Roger Stone

Seasonal and Inter-Annual Climate Forecasting: The New Tool for Increasing Preparedness to Climate Variability and Change in Agricultural Planning and Operations

Climate variability and change affects individuals and societies. Within agricultural systems, seasonal climate forecasting can increase preparedness and lead to better social, economic and environmental outcomes. However, climate forecasting is not the panacea to all our problems in agriculture. Instead, it is one of many risk management tools that sometimes play an important role in decision-making. Understanding when, where and how to use this tool is a complex and multi-dimensional problem. To do this effectively, we suggest a participatory, cross-disciplinary research approach that brings together institutions (partnerships), disciplines (e.g., climate science, agricultural systems science, rural sociology and many other disciplines) and people (scientist, policy makers and direct beneficiaries) as equal partners to reap the benefits from climate knowledge. Climate science can provide insights into climatic processes, agricultural systems science can translate these insights into management options and rural sociology can help determine the options that are most feasible or desirable from a socio-economic perspective. Any scientific breakthroughs in climate forecasting capabilities are much more likely to have an immediate and positive impact if they are conducted and delivered within such a framework. While knowledge and understanding of the socio-economic circumstances is important and must be taken into account, the general approach of integrated systems science is generic and applicable in developed as well as in developing countries. Examples of decisions aided by simulation output ranges from tactical crop management options, commodity marketing to policy decisions about future land use. We also highlight the need to better understand temporal- and spatial-scale variability and argue that only a probabilistic approach to outcome dissemination should be considered. We demonstrated how knowledge of climatic variability (CV), can lead to better decisions in agriculture, regardless of geographical location and socio-economic conditions.

Near‐global impact of the Madden‐Julian Oscillation on rainfall

The accuracy of synoptic-based weather forecasting deteriorates rapidly after five days and is not routinely available beyond 10 days. Conversely, climate forecasts are generally not feasible for periods of less than 3 months, resulting in a weather-climate gap. The tropical atmospheric phenomenon known as the Madden-Julian Oscillation (MJO) has a return interval of 30 to 80 days that might partly fill this gap. Our near-global analysis demonstrates that the MJO is a significant phenomenon that can influence daily rainfall patterns, even at higher latitudes, via teleconnections with broadscale mean sea level pressure (MSLP) patterns. These weather states provide a mechanistic basis for an MJO-based forecasting capacity that bridges the weather-climate divide. Knowledge of these tropical and extra-tropical MJO-associated weather states can significantly improve the tactical management of climate-sensitive systems such as agriculture.

Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts

Sugarcane industries worldwide are exposed to uncertainty associated with variable climate. This variability produces impacts across an integrated value chain comprising of the following industry sectors: cane growing, harvesting and transport, milling, and marketing. The purpose of this paper is to advocate a comprehensive systems approach for using seasonal climate forecast systems to improve risk management and decision-making capability across all sugarcane industry sectors. The application of this approach is outlined for decisions relating to yield forecasting, harvest management, and the use of irrigation. Key lessons learnt from this approach include the need for a participative R&D approach with stakeholders and the need to consider the whole industry value chain. Additionally, there is the need for climate forecast systems to target the varying needs of sugarcane industries.

The International Atmospheric Circulation Reconstructions over the Earth (ACRE) Initiative

In 2006, climate applications scientists in Queensland, Australia, asked the lead author if a longer and more complete historical weather record could be created and fed directly into various crop, pasture, and production models. Existing dynamical reanalyses were steps toward such a product, but they spanned only the last six decades and had well-known shortcomings. To meet the needs of application scientists, new reanalyses would have to extend much further back in time while maintaining accuracy with limited observations. They would also need to be disseminated in a way that is easy to use directly and to downscale to small regions.

Rainfall Variability at Decadal and Longer Time Scales: Signal or Noise?

Rainfall variability occurs over a wide range of temporal scales. Knowledge and understanding of such variability can lead to improved risk management practices in agricultural and other industries. Analyses of temporal patterns in 100 yr of observed monthly global sea surface temperature and sea level pressure data show that the single most important cause of explainable, terrestrial rainfall variability resides within the El Nino-Southern Oscillation (ENSO) frequency domain (2.5-8.0 yr), followed by a slightly weaker but highly significant decadal signal (9-13 yr), with some evidence of lesser but significant rainfall variability at interclecadal time scales (15-18 yr). Most of the rainfall variability significantly linked to frequencies tower than ENSO occurs in the Australasian region, with smaller effects in North and South America, central and southern Africa, and western Europe. While low-frequency (LF) signals at a decadal frequency are dominant, the variability evident was ENSO-like in all the frequency domains considered. The extent to which such LF variability is (i) predictable and (ii) either part of the overall ENSO variability or caused by independent processes remains an as yet unanswered question. Further progress can only be made through mechanistic studies using a variety of models.

Operational seasonal forecasting of crop performance

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.

Positive feedbacks between the Antarctic Circumpolar Wave and the global El Niño‐Southern Oscillation Wave

Atmospheric and oceanic teleconnections link the Antarctic Circumpolar Wave (ACW) in the Southern Ocean [White and Peterson, 1996] and the global El Nino Southern Oscillation (ENSO) wave (GEW) in the tropical Indo-Pacific Ocean [White and Cayan, 2000], both signals characterized by eastward phase propagation and 3- to 5-year-period variability. We extend the tropical standing mode of ENSO into the extratropics by regressing the Nino-3 sea surface temperature (SST) index against sea level pressure (SLP) anomalies over the globe, finding the Pacific-South America (PSA) pattern in SLP anomaly [Cai and Baines, 2001] straddling Drake Passage in the Southern Ocean. The amplitude of this PSA pattern is similar to1/3 that of the ACW in this domain and thus cannot be considered its principal driver. On the other hand, suppressing the tropical standing mode of ENSO in interannual ST (surface temperature) and SLP anomalies over the globe allows the GEW to be observed much more readily, whereupon its eastward phase propagation across the Warm Pool is found to remotely force the ACW in the eastern Pacific and western Atlantic sectors of the Southern Ocean through atmospheric teleconnections [Sardeshmukh and Hoskins, 1988] which propagate along with it. Subsequently, the ACW propagates this imposed GEW signal throughout the remainder of the Southern Ocean as a coupled wave in covarying ST and SLP anomalies, whereupon entering the Indian sector 1.5 to 2.5 years later it spawns a northern branch which takes another 1.5 to 2.5 years to propagate the ACW signal equatorward into the Warm Pool south of Indonesia. There it interferes constructively with the GEW. Thus the two forms of teleconnection, one fast and directed from the tropics to the high southern latitudes via the atmosphere and the other slow and directed from the high southern latitudes to the tropics via the ocean, complete a global circuit of 3- to 5-year duration that reinforces both the ACW and GEW and influences the tropical standing mode of ENSO.

Weather, climate, and farmers: an overview

Challenges in linking meteorological and climatological information with a wide range of farming decisions are addressed in this paper. In particular, while a considerable amount of weather and climate information is now available for farmers, some types of information under development or already operational, particularly climate forecasting, formation, may be ill-suited for use by farmers for their decision-making. Case studies show it is particularly important for those key farm decisions that are amenable to weather and climate information to be identified clearly so that weather and climate information can be better tailored to suit farming decisions. A participatory approach provides farmers with ownership of the processes associated with development of weather and climate information and facilitates advances in linking climate and weather information and forecasts to farm decisions. Decision-support systems provide useful output when used with farmer discussion groups. Developing appropriate interdisciplinary systems to connect climate, weather, and agronomic information, especially including forecasting systems, with farm management is needed if uptake of weather and climate information by farmers is to be successful. Provision of output of climate change scenario and trend information to aid long-term strategic farm management decisions needs to be considered, especially in regions where more vulnerable farming zones exist.

Three Putative Types of El Niño Revealed by Spatial Variability in Impact on Australian Wheat Yield

The El Nino-Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Nino events are often associated with severe drought conditions. However, El Nino events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Nino are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (footprints) found in the El Nino impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean-atmosphere dynamics identifies three types of El Nino differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Nino events on agricultural systems.

Inferential, Nonparametric Statistics to Assess the Quality of Probabilistic Forecast Systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.