Providing Seasonal-to-Interannual Climate Information for Risk Management and Decision Making




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Providing Seasonal-to-Interannual Climate Information
for Risk Management and Decision Making



White Paper for WCC3

Draft: June 8, 2009


Lisa Goddard1, Youcef Aitchellouche2, Walter Baethgen1,
Michael Dettinger3, Richard Graham4, Peter Hayman5,
Mohammed Kadi6, Rodney Martínez7, Holger Meinke8

Additional Contributions: Esther Conrad1


1International Research Institute for Climate and Society, USA

2International Federation of Red Cross and Red Crescent, Senegal

3United States Geological Survey, USA

4Met Office, UK

5Primary Industries & Resources of S. Aus/S. Aus. Res. & Dev. Inst., Australia

6ACMAD, Niger

7 International Research Center on El Niño (CIIFEN), Ecuador

8Wageningen University and Research Centre, The Netherlands


Table of Contents
0. Summary Conclusions and Recommendations…………………………………........... 3

1. Introduction……………………………………………………………………………. 5

2. The Historical Situation……………………………………………………………….. 6

3. The Current Situation……………………………………………………………..8
3.1 Forecast Producers and Their Products………………………………….8
a) Forecasts with global coverage……………………………………8
b) Forecasts with regional coverage………………………………… 10
3.2 Forecast Applications…………………………………………………...... 11
a) General applications: Advisory statements ……………………........... 12
b) Applications tailored to specific sectors……………………….... 13
i. Water resource management examples……………….….. 13
ii. Agricultural decision making example……………….….. 14
iii. Health – malaria example…………………………….…. 15
iv. Other sectors………………………………………….……... 16

4. Gaps Between the Provision of Climate Information and Its Use…………….…. 16
4.1 Spatial and Temporal Scale…………………………………………..… 17
4.2 Forecast Variables and Their Specificity……………………………….. 18
4.3 Communication………….…………………………………………………. 20
4.4 ENSO as a Case Study …………………….…………………………..…… 21

5. Techniques for Increasing the Value of Climate Forecast Information………...… 22
5.1 Technical Efforts………………………………………………………… 22
a) “Translating” climate forecasts into more relevant variables…….. 22
b) Toolboxes…………………………………………………………. 23
c) Historical climate records as a tool for decision making………….. 24
5.2 Chain(s) of Communication Enabling the User of Climate Information… 24
5.3 A Human-Centric Approach to the Definition of ‘Forecast Value’……… 25
5.4 The need for on-going climate science and prediction research for improving
the quality and value of climate information………………………………….... 27

6. Lessons Learned…………………………………………………………………… 28
6.1 Learning about the Users………………………………………………….. 28
6.2 Involving the Media………………………………………………………. 29
6.3 Changing the Language……………………………………………………. 29
6.4 Receiving and Assimilating User Feedback…………………………….… 30
6.5 Involving the Private Sector………………………………………………. 30
6.6 Getting Positive Responses from Governments………………………..…. 30
6.7 Customizing Climate Products……………………………………………. 30
6.8 Getting Involved in User Activities……………………………………….. 31
6.9 Generating Trust from Users…………………………………………..... 31
6.10 Demonstrating the Effectiveness of Climate Applications………………... 31

7. Summary and The Way Forward…………………………………………….…….… 31

8. References……………………………………………………………………………. 35

9. Further Reading……………………………………………………………………… 44

Summary Conclusions and Recommendations

Conclusions:

  • The importance and visibility of climate information systems (CIS) has risen dramatically in the last few years, a trend that is likely to continue. CIS provide information that is relevant for climate-related risk management and decision-making. Climate forecasts are important components of CIS, but the utility of CIS goes much further than just forecasting.

  • The awareness of seasonal-to-interannual (SI) climate forecasts has also increased considerably since the late 1990s, due in large part to Regional Climate Outlook Forums (RCOFs) and intense media coverage of the 1997/98 El Niño event. However, much more effort must be invested in demonstrating use and increasing utility of these forecasts.

  • Increased use and benefit of SI forecasts will occur only with appropriate interpretation or tailoring of climate predictions, particularly in the case of dynamical model predictions. Much work is required to further develop and link SI prediction models with application models (e.g. crop yield prediction)

  • Effective CIS must involve all actors and not just the national Meteorological and Hydrological Services (NMHSs).

  • A better climate service for decision making must ensure that NMHSs and local climate services be able to respond to local users, often by providing locally relevant information, and those services must be supported even as local need may vary from year to year.

  • A culture change is required to build a “chain of communication” that realises the benefits of advances in SI climate predictions to society. The chain must target decision makers responsible for national infrastructures and welfare, and should include also climate intermediaries and NMHSs, sectoral scientists, government, business sectors, media, and others.

  • Involving the relevant sectoral scientists and decision makers as collaborating partners very early in the process is critical to ensure relevance, trust, and ownership of climate-related decision systems.

  • Using CIS that focus on managing climate variability is an important means of preparing for the near future of climate change.



Recommendations

  • Advocate for wider consideration of climate information in climate-impacted decision making and risk management.

  • Promote training on communication of climate information for NMHSs and stakeholders; encourage governments to invest in diverse dissemination structures.

  • Encourage the implementation of more demonstrations through pilot projects.

  • Request more government support for the participation of NMHSs and stakeholders in RCOFs.

  • Promote mechanisms for advances in climate science to support existing networks/responsibilities by enhancing understanding and technical capacity for better decision systems and climate risk management

  • GPCs, RCCs and NMHSs should maintain and manage a network of key contacts through science collaboration, staff exchanges, regular visits/emails/phones/local workshops/videoconferences.

  • Establish and maintain regular tailored climate bulletins to meet specific user requirements

  • Document and disseminate the pitfalls, benefits and success stories of climate products at national, regional and global levels.

  • Encourage research and technology transfer of methods to tailor climate predictions/projections

  • Encourage open access to data from both observations and dynamical models, for present and past conditions.

  • Promote  investments at international and national levels  to improve intermediary structures for climate information

  • On-going investments must be placed in continuing to improve dynamical climate systems, including models, data assimilation systems, and ensemble tehniques.

1. Introduction

The history of physically-based seasonal climate forecasts is relatively short and strongly linked to the ability to predict sea surface temperatures in the El Niño region. The first physically-based model forecast of equatorial Pacific Ocean temperatures was produced only in the mid-1980s (Cane et al. 1986). The growing ability to predict El Niño led to a cascade of efforts for developing and improving the seasonal climate forecasts and attempting to make those useful to society. El Niño is the overall dominant influence in regional climate variability worldwide, though other modes of sea-surface temperature variability can be more important in some regions (see e.g. Folland et al., 1991).


In developing a white paper for Understanding and Predicting Seasonal to Interannual Climate Variability from a “user” perspective or from a “producer” perspective, what is immediately clear is how blurred the boundary between these communities has become over the past decade. A “user” may be a decision maker acting individually or as part of a collective. A “user” may also be a translator of information regarding climate variability or its associated impacts such that the information can be used by decision makers. Similarly, a “producer” may be the climate scientist running a dynamical model of the climate on a large computer. A “producer” may be the sectoral scientist that takes the information from the climate model and feeds it through a hydrology model or crop model. Or, the “producer” may be that same translator referred to above that modifies the initial forecast information into a more usable format for the policy or decision maker. In some places the term “producer” even mean “producer of agricultural products” (i.e. a farmer), highlighting the confusion this terminology can cause.


The blurring of boundaries between these communities began when it became clear that effective climate risk management could not be accomplished in isolated communities. It has been an important realization, but much work remains. This white paper is written from the perspective of “users”, but not those that have been traditionally targeted as “end users”. The “users” voice here is from the translators: those that build on the scientific advances in climate modeling and diagnostics, those that design decision systems for resource management, and those that are participating in the conversation between climate risk management and the prospects of seasonal prediction. The following discussion concerns the advances in providing climate information that are necessary for effective climate risk management at seasonal-to-interannual timescales. These efforts constitute the wealth of research, communication and application that has been attempting to 'bridge the gap' between the traditional 'provider' and 'user' communities.


The paper begins with the historical evolution of developing and using climate information for management and decisions. The discussion then progresses through the current infrastructure of accessing climate forecast information, the gaps between the operational provision of forecasts and their use, considerations for increasing the value of forecast information, some of the lessons learned, and finally we conclude with our view of the way forward. Climate information refers in this paper to both current and historical observations-based data and predictions of future climate conditions, with particular focus on seasonal-to-interannual variations. Most of the attention here will be upon the users' perspective on forecasts, but those forecasts become valuable primarily in context of past climate variations and, where known, the past performances of the forecasts. Also, although this paper focuses on seasonal-to-interannual (SI) climate variability, the context set by the background climate, which may be slowly varying, is well recognized. The successful future of climate risk and resource management depends critically on health of the entire chain of information that the international community is working so hard to forge.


2. The Historical Situation


Until the last decade, plans and decisions that needed climate information often followed an approach based on the long term means of relevant climate variables. For example, maize in a given region was sown in a certain date because the combination of rainfall and temperatures in the following 4-6 months for that region was, on average, the most favorable for the crop growth and development. Plans for distributing water in multipurpose reservoirs (hydroelectricity, irrigation, human consumption) were established with lead times of several months, based on the mean values of the precipitation for the entire year (and in some cases based also on the current situation of for example, snow pack). Health institutions based their action plans for infectious disease outbreaks in a given area considering the long term average temperatures and rainfall of that area. This approach to management is not a general truth, however. It is not uncommon for farmers to use environmental observables to guide their actions. Soil moisture availability, for example, might suggest when to plant what. In particular, resource poor farmers in semi-arid regions are excellent, intuitive risk managers. Of course they are also conservative, they don’t plan for the average season, the plan for the poor season (low plant densities, no or low inputs etc), so they ensure their survival, but never manage to make a real profit, because the miss out in the good years.


Interestingly, the probability that an entire year behaves as an “average” year (e.g., 12 months of “average” rainfall) is virtually zero. Moreover, by definition, the probability that the rainfall of two subsequent trimesters falls in the central (“normal”) tercile, is less than 10%. Still, and up to the 1990’s, planning and decisions in many climate-dependent activities could only be based on these very unlikely “average” or “normal” years.


Where mean conditions have not been used as the default climate “forecast”, resources and risk managers have relied, and often continue to rely, heavily on observed conditions at the time that decisions need to be made for the basis of their forecasts of future eventualities. For example, it has been (and remains) common for managers to base decisions and forecasts of future water supplies solely on observed snowpack and soil moisture for all but the shortest term, multiday problems (Beller-Simms et al., 2008). Observed conditions on the ground have been more reliable and more immediately relevant to the decisions to be made than other climate-science resources for many applications.


Efforts to make these improvements have been prompted, in part, by a growing understanding of the effects of global-scale climate phenomena like El Nino-Southern Oscillation (ENSO) on the climates, resources, and hazards of many regions and a growing expectation that improvements in climate forecasts may provide a basis for decision making that is not currently being exploited. Generally speaking, climate forecasts are forecasts of those variations of the climate system that reflect predictable responses to predictable changes in slowly varying boundary conditions like sea-surface temperatures and radiative imbalances in the Earth’s energy budget. Not all possible sources of climate-forecast skill have been identified or exploited, but boundary-condition contributors may include a variety of large-scale air-sea connections (e.g., Redmond and Koch 1991; Mantua et al., 1997; Enfield and Cid-Serrano, 2006; Hoerling and Kumar, 2003), snow and sea-ice patterns (e.g., Cohen and Entekhabi, 1999; Clark and Serreze, 2000), and soil moisture and vegetation (e.g., Koster and Suarez, 2000). Long-term radiative imbalances associated with human-caused emissions of greenhouse gases into the atmosphere have been a focus of much attention on even longer time scales (IPCC, 2007).


Within the past decade, however, climate scientists have begun to identify potential improvements in long-lead, seasons to years ahead climate forecasts (e.g., Krishnamurti et al. 2000; Goddard and Dilley, 2005; Zheng et al. 2006) and to link them with resource models (e.g., Kim et al., 2000; Kyriakidis et al. 2001) or statistical distributions of management-relevant parameters (e.g., Dettinger et al., 1999; Sankarasubramanian and Lall, 2003) to improve the immediacy and, in some cases, the reliability of the climate forecasts for use in management decisions. Consequently, research institutes such as the International Research Institute for Climate and Society (IRI), invested huge efforts to provide synthesized climate forecasts based on the inputs from the international modeling community and started supplying them to climate information providers (National Weather Services, Regional Climate Centers, Specialized Meteorological Centers) and to key socioeconomic sectors (i.e., agriculture, health, water resources, disaster prevention/reduction, etc.). The premise of these efforts was that supplying the best possible seasonal forecasts would immediately result in better decisions and more effective planning activities in those sectors. Efforts were thus concentrated in investing increased efforts in the dynamical models and statistical methods that resulted in forecasts with better skill.


The initial reaction in the different sectoral communities was extremely optimistic: the new seasonal climate forecasts were viewed as tools that would assist these communities to cope better with the immense challenges posed by climate variability on their activities. Planning and decisions in activities that depend on, or are affected by climate would now be better informed.


However, this initial optimistic environment was shortly followed by frustration in both, the climate science community and the socioeconomic sectors, since expectations from both groups were not fulfilled. Excellent achievements were obtained in the climate science community for supplying seasonal forecasts that were continuously improving. Many studies demonstrated the “potential value” of incorporating this information into the decision-making of different sectors (e.g., Hansen, 2002; Cabrera et al., 2007, McIntosh et al., 2007; Hansen et al, 2009; Hammer et al., 2001, Thomson et al, 2006). However, there was little or no evidence that the generated information was effectively being embedded in the policies, planning or decision-making within the sectors. On the other hand, the socioeconomic sectors started receiving vast amounts of information resulting from the seasonal forecasts but in most cases could not find ways to incorporate it in a useful manner for their routine activities.

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