The Gap Between Weather and Climate Forecasting: Sub-Seasonal to Seasonal Prediction is an ideal reference for researchers and practitioners across the range of disciplines involved in the science, modeling, forecasting and application of this new frontier in sub-seasonal to seasonal (S2S) prediction. It provides an accessible, yet rigorous, introduction to the scientific principles and sources of predictability through the unique challenges of numerical simulation and forecasting with state-of-science modeling codes and supercomputers. Additional coverage includes the prospects for developing applications to trigger early action decisions to lessen weather catastrophes, minimize costly damage, and optimize operator decisions.
The book consists of a set of contributed chapters solicited from experts and leaders in the fields of S2S predictability science, numerical modeling, operational forecasting, and developing application sectors. The introduction and conclusion, written by the co-editors, provides historical perspective, unique synthesis and prospects, and emerging opportunities in this exciting, complex and interdisciplinary field.
Author: Thompkins, A.; R. Lowe; H. Nissan et. al.
Published Date: 2018-10
Topic (s): Sub-seasonal to seasonal prediction; health
Publication Type: Book chapter
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Coffee leaf rust (CLR) is the most destructive coffee disease in the world (Luaces et al., 2011) and has negatively impacted coffee production since the late 1800s (McCook, 2006). CLR damages coffee plants and decreases yields, which in turn reduces labor, influences wages, affects market prices, and inhibits farmers’ ability to manage their farms. The cumulative effects reduce farmer income, affecting livelihoods and food security, and force some to abandon their farms or switch to different crops altogether (WCR, 2014).
In recent years, CLR epidemics have been particularly damaging in Latin American and the Caribbean (Avelino et al., 2015). In 2012–2013, CLR epidemics cost farmers in these regions an estimated $500 million in lost production alone (ICO, 2013) and led to reduced production for at least two years (Avelino et al., 2015). Efforts to minimize the impacts of future outbreaks have led to research on new coffee varieties and, in the case of Colombia, large-scale programs to replace susceptible varieties with more resistant ones (De Silva and Tisdell, 1988; Arneson, 2000; Avelino et al., 2015). Capacitybuilding efforts that enable better management also have been implemented (e.g. De Silva and Tisdell, 1988; Staver et al., 2001; Shiomi et al., 2002; Santamaria and Bayman, 2005; Jackson et al., 2012; Zambolim et al., 2016). These efforts will be aided by the development of early warning systems (e.g. Alves et al., 2011; Luaces et al., 2011; Perez-Ariza et al., 2012; Avelino et al., 2015) and decision-support tools (e.g. Meira et al., 2009; Cintra et al., 2011). The provision of climate-related information has been offered as a vital element in these efforts (Avelino et al., 2015).
The causes of and responses to CLR are complex and demonstrate the multi-faceted relationship between disease characteristics, environmental conditions, climate and weather triggers, and the human actions that promote or hinder the disease. Although there are many factors to consider when managing CLR, climate and weather information has the potential to help farmers with disease management, but has thus far been under used. This document is step toward assessing the state of knowledge roles of weather and climate in supporting the growth and spread of CLR. It draws from more than 50 peer-reviewed articles, reports, and presentations related to CLR and coffee management to provide a summary of the current state of knowledge on the climate and weather influences on CLR.
Author: Rountree, Valerie. Zack Guido.
Published Date: 2016-05
Topic (s): Coffee leaf rust, disease management, agriculture, climate
Publication Type: Fact sheet
Download: IRAP-fact-sheet_updated_26Jun16.pdf
Starting in 2014, Jamaica has been in one of the worst droughts recorded since the 1970s. The drought’s effects on rural livelihood and the Jamaican economy have been devastating. According to widely published reports, the annual agricultural production declined by 30% in 2014 relative to 2013. This, along with brush fires, resulted in $1 billion loss for the economy. In response to the drought, the Jamaican Meteorological Service (JMS), in collaboration with the International Research Institute for Climate and Society (IRI) produced new seasonal drought- related forecast information. The information was provided to over 300 farmers during June 2014-June 2015 by JMS with the help of the Rural Agricultural Development Authority (RADA). The farmers received the information through farmer forums, phone text messages, extension agents, and by contacting the JMS. While anecdotal stories suggest that the losses in agricultural production might have been much greater if not for the provision of the information service by the JMS, they do not constitute robust evidence regarding the economic benefit of the information service. The goal of this study is to evaluate the economic impact of the service provided.
Author: Rahman, Tauhidur. James Buizer. Zackry Guido.
Published Date: 2016-02.
Topic (s): economy, seasonal drought, agriculture, climate services, Jamaica
Publication Type: Final report
Download: Economic-Impact-of-Drought_Information_Service_FINAL.pdf
A Special Issue of the Journal, Earth Perspectives: Transdisciplinarity Enabled on the role of the International Research Institute for Climate and Society (IRI) in Shaping Climate Services was published on June 14th, 2014.
The articles are open source and available online at: http://www.earth-perspectives.com/series/SLCS.
Author: Vaughan, Lisa. IRAP Project Manager.
Published Date: 2014-06
Topic (s): IRI, climate services
Publication Type: Journal
Boundary organizations, knowledge networks, and information brokers have been suggested as mechanisms that help integrate information into decision-making and enhance interactions between the producers and users of climate information. While these mechanisms have been discussed in many studies in disparate fields of research, there has been little empirical research describing how they relate and support each other within studies on climate services. In this paper, two Caribbean Regional Climate Outlook Forums (CariCOFs) convened in 2014 are studied. CariCOFs facilitate the production of regional seasonal climate information and the dissemination of it to a diverse climate and socioeconomic region. Network analysis, key informant interviews, and small group discussions were used to answer two questions: 1) what are the barriers to using seasonal climate forecasts (SCFs) by CariCOF participants and 2) what are the iterative processes of information exchange that address these barriers? The barriers to using SCF include difficulty in demonstrating the value of the forecast to potential users, difficulty in interpreting and explaining the forecast to others, and challenges associated with the scientific language used in the information. To address these constraints, the convener of the CariCOF acts as a boundary organization by enabling interactions between participants representing diverse sectoral and geographic settings. This develops a network that helps build shared scientific understanding and knowledge about how different sectors experience climate risk. These interactions guide information brokering activities that help individuals communicate and translate climate information to facilitate understanding at local levels.
Journal: Weather, Climate, and Society: http://journals.ametsoc.org/doi/10.1175/WCAS-D-15-0076.1
Author: Guido, Zack. Valerie Rountree. Christina Greene. Andrea Gerlak. Adrian Trotman
Published Date: 2016-06
Topic (s): boundary organizations, knowledge networks
A cluster analysis is applied to National Oceanic and Atmospheric Administration daily outgoing longwave radiation anomaly fields over the Intra‐American Seas, for the May to November rainy season 1980–2009. Seven recurrent convection regimes are identified, each with distinct impacts on local rainfall. Three suppressed‐convection regimes prevailing throughout the season and in particular during the Mid‐Summer Drought are related to transient anticyclonic circulation anomalies and broad drying over the region. The remaining regimes are all related to enhanced convection and cyclonic circulation anomalies over the Caribbean. For one wet regime, the cyclonic anomaly is located over Central America, which increases moisture advection from the eastern Pacific and in turn rainfall over Central and South America to the disadvantage of northern regions of the Caribbean. The three other regimes are associated with a weaker Caribbean Low Level Jet along its southern branch stretching along the South American coast, while its northern branch is strengthened, exposing the Caribbean to more moisture advection from the northeast trade winds, enhancing convection and rainfall locally. These three wet regimes are related to northwestward‐propagating convective cells that can be traced in a composite sense to the southward incursion of baroclinic waves from the midlatitudes, and anticyclonic wave breaking. In addition, their frequencies are found to be higher during phases 1 and 2 of the Madden‐Julian Oscillation, suggesting a connection with easterly waves emanating from African convection. Relationships are shown between these three northwestward‐propagating wet regimes and historical floods in the Caribbean illustrating the potential value of the convective regime approach for ultimately improving regional predictions and disaster early warning on sub‐seasonal scales.
Author: Vigaud, N. and A.W.Robertson
Published Date: 2017-03
Topic (s): sub-seasonal convection variability; weather typing; tropical-midlatitudes interactions; IAS rainfall; Caribbean floods
This study examines the dynamics of late spring rainfall (the Early Rainy Season, ERS) in the Caribbean region, in hopes of identifying mechanistic‐based predictors for low‐frequency climate modulations of the system. The subtropical Caribbean rain‐belt develops in May as seasonal warming proceeds. By July, the rain‐belt retreats north apparently following the westerlies and their vigorous synoptic disturbances. Daily climatology data suggest a physical definition of the Caribbean ERS as mid‐May to mid‐late June. Based on an examination of daily loops for several seasons, we hypothesize that rainfall occurs quasi‐randomly throughout tongues of air with sufficiently high (above 45–50 mm) precipitable water (PW). These moist airmasses are brought north from the deep tropics by low‐level southerlies, and typically bent over into SW‐NE bands by latitudinal shear of the westerlies. The low‐level flow that transports PW tongues is partly induced by upper‐level synoptic disturbances in the westerlies, but also involves the gentle persistent flow around a geographically anchored Panama Low. While forced ascent is sometimes active ahead of these upper‐level troughs, convective and mesoscale processes can produce rain wherever PW is sufficient. In summary, we hypothesize that rainfall hinges largely on the Lagrangian statistics of moist air tongues. Comparison is drawn between the Caribbean rain‐belt and its East Asian counterpart (Meiyu‐Baiu), and other mechanisms and diagnostics from that literature are discussed. Statistical prediction exercises, based on mechanistically chosen predictors, could both test hypotheses and aid local agricultural interests in the region.
Author: Allen, Theodore L.; Brian E. Mapes
Published Date: 2017-06
Topic (s): Caribbean precipitation; Caribbean rain-belt; mid-summer drought; Caribbean early rainfall season; Caribbean; precipitation
In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.
Author: Coughlan de Perez, Erin; et. al.
Published Date: 2017-09
Topic (s): climate; flood forecasting; flood management; soil moisture; rainfall; emergency preparedness; floods; flooding; indicators; arid regions; arid environments; extreme values; rain; wet climates; disaster management; hydrology
This paper proposes a heat-wave definition for Bangladesh that could be used to trigger preparedness measures in a heat early warning system (HEWS) and explores the climate mechanisms associated with heat waves. A HEWS requires a definition of heat waves that is both related to human health outcomes and forecastable. No such definition has been developed for Bangladesh. Using a generalized additive regression model, a heat-wave definition is proposed that requires elevated minimum and maximum daily temperatures over the 95th percentile for 3 consecutive days, confirming the importance of nighttime conditions for health impacts. By this definition, death rates increase by about 20% during heat waves; this result can be used as an argument for public-health interventions to prevent heat-related deaths. Furthermore, predictability of these heat waves exists from weather to seasonal time scales, offering opportunities for a range of preparedness measures. Heat waves are associated with an absence of normal premonsoonal rainfall brought about by anomalously strong low-level westerly winds and weak southerlies, detectable up to approximately 10 days in advance. This circulation pattern occurs over a background of drier-than-normal conditions, with below-average soil moisture and precipitation throughout the heat-wave season from April to June. Low soil moisture increases the odds of heat-wave occurrence for 10–30 days, indicating that subseasonal forecasts of heat-wave risk may be possible by monitoring soil-moisture conditions.
Author: Nissan, Hannah; Katrin Burkart; Erin Coughlan de Perez; Maarten Van Aalst; Simon Mason
Published Date: 2017-10
Topic (s): climate prediction; seasonal forecasting; short-range prediction; emergency preparedness; policy
This study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low‐level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated.
Author: Alfaro, Eric J.; Xandre Chourio. Angel G. Munoz, Simon J. Mason
Published Date: 2017-11
Topic (s): seasonal climate prediction; precipitation; statistical models; MOS predictive schemes; canonical correlation analysis
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Lisa Vaughan Program Manager, International Research and Applications Project (IRAP) P: 1 (301) 734-1277 F: 1 (301) 713-0518 E: lisa.vaughan@noaa.gov
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Americans’ health, security and economic wellbeing are tied to climate and weather. Every day, we see communities grappling with environmental challenges due to unusual or extreme events related to climate and weather.