Climate Outlook Research Articles

Ensemble Forecasts of Drought Indices Using a Conditional Residual Resampling Technique

AbstractThe historical climate record and seasonal temperature and precipitation records provide useful datasets for making short-term drought predictions. A variety of methods have exploited these resources, but few have quantitatively measured uncertainties associated with predictions of drought index values commonly used in management plans. In this paper, stochastic approaches for estimating uncertainty are applied to drought index predictions. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) seasonal forecasts and resampling of nearest-neighbor residuals are incorporated to measure uncertainty in monthly forecasts of Palmer drought severity index (PDSI) and standardized precipitation index (SPI) in central South Carolina. Kuiper skill scores of PDSI indicate good forecast performance with up to 3-month lead time and improvements for 1-month-lead SPI forecasts. NOAA CPC climate outlook improved the forecast skill by as much as 40%, and the degree of improvement varies by season and forecast lead time.

A review of climate risk information for adaptation and development planning

AbstractAlthough the use of climate scenarios for impact assessment has grown steadily since the 1990s, uptake of such information for adaptation is lagging by nearly a decade in terms of scientific output. Nonetheless, integration of climate risk information in development planning is now a priority for donor agencies because of the need to prepare for climate change impacts across different sectors and countries. This urgency stems from concerns that progress made against Millennium Development Goals (MDGs) could be threatened by anthropogenic climate change beyond 2015. Up to this time the human signal, though detectable and growing, will be a relatively small component of climate variability and change. This implies the need for a twin‐track approach: on the one hand, vulnerability assessments of social and economic strategies for coping with present climate extremes and variability, and, on the other hand, development of climate forecast tools and scenarios to evaluate sector‐specific, incremental changes in risk over the next few decades. This review starts by describing the climate outlook for the next couple of decades and the implications for adaptation assessments. We then review ways in which climate risk information is already being used in adaptation assessments and evaluate the strengths and weaknesses of three groups of techniques. Next we identify knowledge gaps and opportunities for improving the production and uptake of climate risk information for the 2020s. We assert that climate change scenarios can meet some, but not all, of the needs of adaptation planning. Even then, the choice of scenario technique must be matched to the intended application, taking into account local constraints of time, resources, human capacity and supporting infrastructure. We also show that much greater attention should be given to improving and critiquing models used for climate impact assessment, as standard practice. Finally, we highlight the over‐arching need for the scientific community to provide more information and guidance on adapting to the risks of climate variability and change over nearer time horizons (i.e. the 2020s). Although the focus of the review is on information provision and uptake in developing regions, it is clear that many developed countries are facing the same challenges. Copyright © 2009 Royal Meteorological Society

Downscaling 15-day Ensemble Weather Forecasts and Extension to Short-term Climate Outlooks

Downscaling 15-day Ensemble Weather Forecasts and Extension to Short-term Climate Outlooks

Learning from 10 Years of Climate Outlook Forums in Africa

As new projects and programs are proposed to promote climate adaptation and disaster risk reduction in Africa, it is important to learn from the successes and failures of the Climate Outlook Forums.

Climate Outlook to 2030

Our forecast for global average temperature to 2030 has been updated for the progression of Solar Cycle 23 and the contribution that will be made by increased carbon dioxide in the atmosphere. The increased length of Solar Cycle 23 supports the view that Solar Cycle 24 will be weak, with the consequence of increased certainty that that there will be a global average temperature decline in the range of 1° to 2°C for the forecast period. The projected increase of 40 ppm in atmospheric carbon dioxide to 2030 is calculated to contribute a global atmospheric temperature increase of 0.04°C. The anthropogenic contribution to climate change over the forecast period will be insignificant relative to natural cyclic variation.

Reliability of canola production in different rainfall zones of Western Australia

The area of canola in the wheat-based farming systems of the wheatbelt of Western Australia (WA) expanded rapidly during the 1990s and has subsequently decreased. Due to the short history of canola production in WA, there is little information on yield and oil content expectations in relation to rainfall, location, and soil type. In this paper we: (1) present the recent history of canola production in the context of the long-term climate record; (2) assess the effect of location, rainfall, soil type, and soil water at sowing on yield and oil content; and (3) determine cut-off sowing dates for profitable canola production. Simulations were run using the APSIM-Canola model with long-term climate records for 3 selected locations from the low-, medium-, and high-rainfall zones and different soil types. Analysis of recent trends in canola area showed that poor seasons and price volatility in the last few years have contributed to farmers’ perception of risk and hence the decline in area sown. Long-term simulations showed the importance of location, sowing date, soil type, and stored soil water at sowing on grain yield. Yield was negatively related to sowing date. Light-textured soils had lower yields and larger yield penalties with delayed sowing than heavy-textured soils. Soil water at sowing gave a yield advantage in most years in all locations studied, but especially in low- and medium-rainfall locations. Variation in oil content was most strongly affected by sowing date and location, while soil type and soil water at sowing had a minor effect. Long-term simulation analysis can be used as a tool to establish the latest possible sowing date to achieve profitable canola for different locations and soil types, given different canola prices and growing costs. Given the vulnerability of profitability to seasonal conditions, in the low- and medium-rainfall zone, the decision to grow canola should be tactical depending on stored soil water, sowing opportunities, seasonal climate outlook, prices, and costs. In contrast, in the high-rainfall zone, canola production is relatively low risk, and could become a reliable component of rotations.

Needs for applied climate education in agriculture

This paper reports on a purposive survey study which aimed to identify needs for the development, delivery and evaluation of applied climate education for targeted groups, to improve knowledge and skills to better manage under variable climatic conditions. The survey sample consisted of 80 producers and other industry stakeholders in Australia (including representatives from consulting, agricultural extension and agricultural education sectors), with a 58% response rate to the survey. The survey included an assessment of (i) knowledge levels of the Southern Oscillation Index and sea surface temperatures, and (ii) skill and ability in interpreting weather and climate parameters. Results showed that despite many of the respondents having more than 20 years experience in their industry, the only formal climate education or training undertaken by most was a 1-day workshop. Over 80% of the applied climate skills listed in the survey were regarded by respondents as essential or important, but only 42% of educators, 30% of consultants and 28% of producers rated themselves as competent in applying such skills. Essential skills were deemed as those that would enable respondents or their clients to be better prepared for the next extended wet or dry meteorological event, and improved capability in identifying and capitalising on key decision points from climate information and a seasonal climate outlook. The complex issue of forecast accuracy is a confounding obstacle for many in the application of climate information and forecasts in management. Addressing this problem by describing forecast ‘limitations and skill’ can help to overcome this problem. The survey also highlighted specific climatic tactical and strategic information collated from grazing, cropping and agribusiness enterprises, and showed the value of such information from a users perspective.

Who can eat information? Examining the effectiveness of seasonal climate forecasts and regional climate-risk management strategies

Southern Africa is a region facing multiple stressors, including chronic, recurrent food insecurity and persistent threats of famine. Climate information, including seasonal climate forecasts, has been heralded as a promising tool for early-warning systems and agricultural risk management in southern Africa. Nevertheless, there is concern that climate information, for example climate fore- casts, are not realizing their potential value in the region. The present study considers the actual and potential roles played by climate information in reducing food insecurity in southern Africa from 2 perspectives. The first relates to improved understanding of the contextual environment in which end users operate and use information. Users, including farmers, usually operate in an environment of considerable uncertainty, reacting to and coping with multiple stressors whose impacts are not always clear or predictable. The second perspective relates to improving the current design and vari- ety of mechanisms (e.g. climate outlook forums) for the dissemination and uptake of climate informa- tion. The first relates to improved understanding of the contextual environment in which end users operate and use information. Users, including farmers, usually operate in an environment of consid- erable uncertainty, reacting to and coping with multiple stressors whose impacts are not always clear or predictable. The second perspective relates to improving the current design and variety of mech- anisms (e.g. climate outlook forums) for the dissemination and uptake of climate information. Climate information, it is argued, used in isolation (e.g. in 'stand alone' climate outlook forums) and under- taken in a traditional, linear fashion, where information is moved from producer to user, is divorced from the broader, complex social context in which such information is embedded. This current articulation of climate information flow represents an ineffective means of dealing with climate vari- ability and food security. Alternative modes of interaction (e.g. using existing platforms to 'piggy- back' information or seeking appropriate 'boundary organisations') should be found to sustainably manage climate risks in the region.

Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications

AbstractEvaluating a range of scenarios that accurately reflect precipitation variability is critical for water resource applications. Inputs to these applications can be provided using location‐ and interval‐specific probability distributions. These distributions make it possible to estimate the likelihood of rainfall being within a specified range. In this paper, we demonstrate the feasibility of fitting cell‐by‐cell probability distributions to grids of monthly interpolated, continent‐wide data. Future work will then detail applications of these grids to improved satellite‐remote sensing of drought and interpretations of probabilistic climate outlook forum forecasts. The gamma distribution is well suited to these applications because it is fairly familiar to African scientists, and capable of representing a variety of distribution shapes. This study tests the goodness‐of‐fit using the Kolmogorov–Smirnov (KS) test, and compares these results against another distribution commonly used in rainfall events, the Weibull. The gamma distribution is suitable for roughly 98% of the locations over all months. The techniques and results presented in this study provide a foundation for use of the gamma distribution to generate drivers for various rain‐related models. These models are used as decision support tools for the management of water and agricultural resources as well as food reserves by providing decision makers with ways to evaluate the likelihood of various rainfall accumulations and assess different scenarios in Africa. Copyright © 2006 Royal Meteorological Society

The Role of an Advanced Land Model in Seasonal Dynamical Downscaling for Crop Model Application

Abstract An advanced land model [the National Center for Atmospheric Research (NCAR) Community Land Model, version 2 (CLM2)] is coupled to the Florida State University (FSU) regional spectral model to improve seasonal surface climate outlooks at very high spatial and temporal resolution and to examine its potential for crop yield estimation. The regional model domain is over the southeast United States and is run at 20-km resolution, roughly resolving the county level. Warm-season (March–September) simulations from the regional model coupled to the CLM2 are compared with those from the model with a simple land surface scheme (i.e., the original FSU model). In this comparison, two convective schemes are also used to evaluate their roles in simulating seasonal climate, primarily for rainfall. It is shown that the inclusion of the CLM2 produces consistently better seasonal climate scenarios of surface maximum and minimum temperatures, precipitation, and shortwave radiation, and hence provides superior inputs to a site-based crop model to simulate crop yields. The FSU regional model with the CLM2 exhibits some capability in the simulation of peanut (Arachis hypogaea L.) yields, depending upon the convective scheme employed and the site selected.

Operational forecasting of South African sugarcane production: Part 1 – System description

Commercial sugarcane crops in South Africa are grown under a wide range of agronomic and socio-economic conditions. These factors, together with climatic variation have resulted in a 17% variation in sugarcane production and there is considerable scope to improve productivity through accurate and timeous forecasts. This paper reports on the development of an operational crop forecasting system based on a simulation model. The country’s entire area of sugar production was subdivided into homogeneous climate zones using a wide range of data and expert opinion. These zones serve as simulation units within the system and model input and area aggregation data were obtained for each climate zone. Irrigation is simulated according to typical, zone specific strategies taking into account water use restrictions. Simulations of crops growing in the current year are completed using 10 historic seasons to substitute the remainder of the season. The selection of these seasons is based on the climate outlook. Reports containing information for national, regional and site specific cane production are generated and distributed to industry stakeholders. To the authors’ knowledge, this is the first national scale model-based operational yield forecasting system for sugarcane. Possible future improvements to the system may include stochastic input variables, more representative irrigation simulations, quantifying forecast uncertainty and providing suitable reference crop yield. The system is evaluated in another paper.

Application of seasonal climate outlooks to forecast sugarcane production in South Africa

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 30:239-246 (2006) - doi:10.3354/cr030239 Application of seasonal climate outlooks to forecast sugarcane production in South Africa C. N. Bezuidenhout*, R. E. Schulze School of Bioresources Engineering & Environmental Hydrology, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa *Email: bezuidenhoutc@ukzn.ac.za ABSTRACT: Sugarcane production is an economically important activity in South Africa. Climate variability causes high fluctuations in annual production, and stakeholders in the industry are often compelled to make crop management, harvesting, transport, milling and marketing decisions under high levels of uncertainty. In this study, a previously derived model-based yield forecasting system is used to produce hindcasts of sugarcane production in South Africa. The derivation of climate outlooks by the South African Weather Service (SAWS) and a technique to translate these into yield forecasts are explained. Model generated forecasts are consistently more accurate than forecasts based on climatology and, depending on the time of the year, capture between 11 and 58% of the natural variation in national sugarcane production. Climate outlook information generally enhances production forecasts, suggesting that these outlooks are sufficiently accurate for decision making and that the translation techniques perform satisfactorily. Different regions, however, display different accuracy responses to the climate outlook and several suggestions for future research are made, such as incorporating different climate forecasts and creating consortiums between climate forecasters, crop modellers and decision makers. KEY WORDS: Seasonal climate outlook · Production forecasting · Sugarcane · Climate forecast application Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 30, No. 3. Online publication date: April 26, 2006 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2006 Inter-Research.

Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa

Seasonal climate outlooks provide one tool to help decision-makers allocate resources in anticipation of poor, fair or good seasons. The aim of the 'Climate Outlooks and Agent-Based Simulation of Adaptation in South Africa' project has been to investigate whether individuals, who adapt gradually to annual climate variability, are better equipped to respond to longer-term climate variability and change in a sustainable manner. Seasonal climate outlooks provide information on expected annual rainfall and thus can be used to adjust seasonal agricultural strategies to respond to expected climate conditions. A case study of smallholder farmers in a village in Vhembe district, Limpopo Province, South Africa has been used to examine how such climate outlooks might influence agricultural strategies and how this climate information can be improved to be more useful to farmers. Empirical field data has been collected using surveys, participatory approaches and computer-based knowledge elicitation tools to investigate the drivers of decision-making with a focus on the role of climate, market and livelihood needs. This data is used in an agent-based social simulation which incorporates household agents with varying adaptation options which result in differing impacts on crop yields and thus food security, as a result of using or ignoring the seasonal outlook. Key variables are the skill of the forecast, the social communication of the forecast and the range of available household and community-based risk coping strategies. This research provides a novel approach for exploring adaptation within the context of climate change.

Consensus Seasonal Flood Forecasts and Warning Response System (FFWRS): An Alternate for Nonstructural Flood Management in Bangladesh

Despite advances in short-range flood forecasting and information dissemination systems in Bangladesh, the present system is less than satisfactory. This is because of short lead-time products, outdated dissemination networks, and lack of direct feedback from the end-user. One viable solution is to produce long-lead seasonal forecasts--the demand for which is significantly increasing in Bangladesh--and disseminate these products through the appropriate channels. As observed in other regions, the success of seasonal forecasts, in contrast to short-term forecast, depends on consensus among the participating institutions. The Flood Forecasting and Warning Response System (henceforth, FFWRS) has been found to be an important component in a comprehensive and participatory approach to seasonal flood management. A general consensus in producing seasonal forecasts can thus be achieved by enhancing the existing FFWRS. Therefore, the primary objective of this paper is to revisit and modify the framework of an ideal warning response system for issuance of consensus seasonal flood forecasts in Bangladesh. The five-stage FFWRS-i) Flood forecasting, ii) Forecast interpretation and message formulation, iii) Warning preparation and dissemination, iv) Responses, and v) Review and analysis-has been modified. To apply the concept of consensus forecast, a framework similar to that of the Southern African Regional Climate Outlook Forum (SARCOF) has been discussed. Finally, the need for a climate Outlook Fora has been emphasized for a comprehensive and participatory approach to seasonal flood hazard management in Bangladesh.

Evaluation of the Climate Outlook Forums' seasonal precipitation forecasts of southeast South America during 1998-2002

The series of 16 Climate Outlook Forums' (COFs') seasonal precipitation forecasts for southeast South America since December 1997 are verified. The COFs' forecasts consist of a probabilistic tercile distribution of precipitation for the upcoming 3 months period, over a domain in South America between 20 and 40°S, to the east of the Andes Mountains. The COFs' forecast is the result of the consensus agreement between atmosphere model predictions, physically based statistical model predictions, results of diagnostic analysis and published research on climate variability over the region, and expert interpretation of this information in the context of the current situation. The validation is conducted by means of the hit score (HS), which expresses the percentage of forecasts verified in the dominant predicted category, and the ranked probability skill Score (RPSS), which measures the difference between the probability distribution of the forecasts and the observed categories. The HS reveals that over most of the region the categorical agreement is low and close to 33%, which is the result that would be obtained by a three-category random forecast, and only sparse regions achieve a categorical agreement between 40 and 50%. The regions with positive RPSS (i.e. forecasts better than climatology and potentially useful for applications) represent 30% of the total area considered by the COFs. There are significantly large regions with negative RPSS, i.e. forecasts worse than climatology. The result of the COFs' forecasts is compared with that of the IRI Net Assessment forecasts, the only source of physically based seasonal forecasts regularly available at the COFs' discussions. There is general coincidence in the regions with positive and negative skill. Although one might expect the consensus of the COFs' forecasts to improve upon the skill of the IRI forecast, this was not the case during this period. Copyright © 2005 Royal Meteorological Society

Production risk of canola in the semi-arid subtropics of Australia

The area of canola is expanding in the wheat-based farming systems of the summer-dominant rainfall zone of the Australian wheatbelt. Despite this, there is little information on yield and oil content expectations in relation to rainfall, location, and soil type and the reliability of the crop in a region characterised by high climatic variability. In this paper we assess variation in profitability of canola production in locations with different rainfall in the north-eastern wheatbelt by using long-term simulations at 4 locations spanning the climatic range of the region (Gunnedah, Moree, Walgett, Roma) with a validated model (APSIM-Canola). A new semi-mechanistic method for simulating oil content, accounting for temperature and water deficit effects during grain filling, is described and tested. Key agronomic determinants of reliable grain yield and oil content are identified. Long-term simulations showed strong effects of location, plant-available soil water at sowing (PAW), and in-crop rainfall on grain yield expectations. Yield was negatively related to sowing date, particularly in those situations of high water supply (PAW and in-crop rainfall). Grain yield was positively related to in-crop rainfall up to 300 mm, with water use efficiency in most seasons falling between 6 and 12 kg/ha.mm. Variation in oil content was most strongly affected by sowing date, followed by location, with PAW having a minor effect. Importantly, the price bonus cut-off for oil content of 42% was exceeded in 25, 40, 40, and 55% of seasons for Roma, Walgett, Moree, and Gunnedah, respectively. Negative and falling phases of the SOI in April–May were associated with lower grain yield and oil contents, whereas positive and rising phases with higher grain yield and oil content. This suggests that the choice to sow canola over other alternatives could be a tactical decision that depends upon the seasonal climate outlook. The approach used in this paper can be applied to the analysis of canola production risk (yield and oil content) and profitability in other prospective environments.

Context evaluation: a profile of irrigator climate knowledge, needs and practices in the northern Murray - Darling Basin to aid development of climate-based decision support tools and information and dissemination of research

Understanding client needs, knowledge and practices offers a means of ensuring research outputs match intended audience requirements. This paper shows the initial impact of context evaluation on the development of a suite of decision support tools and information to help irrigators better manage their water resources under different climatic conditions. The context evaluation study involved a survey of ~170 irrigators in the northern Murray�Darling Basin in Australia. It sought to clarify how they make cropping area and water management decisions and their levels of understanding and use of climate information. We found irrigators consult widely on cropping decisions and those with large areas commonly apply the Southern Oscillation Index to property decisions. Respondents demonstrated a reasonably good understanding of climate phenomena in an Southern Oscillation Index knowledge test. Two-thirds use seasonal climate outlook information, but only 20% are very confident to apply climate information to decisions. More than half would find a decision support system (comprising tools and information) useful for cropping decisions. Almost 75% would change their crop area, and 43% their crop type, if given advance information about water availability up to 4 months ahead of irrigation season. About 70% have access to a computer and half to the internet, but two-thirds consider their personal computing skill is only nil or basic. Twenty-three percent of respondents expressed interest in working directly with the research team to interact regarding their requirements, indicating the potential for future participative research activities such as collaborative, on-farm research. The context evaluation facilitated formation of a focus group that cooperated to assess research findings and incorporate improvements to the project�s set of decision support tools. The evaluation was a new experience for the researchers and, albeit an arms-length consultation process, it has broadened our knowledge about our target audience and their preferred ways to access research findings.

Eastern Equatorial Pacific SST Variability: ENSO and Non-ENSO Components and Their Climatic Associations

Using an updated Kaplan et al. global SST anomaly (SSTA) dataset (1870-1999) a canonical representation of El Nino-Southern Oscillation (ENSO) is constructed. When this canonical ENSO is subtracted from the data, a residual (non-ENSO) dataset for SSTA is left that includes interseasonal to multidecadal variability. Over the eastern equatorial Pacific (Nino-3) the canonical ENSO accounts for about 79% of the total SSTA variability, and the residual, dominated by decadal timescales, accounts for the rest. In particular, about 40%-50% of the amplitudes of the strong 1982-83 and 1997-98 El Nino events were accounted for by the residual variability. The non-ENSO variability is characterized by the known shift from cold to warm eastern tropical Pacific in the mid- to late 1970s as well as by a (nonstationary) interannual variance increase during the 1980s and 1990s. Composite maps of surface (SST, sea level pressure, and winds) and tropospheric (divergent winds, velocity potential, and vertical velocity) variables are used to compare the spatial patterns characterizing the canonical ENSO and the residual components of the Nino-3 variability. The residual composites are found to only share large-amplitude fluctuations of SST anomalies in the equatorial Pacific east of the date line. When these com- posites are separated into decadal and interannual components, the decadal part closely resembles the structure of the Pacific decadal oscillation. The major patterns of tropospheric variability associated with the ENSO and decadal non-ENSO components are very different. At low latitudes, they imply nearly opposite impacts on far- field regional climates, based on their respective warming (or cooling) phases within the Nino-3 region. This unexpected result for low-latitude climate associations runs contrary to the naive expectation (recently shown to be true for North America) that a decadally warm tropical east Pacific will reinforce the climate effects associated with ENSO alone. This indicates that, in the Tropics, climate outlooks may be more accurate if based on separately analyzed relationships between these SSTA components and their associated climate fluctuations.

Integrating climate forecasts and natural gas supply information into a natural gas purchasing decision

AbstractThis paper illustrates a key lesson related to most uses of long‐range climate forecast information, namely that effective weather‐related decision‐making requires understanding and integration of weather information with other, often complex factors. Northern Illinois University's heating plant manager and staff meteorologist, along with a group of meteorology students, worked together to assess different types of available information that could be used in an autumn natural gas purchasing decision. Weather information assessed included the impact of ENSO events on winters in northern Illinois and the Climate Prediction Center's (CPC) long‐range climate outlooks. Non‐weather factors, such as the cost and available supplies of natural gas prior to the heating season, contribute to the complexity of the natural gas purchase decision. A decision tree was developed and it incorporated three parts: (a) natural gas supply levels, (b) the CPC long‐lead climate outlooks for the region, and (c) an ENSO model developed for DeKalb. The results were used to decide in autumn whether to lock in a price or ride the market each winter. The decision tree was tested for the period 1995–99, and returned a cost‐effective decision in three of the four winters. Copyright © 2000 Royal Meteorological Society

Using NOAA's New Climate Outlooks in Operational Hydrology

The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center recently began issuing new multiple long-lead outlooks of meteorological probabilities. Operational hydrology approaches for generating probabilistic hydrological outlooks must be compatible with these meteorological outlooks yet preserve spatial and temporal relationships observed in past meteorology. Many approaches, however, either limit the use of historical data to be compatible with meteorological outlooks or limit compatibility with the outlooks to allow fuller use of historical data. An operational hydrology approach that uses all historical data while remaining compatible with many of the new long-lead outlooks, in order of user priority, is described here. The approach builds a hypothetical very large structured set of possible future scenarios, to be treated as a “sample” from which to estimate outlook probabilities and other parameters. The use of this hypothetical set corresponds to the weighted use of a scenario set based on historical data. The determination of weights becomes an optimization problem for the general case. An example illustrates the concepts and method.