Southern Oscillation (ENSO), extreme climate events and reported food shortages. Over the 120 year period between 1890 and 2009, there have been 15 widespread droughts and 13 of these were associated with El Niño events, and eight of the 12 widespread floods were associated with La Niña events. On a national scale droughts were associated with El Niño systems and wet events were associated with La Niña systems. Since the early 1900s eleven major and widespread food shortages have been reported in the highlands but they have not been associated with drought alone but also with water surplus and frost. Eight of the eleven widespread food shortages were associated with El Niño years (1997, 1987, 1982, 1972, 1965, 1941, 1932, 1911–14) and four of these were preceded by La Niña events (1996, 1971, 1964, 1910). There was evidence of anomalous frosts at lower altitudes (1450 m) and more frequent frosts at higher altitudes (N2200 m) during clear skies in El Niño droughts that also contributed to food shortages. It is a combination of climatic extremes that causes the damage to crops that leads to a shortage of subsistence food in the highlands. The Standardised Precipitation Index provided a useful warning of success of more than 60% for El Niño droughts in 10 of the 18 locations; however the success rates of La Niña flood warnings at these locations was lower (b60%). Using seasonal climate forecasts based on ENSO and climate integrated crop models may provide early warning for farmers, industry agencies and government to help prepare for food shortages. Strategies that can help subsistence farmers cope with extreme climate events and the use, and value of seasonal climate forecast information are discussed

ENSO-related rainfall changes over the New Guinea region

The large-scale nature of El Niño-Southern Oscillation (ENSO) impacts on rainfall in the western Pacific region is generally well known but in some regions, where there are relatively few observations and the terrain is mountainous, the details of the impacts are less obvious. Here we analyze rainfall data for the New Guinea region comprising station observations, reanalysis products, and satellite-based estimates in order to better understand some of these details. We find that most gridded products are limited due to their relatively coarse horizontal resolutions that fail to resolve topographic effects. However, the relatively fine resolution Tropical Rainfall Measurement Mission satellite-based product appears to provide reliable estimates and linear correlations between the data and the NINO34 sea surface temperature index provides an insight into the pattern of ENSO rainfall impacts. The first major finding is that the correlation patterns reveal that some highland regions are impacted differently to other surrounding regions, most likely because of the interaction between winds and topography. Second, we find that the association between ENSO and rainfall for stations in the New Ireland/New Britain region tends to be nonlinear, in the sense that warm (El Niño)/cool (La Niña) events cause a decrease in rainfall - the strong 2010-2011 La Niña event being a clear example. Both findings help explain why previous studies have tended not to identify a simple large-scale response of New Guinea rainfall to ENSO. Key Points Patterns of ENSO-related rainfall impacts over New Guinea are complex. Satellite-based rainfall estimates (TRMM)provide details about these impacts. There is evidence of non-linear relationships for some regions. ©2013. American Geophysical Union. All Rights Reserved

Effect of Climate Factors on the Childhood Pneumonia in Papua New Guinea: A Time-Series Analysis

This study aimed to assess the association between climate factors and the incidence of childhood pneumonia in Papua New Guinea quantitatively and to evaluate the variability of the effect size according to their geographic properties. The pneumonia incidence in children under five-year and meteorological factors were obtained from six areas, including monthly rainfall and the monthly average daily maximum temperatures during the period from 1997 to 2006 from national health surveillance data. A generalized linear model was applied to measure the effect size of local and regional climate factor. The pooled risk of pneumonia in children per every 10 mm increase of rainfall was 0.24% (95% confidence interval: −0.01%–0.50%), and risk per every 1 °C increase of the monthly mean of the maximum daily temperatures was 4.88% (95% CI: 1.57–8.30). Southern oscillation index and dipole mode index showed an overall negative effect on childhood pneumonia incidence, −0.57% and −4.30%, respectively, and the risk of pneumonia was higher in the dry season than in the rainy season (pooled effect: 12.08%). There was a variability in the relationship between climate factors and pneumonia which is assumed to reflect distribution of the determinants of and vulnerability to pneumonia in the community

Effect of Climate Factors on the Childhood Pneumonia in Papua New Guinea: A Time-Series Analysis

This study aimed to assess the association between climate factors and the incidence of childhood pneumonia in Papua New Guinea quantitatively and to evaluate the variability of the effect size according to their geographic properties. The pneumonia incidence in children under five-year and meteorological factors were obtained from six areas, including monthly rainfall and the monthly average daily maximum temperatures during the period from 1997 to 2006 from national health surveillance data. A generalized linear model was applied to measure the effect size of local and regional climate factor. The pooled risk of pneumonia in children per every 10 mm increase of rainfall was 0.24% (95% confidence interval: −0.01%–0.50%), and risk per every 1 °C increase of the monthly mean of the maximum daily temperatures was 4.88% (95% CI: 1.57–8.30). Southern oscillation index and dipole mode index showed an overall negative effect on childhood pneumonia incidence, −0.57% and −4.30%, respectively, and the risk of pneumonia was higher in the dry season than in the rainy season (pooled effect: 12.08%). There was a variability in the relationship between climate factors and pneumonia which is assumed to reflect distribution of the determinants of and vulnerability to pneumonia in the community