Intensity Of Extreme Events Research Articles

Effects of past and projected river discharge variability on freshwater production in an anadromous fish

AbstractKnowledge about population responses to environmental variability, including extreme climatic events, is crucial for understanding their current status and likely fate under future environmental change. The frequency and intensity of extreme events is projected to increase, especially in freshwater ecosystems.Anadromous fishes depend on freshwater habitats for spawning and juvenile rearing, making them sensitive to altered hydrologic regimes. Here, we evaluate the effect of past and projected variability in river hydrology on freshwater production of naturally spawning coho salmon populations from coastal river systems in Washington,USA.Using a stage‐based life‐cycle model, we show that juvenile production during freshwater residence depends on river flow characteristics. Most importantly, juvenile production is reduced by low minimum stream flows during summer.Based on climate model projections suggesting more extreme summer droughts in the region, we then simulate changes in river flow characteristics and quantify the effects of more frequent and severe low flows during summer on juvenile production as well as the harvest that can be sustained in these populations.Our results demonstrate that changes in hydrologic regimes due to environmental change, especially extreme low flows during summer, may significantly affect juvenile production in anadromous fishes such as coho salmon and the services these populations provide to their ecosystems and humans. Understanding population responses to extreme climatic events is thus essential for improving species conservation and risk assessments.

Shoreline Change Analysis in Parts of the Barrier–Lagoon and Mud Sections of Nigeria Coast

This study assesses the short- and long-term changing position of the shorelines along the barrier lagoon and mud section of the Nigeria coast using remote sensing techniques. Five shoreline positions, covering a 30-year period (1986–2016), were extracted from medium resolution multi-spectral Landsat satellite imageries using both manual and semi-automatic shoreline extraction methods. Approximately, 533 orthogonal transects were cast using DSAS at simple right angles along the entire coast at 250-m interval. The shoreline change analysis was calculated using the Net Shoreline Movement and the End Point Rate techniques. The results show that the shoreline is highly dynamic; with the average rate of erosion estimated to be 28.08[Formula: see text]m/year and the average rate of accretion estimated to be 20.56[Formula: see text]m/year. While the persistence of erosional tendencies was found mainly in the Okesiri-Abereke-Aiyetoro parts of the mud section of the shoreline, the accretional tendencies was found mainly in the Aboraji-Araromi (barrier lagoon) and the Ajegunle-Jinriwo-Awoye (mud section) parts of the shoreline. The high dynamism of the shoreline is mainly attributed to the increasing frequency of storm surges in the area with over 13 incidents experienced within this period. This study submits that addressing coastal erosion and flooding problems in Nigeria should be based on the system boundary model where the coastal process and dynamics are constantly monitored holistically rather than locally or regionally as it is currently being done. This will also ensure the incorporation of the extent, frequency and intensity of extreme event in the development of adaptation measures.

Spatial and temporal variations of precipitation during 1979–2015 in Jinan City, China

AbstractThe spatiotemporal variation of precipitation significantly affects regional hydrological processes and the management of water resources worldwide, indirectly contributing to an aggravation in the frequency and intensity of extreme events, especially in urban areas. To analyze the spatiotemporal variation characteristics of precipitation during 1979–2015 in Jinan City, the China Meteorological Forcing Dataset and 12 precipitation-related indices are adopted and analyzed by using Mann–Kendall trend test, Sen's slope estimator and Pettitt test in this study. The results show that: (1) the annual mean precipitation (AMP) shows a gradual increasing trend from the northern plain area to the southern mountainous area; (2) the heaviest summer precipitation occurs in the southern part of downtown with a high frequency, resulting in the drastic amplification of urban rainstorm flood disasters; (3) the spatial distributions of most indices show a gradual increasing trend from the northern plain area to the southern mountainous area, while consecutive dry days show the opposite tendency; and (4) most indices roughly show similar spatiotemporal variation characteristics with AMP, i.e., decreases in southwestern area, but increases in the eastern mountainous region and the north plain area, exhibiting an overall increasing trend at the 1% significance level.

Identifying drought- and flood-prone areas based on significant changes in daily precipitation over Iran

Variations in frequency and intensity of extreme events have substantial impact on water resources and environment, which in turn are reflected on agriculture, society, and economy. We assessed spatiotemporal changes in pattern of daily precipitation to identify drought- and flood-prone areas of Iran. To do this, we generated gridded daily precipitation for the period of 1962–2013 over Iran using measured daily precipitation and the Kriging approach. We applied 11 precipitation indices that were stated by the Expert Team on Climate Change Detection and Indices (ETCCDI) to identify significant changes in frequency and intensity of extreme precipitation events. According to significant changes of these 11 precipitation indices, drought- and flood-prone areas of Iran were, then, detected. We observed significant changes in pattern of daily precipitation over more than half of the country. 40% of the country, which were located in the elevated regions of Iran, particularly along Zagros Mountain, was identified as flood-prone areas. As a result, in these regions, there is a need for flood risk management based on changes in stormwater events such as runoff generated from rain on snow and snowmelt events. In addition, we detected drought-prone areas in large portion of the northwest of Iran and in the low elevated regions of the country that have semiarid or arid climate. This suggests that it is necessary to prepare a long-term drought plan to mitigate impacts of severe drought events.

SPREAD: a high-resolution daily gridded precipitation dataset for Spain – an extreme events frequency and intensity overview

Abstract. A high-resolution daily gridded precipitation dataset was built from raw data of 12 858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary islands. The original data were quality-controlled and gaps were filled on each day and location independently. Using the serially complete dataset, a grid with a 5 × 5 km spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterise the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The Mediterranean coast and the Central Range showed the highest frequency and intensity of extreme events, while the number of wet days and dry and wet spells followed a north-west to south-east gradient in peninsular Spain, from high to low values in the number of wet days and wet spells and reverse in dry spells. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that is difficult to achieve when using other methods, pre-selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/7393.

Changes in the patterns of extreme rainfall events in southern Brazil

ABSTRACTIn this study we have examined changes in frequency and intensity of extreme rainfall events for the state of Santa Catarina (SC) in southern Brazil during austral spring and summer. The results show that changes in El Niño‐Southern Oscillation (ENSO) between 1979–1999 and 2000–2015 due to different phases of Interdecadal Pacific Oscillation have an impact on the teleconnection patterns from the Pacific to South America. As a consequence, precipitation over SC in the late period is less likely to be linked to ENSO. Moreover, there have been changes not only in the frequency and intensity of extreme events between the two periods but also in the mechanisms that cause the extremes affecting the spatial distribution of the extremes. In spring, the extreme events are less likely to occur during El Niño events and more frequent during La Niña and neutral years. The classical mechanism, of an enhanced South Atlantic Lower Level Jet (SALLJ) bringing moisture to SC, is no longer responsible for the extremes in the spring. The current main mechanism is associated with the presence of frontal systems during ENSO years and the presence of the South Atlantic Convergence Zone (SACZ) to the north combined with an anticyclonic system off the South American coast during neutral years. As a consequence the extreme events are currently more frequent at stations in the eastern side of SC. In summer during neutral years, SACZ events have become less frequent at the same time that atmospheric blocking events have become more frequent, explaining the decrease in extremes. On the other hand, SACZ events have become more frequent during La Niña years and so do the extremes in SC, with El Niño events playing a less important role.

How to apply the dependence structure analysis to extreme temperature and precipitation for disaster risk assessment

IPCC reports that a changing climate can affect the frequency and the intensity of extreme events. However, the extremes appear in the tail of the probability distribution. In order to know the relationship between extreme events in the tail of temperature and precipitation, an important but previously unobserved dependence structure is analyzed in this paper. Here, we examine the dependence structure by building a bivariate joint of Gumbel copula model for temperature and precipitation using monthly average temperature (T) and monthly precipitation (P) data from Beijing station in China covering a period of 1951–2015 and find the dependence structure can be divided into two sections, they are the middle part and the upper tail. We show that T and P have a strong positive correlation in the high tail section (T > 25.85 °C and P > 171.1 mm) (=0.66, p < 0.01) while they do not demonstrate the same relation in the other section, which suggests that the identification of a strong influence of T on extreme P needs help from the dependence structure analysis. We also find that in the high tail section, every 1 °C increase in T is associated with 73.45 mm increase in P. Our results suggested that extreme precipitation fluctuations by changes in temperature will allow the data dependence structure to be included in extreme affect for the disaster risk assessment under future climate change scenarios. Copula bivariate jointed probability distribution is useful to the dependence structure analysis.

Impacts of a Changing Climate on Economic Damages and Insurance

Weather and climate extremes cause huge economic damages and harm many lives each year (∼35000/year). There is evidence that some types of weather and climate extremes, like heat waves and flooding, have already increased or intensified over the last few decades, and climate projections reveal a further intensification for many types of weather and climate extremes in many regions though the uncertainties still remain large. While there is evidence for increases in economic losses it is uncertain whether this is due to an increase in the number and intensity of extreme events or can be attributed to socio-economic changes. However, there is agreement that the increasingly complex and internationally distributed supply chains of global companies make them more vulnerable to extreme weather events. It is also thought that global warming will adversely affect the global food supply chain and especially developing countries. Here I survey how weather and climate extremes impact our economies and how we can mitigate their impacts using financial and insurance products.

Fewer rainy days and more extreme rainfall by the end of the century in Southern Africa

Future changes in the structure of daily rainfall, especially the number of rainy days and the intensity of extreme events, are likely to induce major impacts on rain-fed agriculture in the tropics. In Africa this issue is of primary importance, but the agreement between climate models to simulate such descriptors of rainfall is generally poor. Here, we show that the climate models used for the fifth assessment report of IPCC simulate a marked decrease in the number of rainy days, together with a strong increase in the rainfall amounts during the 1% wettest days, by the end of the 21st century over Southern Africa. These combined changes lead to an apparent stability of seasonal totals, but are likely to alter the quality of the rainy season. These evolutions are due to the superposition of slowly-changing moisture fluxes, mainly supported by increased hygrometric capacity associated with global warming, and unchanged short-term atmospheric configurations in which extreme events are embedded. This could cause enhanced floods or droughts, stronger soil erosion and nutriment loss, questioning the sustainability of food security for the 300 million people currently living in Africa south of the Equator.

Soil erosion and global warming in India

Accelerated soil erosion, the rate in excess of the tolerable level of soil loss, constitutes serious environmental and agronomic risks in India. However, the available estimates of land area affected by erosion are highly variable and technique-dependent. Some estimates indicate the land area affected by soil erosion at 30.0 to 32.8 million hectare (Mha) by water erosion and 8.9 to 10.8 Mha by wind erosion. An additional land area affected by vegetation degradation by water erosion is estimated at ∼40 Mha. The magnitude of gross soil erosion by water is estimated at 5.1±0.4 Gt (15.6 t/ha) per year, of which 34.1±1.2% is deposited in the reservoirs. Relative contribution to sediments is 81% from rivers in the north and 19% from those in the south. Risks of soil degradation may be exacerbated with the projected climate change because of the increase in frequency and intensity of extreme events: the drought-flood syndrome. Soil erosion has on-site and off-site effects. On-site, erosion truncates the surface soil, depletes soil organic matter reserves and nutrient contents, reduces plant-available water retention capacity, decreases use efficiency of inputs and reduces agronomic productivity. Off-site, soil erosion aggravates non-point source pollution, exacerbates risks of toxic algal blooms and eutrophication, and increases emission of greenhouse gases into the atmosphere. Site-specific data, based on watershed-based longterm research, are needed to understand the fate of erosion-induced transport of soil carbon.

Riverine fishers' knowledge of extreme climatic events in the Brazilian Amazonia.

BackgroundClimate change is altering climate patterns, mainly increasing the frequency and intensity of extreme events with potentially serious impacts on natural resources and the people that use them. Adapting to such impacts will require the integration of scientific and local (folk) knowledge, especially the first-hand experiences and perceptions of resource users such as fishers. In this study, we identify how commercial riverine fishers in the Amazon remember extreme climatic events (flood and drought) and how they face the consequences of extreme events on fish availability.MethodsData were collected from the main Manaus fishery harbor between June and October of 2013. Semi-structured questionnaires and a historical timeline technique were used to gather data from artisanal commercial fishers. Fishers’ knowledge of extreme climate events was assessed by their “cultural consensus” for identification of event years and perceived impacts. Fishers’ responses were also compared to hydrological data to test their similarity.ResultsThere was a high level of cultural consensus among fishers about extreme events years. They were able to identify four consecutive unusual droughts, between 2009 and 2012. Elevated levels of fish mortality and decreases in the fishery were perceived as consequences of the drought events, as well as, a reduction in fish size, and disappearance of some species. Extreme flood events were associated with greater difficulties accessing fishing grounds.ConclusionsExtreme climatic events (floods and droughts) were remembered, and the recent increase in their intensity and frequency was also perceived. Moreover, extreme climate event (mainly droughts) impacts on fishery resources were also observed. Such information is potentially valuable for educational programs to further improve adaptation of local Amazonian fishing communities to future climate change, e.g. increasing local ecological knowledge using learning material based on their perception.

Role of El Niño Southern Oscillation (ENSO) in Extreme Event Related Adverse Health Outcomes in Maryland, USA

Introduction: Increasing body of literature suggests frequency, duration, and intensity of extreme events are rising and will increase in response to changing climate. Others have linked such extreme events with adverse health outcomes. Large-scale weather phenomenon (i.e., ENSO) is known to impact US weather patterns, but no studies to date have investigated how ENSO may modulate the associations between extreme events and adverse health outcomes. Methods: We linked hospitalization records for asthma/MI and culture-confirmed campylobacter/salmonella infections with extreme heat and precipitation events in Maryland. Extreme events were identified based on local climatology specific to each county derived from a 30-year baseline. We obtained data on phases of ENSO (El Niño, La Niña, Neutral) from the National Weather Service Climate Prediction Center. A time-stratified case-crossover design was used to examine associations between exposure to extreme events exposure and chronic health outcomes, while infection risk was assessed using multivariate negative binomial regression. We used stratified analysis to investigate how associations varied by ENSO phase. Results: There were 116,470 asthma and 138,343 MI hospitalizations and 4,804 and 9,527 reported infections of campylobacter and salmonella, respectively, during the study periods. Risk of salmonella and campylobacter associated with extreme heat and precipitation events was highest during the La Niña period. For asthma, risk associated with extreme heat and precipitation events was highest during El Niño. For MI, extreme heat related risk was highest during El Niño, while extreme precipitation related risk was highest during La Niña. Conclusion: Our results show extreme heat and precipitation related risk varies considerably during ENSO phases, which has a strong but uneven influence on weather across the globe. Studies investigating the link between climate change and health need to account for this phenomenon.

Interpreting the Climatic Effects on Xylem Functional Traits in Two Mediterranean Oak Species: The Role of Extreme Climatic Events.

In the Mediterranean region, the widely predicted rise in temperature, change in the precipitation pattern, and increase in the frequency of extreme climatic events are expected to alter the shape of ecological communities and to affect plant physiological processes that regulate ecosystem functioning. Although change in the mean values are important, there is increasing evidence that plant distribution, survival, and productivity respond to extremes rather than to the average climatic condition. The present study aims to assess the effects of both mean and extreme climatic conditions on radial growth and functional anatomical traits using long-term tree-ring time series of two co-existing Quercus spp. from a drought-prone site in Southern Italy. In particular, this is the first attempt to apply the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) technique and Bayesian modeling procedures to xylem traits data set, with the aim of (i) detecting non-linear long-term responses to climate and (ii) exploring relationships between climate extreme and xylem traits variability in terms of probability of occurrence. This study demonstrates the usefulness of long-term xylem trait chronologies as records of environmental conditions at annual resolution. Statistical analyses revealed that most of the variability in tree-ring width and specific hydraulic conductivity might be explained by cambial age. Additionally, results highlighted appreciable relationships between xylem traits and climate variability more than tree-ring width, supporting also the evidence that the plant hydraulic traits are closely linked to local climate extremes rather than average climatic conditions. We reported that the probability of extreme departure in specific hydraulic conductivity (Ks) rises at extreme values of Standardized Precipitation Index (SPI). Therefore, changing frequency or intensity of extreme events might overcome the adaptive limits of vascular transport, resulting in substantial reduction of hydraulic functionality and, hence increased incidence of xylem dysfunctions.

A comprehensive analysis of trends in extreme precipitation over southeastern coast of Brazil

ABSTRACTSoutheast Brazil (SE Brazil) is the most densely populated region in Brazil. Previous studies have shown evidence of positive trends in average precipitation and extreme events in a few locations, suggesting the increase in rainfall‐related hazards with potential impacts to urbanized areas of SE Brazil. This study provides a comprehensive analysis of the spatial variability of trends in extreme precipitation over SE Brazil focusing on regional and local scales. We examine two daily rainfall datasets with more than 70 years of data: individual stations and gridded observed precipitation data. Our results indicate that the frequency of both rainy days and extreme daily precipitation events have increased in Sao Paulo state. Conversely, precipitation has become more concentrated in fewer events in Rio de Janeiro and Espirito Santo states where both data sets indicate positive trends in the intensity of extreme daily rainfall. The increases in frequency and intensity of extreme events have both contributed to positive trends in total seasonal and average daily precipitation over Sao Paulo. Additionally, the individual stations indicate negative trends in the number of light rainy days over large urbanized areas in the state of Sao Paulo. The spatial patterns of trends indicate that they are influenced by the proximity of large urban centres and topographic features, and also suggest variations and changes in the major climatic systems affecting precipitation regimes over SE.

Climatology and trend analysis of extreme precipitation in subregions of Northeast Brazil

The present paper aims of computing climatology and trend analysis of occurrence and intensity of extreme events of precipitation in subregions of Northeast Brazil (NEB). We used daily rainfall data of 148 rain gauges collected from the hydrometeorological network managed by the National Water Agency during 1972 to 2002 and used quantiles technique in order to select rainfall events. Defining heavy rainfall events as those when at least one rain gauge recorded rainfall above the 95th percentile, normal rainfall was between the 45th and 55th percentiles, and weak rainfall events were under the 5th percentile. The Mann-Kendall nonparametric test was used to calculate the linear trend of the quantity and intensity of rainfall events. The NEB was divided in five subregions using the cluster analysis based on Euclidean distance and Ward’s method: Northern coast, Northern semiarid, Northwest, Southern semiarid, and Southern coast. The results suggest that the subregions are less influenced by El Nino and La Nina, and dry areas have higher variability, with the greatest number of intense events.

Instituting environmental sustainability and climate resilience into the governance process: Exploring the potential of new urban development schemes in India

India is on the cusp of major transformation in terms of how it translates and directs urbanization. The recently announced flagship schemes of the Government of India – the ‘Smart Cities’ scheme, the Atal Mission for Rejuvenation and Urban Transformation (AMRUT) scheme, The Housing for All scheme – offer avenues for addressing some of the most daunting challenges that cities in India face today, including climate change impacts and disaster risks. While India has faced multiple climate-related hazards in the recent years, the frequency and intensity of extreme events on the Indian subcontinent has arguably begun to increase. While there have been clear entry points in the governance system and policy making through which the climate resilience agenda could have been integrated into urban development planning in Indian cities, it has not yet been done, largely due to the lack of realization of this very need among India’s decision makers. This paper discusses these issues in detail and outlines the gaps and challenges for bringing climate resilience into the mainstream agenda. Furthermore, it explores the possible avenues or entry points for integrating climate resilience into the urban development agenda through these schemes.

Spatial and Temporal Variability of Rainfall in the Alps–Mediterranean Euroregion

AbstractThis study describes the main patterns of rainfall distribution in the Alps–Mediterranean “Euroregion” using a ground radar and characterizes the associated processes using model output. The radar dataset spans 2009–12 with fine spatial (1 km) and temporal (5 min) resolutions. The most significant rain accumulations were observed in 2009 and 2010, and the most intense extreme events occurred in 2010. Conversely, 2012 was a dry year. Model output revealed that the wind shear, the pressure, and the meridional wind at low level were the three main factors explaining the rainfall variability between 2009 and 2012. At the monthly scale, the maximum of rain accumulation was observed in November along the coast. Results also showed that the most intense rain rates were observed during early summer and autumn in the “Pre-Alps.” The monthly variability was characterized by a displacement of extreme rain events from land to sea from late spring to winter. Correlation analyses showed that this displacement was essentially controlled by the convective available potential energy (CAPE). Rainfall showed a diurnal variability from April to August for the land areas of the Alps–Mediterranean Euroregion. The diurnal variability was significant during the spring and summer months, with maximal rain intensity between 1600 and 1800 UTC. The correlation of the rainfall with CAPE showed that this cycle was related to atmospheric instability. A secondary peak in average rain rate was observed during the early morning and was likely triggered by land breezes. The results highlighted that rainfall characteristics are extremely diverse in terms of intensity and distribution in this relatively small region.

Climate Change and Livestock Production in India: Effects and Mitigation Strategies

Climate change poses formidable challenge to the development of livestock sector in India. The anticipated rise in temperature between 2.3°c and 4.8°c over the entire country together with increased precipitation resulting from climate change is likely to aggravate the heat stress in dairy animals, adversely affecting their productive and reproductive performance, and hence reducing the total area where high yielding dairy cattle can be economically reared. Given the vulnerability of India to rise in sea level, the impact of increased intensity of extreme events on the livestock sector would be large and devastating for the low-income rural areas. The predicted negative impact of climate change on Indian agriculture would also adversely affect livestock production by aggravating the feed and fodder shortages. The livestock sector which will be a sufferer of climate change is itself a large source of methane emissions, an important greenhouse gas. In India, although the emission rate per animal is much lower than the developed countries, due to vast livestock population the total annual methane emissions are about 9–10 Tg from enteric fermentation and animal wastes. Other direct or indirect effect such as feed resources, water resources and health contributed significantly to the change in production pattern in India`s livestock industry that support more than 70% of rural populace. It is suggested that strong and sound policies to be implemented to preserve local indigenous breeds of livestock, improvement of pasture, water resource management, research and development would mitigate the monumental consequences of climate change on livestock in India.

Uncertainty Analysis in the Evaluation of the DDF Curves Parameters in Climate Change Scenarios

On the global scale, there is a robust observational evidence that, over the last decades, the frequency and intensity of extreme events significantly changed, even if regional and local studies have highlighted complex and non-uniform spatial patterns. Climate change can cause increased rainfall intensities which leads to an additional impact on drainage systems, due to the alteration of magnitude and frequency of peak flows over their service life. For this reason, the design criteria of urban drainage infrastructures need to be revised and updated, in order to take into account the possible variations of extreme rainfall. In particular, the Depth-Duration-Frequency (DDF) curves, widely used in engineering to assess the return periods of rainfall events, require an adjustment for climate change. The main purpose of this study is to provide a methodology to assess the DDF curves parameters in climate change scenarios, once the evidence of a statistically significant trend in extreme rainfall was verified. Specifically, a Bayesian procedure has been applied for two cases of study located in the Sicily (Southern Italy), in order to incorporate the effects of extreme rainfall variations on the definition of DDF curve for a future climate condition and to evaluate the uncertainty related to such projection. The climate projection has been compared with a baseline scenario representative of current climate conditions. Finally, the implications of the uncertainty related to the DDF parameters estimation on the design of real detention tanks were analyzed, thus providing an evaluation of their hydraulic performance under the assumption of climate change. Results showed that the assessment of DDF parameters from historical extreme rainfall series could not be adequate to provide reliable estimations of future design storm. Therefore, the occurring climate change cannot be neglected in the design procedure based on extreme rainfall estimation.

Correlating MJO Activity with Argentina Rainfall and Atlantic Hurricanes Using ICI-RAFT

Knowledge of how the frequency and intensity of extreme events are affected by climate variability will contribute to answering questions related to drought and water availability that are important in water resources management. A tool titled the “International Center for Integrated Water Resources Management–Regional Analysis of Frequency Tool” (ICI-RAFT), which includes a database of several climate indices, was developed to accomplish this using rainfall observations on the ground. The method used was applied to rainfall obtained from 20 rain gauges in western central Argentina and hurricanes in the Atlantic Ocean during the respective active seasons. The global climate index that was found to have the greatest impact in both cases was the Madden-Julian Oscillation (MJO), which is the focus of the current study. Specifically, MJO activity over central Africa (MJO9) using a lag of 4 months and the western Indian Ocean (MJO10) using zero lag had the greatest influences, respectively. It was found that knowledge of the value of the MJO at these locations allows the prediction of below- or above-average rainfall or hurricane conditions for the current season and even allows a more precise quantitative estimate of this activity to be made.