User Interface Platform and its linkages with CSIS Dr Roger Stone - - - PowerPoint PPT Presentation

User Interface Platform and its linkages with CSIS – Dr Roger Stone - 14:30 “Discussion on how to serve efficiently the users; likely different suggestions with respect of different users’ categories”

Aim of TTUI The Task Team on User Interface will:

• Collect and assess existing case studies relevant to quantifying the social and

economic benefits of using climate information, products and services,

• Develop a guideline for users on integrating climate predictions and information into

climate risk management, and adaptation strategies and planning, to include user- friendly terminology,

• Collect and develop information on the susceptibility of various sectors to climate

variations and change and on the use of climate information in climate risk management and adaptation by specific sectors, and publish these in the form of an

• nline catalogue,
• In collaboration with CBS CCl CHy ET on Meteorological, Hydrological and Climate

Services for Improved Humanitarian Planning and Response, develop an implementation plan to facilitate the provision of meteorological, Hydrological and Climate Services to the international humanitarian agencies from National Meteorological and Hydrological Services (NMHSs), RSMCs, Global and Regional Climate Centres (RCCs),

• Inform the CCl Management Group on completion of the tasks (within a period of

three years from the date of formation of the Task Team) and that the team can be dissolved.

Members

Roger C. STONE (Lead) - AUSTRALIA (RAV) Abdullah CEYLAN – TURKEY (RAVI) Valentina KHAN – RUSSIAN FEDERATION (RAVI) Shuhei MAEDA - JAPAN (RAII) Samwel Omwoyo MARIGI – KENYA (RAI)

Deliverables: A guideline for users on integrating climate predictions and information into climate risk management, and adaptation strategies and planning, to include a user-friendly terminology; · A collection of existing case studies relevant to quantifying the social and economic benefits of using climate information, products and services; · Information on the susceptibility of various sectors to climate variations and change and the use of climate information in climate risk management and adaptation by specific sectors, in the form of an online catalogue.

WMO Task Team on User Interface (TTUI)

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

The WMO TT-UI will likely recommended the following guidelines to assist WMO and National Meteorological and Hydrological Services (NMHS) to succeed in the overall aim of improving users’ benefits from the advances being made in climate science and research and in use of integrating systems of value to users core needs. 1.Apply the overall recommended systematic approach agreed to by TTUI as a guideline for users in integrating climate predictions into climate risk management. 2.There is a core need by developers of seasonal climate and climate change forecast models and systems to work much more closely with the likely developers of tactical and strategic management systems in selected industries (eg agriculture, hydrology, energy, insurance, etc) . 3.There is an urgent need to provide more modelled or actual case studies of detailed quantified economic benefits of the value of seasonal climate and climate change forecasts for specific industries in order to demonstrate to industry/users, scientists, and policy makers where critical opportunities and value would or does exist in the application of climate science outputs. 4.There is a need to establish more systems modelling research and

• perational centres where interdisciplinary systems models and

approaches can be developed that will effectively integrate climate forecast modelling systems with comprehensive industry decision systems. 5.There is a need to increase interaction with user groups in addition to agriculture where it appears the greatest amount of user interaction in regards to climate forecasts has so far taken place.

Members

Roger C. STONE (Lead) - AUSTRALIA (RAV) Abdullah CEYLAN – TURKEY (RAVI) Valentina KHAN – RUSSIAN FEDERATION (RAVI) Shuhei MAEDA - JAPAN (RAII) Samwel Omwoyo MARIGI – KENYA (RAI)

Overall, TTUI recommended a systematic approach that was agreed to at the Meeting of the Commission for Climatology (CCl) Task Team

• n User Interface (TT-UI) Geneva, Switzerland (2011) as a guideline

for users in integrating climate predictions into climate risk management:

• Understand the ‘target system’ (eg agriculture, water resources, etc) and

its management: it is essential to understand the system dynamics and

• pportunities for management intervention i.e. identify those decisions that

could influence desired systems behaviour or performance – what are those decisions that are important?;

• Understand the impact of seasonal climate variability: it is important to

understand where in the target system climate risk is an issue;

• Determine the opportunities for tactical/strategic management in response

to the forecasts. If forecasts are now available, what possible options are there at relevant decision-points? How might decisions (eg in agriculture, water resources, health) be changed in response to forecasts? What nature of forecast would be most useful? and - what lead-time is required for management responses?

Meeting of the Commission for Climatology (CCl) Task Team on User Interface (TT-UI) Geneva, Switzerland, 29-31 March, 2011

WMO Task Team on User Interface (TTUI)

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

The WMO TT-UI will likely recommended the following guidelines to assist WMO and National Meteorological and Hydrological Services (NMHS) to succeed in the overall aim of improving users’ benefits from the advances being made in climate science and research and in use of integrating systems of value to users core needs. 1.Apply the overall recommended systematic approach agreed to by TTUI as a guideline for users in integrating climate predictions into climate risk management. 2.There is a core need by developers of seasonal climate and climate change forecast models and systems to work much more closely with the likely developers of tactical and strategic management systems in selected industries (eg agriculture, hydrology, energy, insurance, etc) . 3.There is an urgent need to provide more modelled or actual case studies of detailed quantified economic benefits of the value of seasonal climate and climate change forecasts for specific industries in order to demonstrate to industry/users, scientists, and policy makers where critical opportunities and value would or does exist in the application of climate science outputs. 4.There is a need to establish more systems modelling research and

• perational centres where interdisciplinary systems models and

approaches can be developed that will effectively integrate climate forecast modelling systems with comprehensive industry decision systems. 5.There is a need to increase interaction with user groups in addition to agriculture where it appears the greatest amount of user interaction in regards to climate forecasts has so far taken place.

Members

Roger C. STONE (Lead) - AUSTRALIA (RAV) Abdullah CEYLAN – TURKEY (RAVI) Valentina KHAN – RUSSIAN FEDERATION (RAVI) Shuhei MAEDA - JAPAN (RAII) Samwel Omwoyo MARIGI – KENYA (RAI)

• Evaluate the worth of tactical or strategic decision options: the quantification

and clear communication of the likely outcomes (e.g. economic or environmental), and associated risks of changing a management practice (eg in agriculture, water resources, etc) are key to achieving adoption of the technology.

• Implement participative implementation and evaluation: working with

industry managers/decision-makers generates valuable insights and learning throughout the entire process: i.e. identifying relevant questions/problems and devising suitable technologies and tools.

• Provide feedback to seasonal climate forecasting research in the

NMHS/university/etc: rather than just accepting a given climate forecast, consider what specific improvements would be of greatest value in the target

• system. This can provide some direction for the style of delivery of forecasts

and for climate research of value for particular sectors. “Climate information doesn’t have to be perfect to be useful; it just needs to support a decision”. (Approach concepts, especially after Hammer, 2000; also refer to Hammer et al., 2001; Stone and Meinke, 2005, 2007; Rodriguez, 2010; Stone, 2012).

Meeting of the Commission for Climatology (CCl) Task Team on User Interface (TT-UI) Geneva, Switzerland, 29-31 March ,2011

Decision type (eg. only)

Climate period

Logistics (eg. scheduling of planting / harvest

• perations; short-term buying decisions (stock)

Intraseasonal (> 0.2) MJO Tactical crop management (fertiliser/pesticide use) Intraseasonal (0.2-0.5) Crop type/area/fertiliser app (wheat/chickpeas); stocking rates; agistment planning; grain supply. Seasonal (~ 1.0) ENSO Crop sequence (eg. long or short fallows); agistment Interannual (1-2.0) SAM Crop rotation (eg. winter or summer crop); selling due to likely drought in QBO West Phase + STR Annual/biennial (2) QBO) Industry issues(eg. grain/cotton); land purchase Decadal (~ 10) + STR Agricultural industry (eg. crops or pasture) Interdecadal (10-20) IPO Landuse (eg. Agriculture or natural system) Multidecadal (20+ ) Landuse and adaptation of current systems Climate change

Useful to map Agricultural Management Decisions and Climate Systems that

• perate at various time scales (Meinke and Stone, 2005; Stone and Meinke, 2005)

(Stone and Plant 2014).

What are the decisions? Linking climate information to stakeholder decisions – complex issues of scale – Targeting seasonal forecasts (example from sugar industry) Farm Harvest, Transport, Mill Catchment Marketing Policy

Industry Scale Axis Seasonal c l i m a t e forecast information

• Irrigation
• Fertilisation
• fallow practice
• land prep
• planting
• weed manag.
• pest manag.
• Improved Planning

for wet weather disruption – season start and finish

• Crop size forecast
• CCS, fibre levels
• Civil works

schedule Land & Water Resource Management Environmental Management

• Water

allocation

• Planning

and policy associated with exceptional Events

I ndustry Business and Resource Managers Government

Crop size Forecast Early Season Supply Supply Patterns

• Shipping
• Global Supply

The sugar industry: understand decisions across the value chain: “seasonal climate forecasting has no value unless it changes a management decision”

The Cane Plant Sugarcane Production Harvest & Transport Raw Sugar Milling Marketing & Shipping

• Best use of scarce/costly

water resources

• Better decisions on

farm operations

• Improved planning

for wet weather disruption

• Best cane supply

arrangements

• crush start and

finish times

• Better scheduling
• f mill operations
• crop estimates
• early season

cane supply

• Better marketing decisions based
• n likely sugar quality
• More effective forward selling

based on likely crop size

• Improved efficiency of sugar

shipments based on supply pattern during harvest season

Y .L. Everingham, R.C. Muchow, R.C. Stone, N.G. Inman-Bamber, A. Singels, C.N. Bezuidenhout (2002)

Seasonal climate forecasting needs to connect to management decisions…

The grazing industry - climate issues across the supply chain

Producer Production Transport Processing Retail, Marketing & Shipping

• Stocking assessments/

stocking rates

• Drought preparation
• Looking for exit strategies.
• Selling
• Agisting
• Buying fodder.
• booking transport
• managing transport

availability in peak demand periods

• Booking processing space
• Decisions regarding

supply: daily, monthly, six monthly planning.

• Winter maintenance

issues.

• Shift Worker rosters.
• (avoid

lambs getting sold small/lightweight) “Going into dry is fantastic – heaps of stock, plant at capacity and decreasing prices

• Coming out of dry, often 3-5

months after season break, lack of supply: key issues regarding long term viability emerge”

• A

ssessing market signals, forward contracts

• Retaining price

consistency and consumer retention based on nutrition, integrity, value, enjoyment.

(Lamb industry: Stone, Hancock, Davison, 2014)

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management Crop production decisions. For many regions of the United States of America:

• Operational seasonal climate forecasts offer quantified potential to guide production decisions, such as crop

species or cultivar selection, fertility management, area to be planted, pest management, intensity and timing of grazing and purchase, sale, or movement of animals (Steiner et al., 2004).

• Additionally, management decisions related to marketing, labour, and diversification, and regional decisions relating to

input supply, markets, transportation, storage, or community health services can also be guided by climate forecasts.

• They demonstrated that seasonal forecasts have sufficient utility to guide decision-making in some specific regions and for

some specific seasons. However, to move forward, continued improvement and evaluation of forecasts skill are

• needed. Improvements in forecasting tools for regions that gain little from current forecasts and forecasts of

extreme events should also be a focus for further work.

• Uncertainty analysis for scenario simulation, tools to assess trade-offs within a whole farm context, and better

methods to communicate probabilistic outcomes are needed.

• They show that engaging farmers (users) as partners is critical in the development of new tools to support

decision-making on-farm and using seasonal climate forecasts within the context of overall risk analysis and management of an agricultural system.

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

• Yet, a key issue 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.

• A further perspective relates to improving the current design and variety of mechanisms (e.g.

climate outlook forums) for the dissemination and uptake of climate information. Climate information, used in isolation, (e.g. in ‘stand-alone’ climate outlook forums) and undertaken in a traditional, linear fashion, where information is moved from the forecast 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 variability and food security. Alternative modes of interaction (e.g. using existing platforms to ‘piggyback’ information or seeking appropriate ‘boundary organisations’) should be found to sustainably manage climate risks (adapted from Vogel and O’Brien, 2006).

• Efforts to mitigate climate-related risk should be shifted away from the improvement of forecast skill or

dissemination: instead, greater attention needs to be given to the infrastructural and institutional advances necessary to facilitate the use of forecast information within a range of contexts, e.g. rural livelihood strategies if better examples of successful case studies are to be developed and discovered (Vogel and O’Brien, 2006). .

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

Vogel and O’Brien, 2006.

An example of the current institutional and information delivery process in (southern) Africa (part A) but with a suggested improved overall dissemination and institutional process and systems also provided (part B).

A dedicated centre? (Meinke and Stone, 2005). The need for an interdisciplinary, integrated, systems science approach? the need to really understand the specific needs and decision-points

• f each sector and sub-sector; aim for targeted outputs rather than only general forecast
• utputs? co-development with users of outputs/ products? participative implementation of new

systems; assess value of climate forecasts; look at user value chains?…

THANK YOU

• There are a number of studies in Australia and elsewhere (Stone, Hammer, Meinke) that demonstrate that the leap

directly from a generally publicly issued seasonal forecast to a real decision is too great to be done (well) intuitively.

• The use of modelling and scenario simulation and associated analysis adds substantial value by enabling

information to be much more relevant to the decision in question than the general piece of information contained in the forecast. There are numerous possibilities and interactions and the insights gained from analysis

• f expected outcomes and risks provide a much more relevant and rich information source for the decision-maker.
• Simulate trading systems: World wheat production is particularly vulnerable to seasonal climate variability,

because it is typically grown in relatively arid regions of the world, and like sugar, some of the largest wheat producing regions in the world are in areas in which consistent weather patterns associated with the El Niño- Southern Oscillation (ENSO) have been identified. A model has simulated 25 years of world wheat trade with results placed in present value terms. The results (Hill et al) show wheat producers in Australia, Canada and the USA can expect to gain 15.7%, 5.3%, and 5.1%.in profit through use of ENSO-based forecast systems.

• Studies by Chen, Mjelde and Hill ‘affirm once more the findings of others that ENSO forecasts have considerable

value for agriculture’ but they ‘go further to show that the value of these types of forecasts can roughly be doubled by releasing more refined forecasts and educating agricultural producers to use them’.

• Generated estimates of welfare gains from a number of detailed scenarios show that simple three phase ENSO

forecast information increases total value to certain US agricultural enterprises for 10 US regions by $399 million dollars per annum. However use of more refined forecasts (eg the ‘five phase SOI’ information) raises the total value

• f information to $754 million dollars in these regions and also reduces income variability. They also show that

total value to the US economy from just this approach in use of seasonal climate forecasts to be worth, on average, approximately $2,095m pa….)

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management APSIM

“Collect and assess existing case studies relevant to quantifying the social and economic benefits of using climate information, products and services”.

• A study in the application of an Australian developed seasonal climate forecast system that is comparatively

simply utilised directly into management applications, especially in agriculture and water resources and application for Ghana is provided (Adiku and Stone).

• It was demonstrated that an ‘SOI-phase system’ - an intermediate step in statistically assessing relationships

between rainfall or temperature and key ENSO indicators - before a full forecasting system is eventually utilised (Stone et al, Nature, 1996) offers considerable scope for using a first step seasonal climate forecasting system

• perationally: eg in rainfall prediction and water management in Ghana.
• This approach links to decision making in water management in southern Ghana through the fact that it

is continuously measured and results are available even throughout the growing season. Hence, benefits to day-to-day decision making on water management is believed to be enhanced (authors).

• A study (by Anwar et al) has covered 117 years of spring rainfall and 104 years of grain yield simulated using

the Agricultural Production Systems Simulator (APSIM) model, from four locations in southeast Australia. Important variations in shift and dispersion in spring rainfall and simulated wheat yields were observed across the studied locations.

• It was concluded that adequate forecasts of spring rainfall and grain yield could be produced at the end of July in

each year, using both SOI and SST phase-based systems. These results were identified in relation to the potential benefit of making tactical top-dress management applications of nitrogen fertilisers during early August.

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

• A methodology for the development of a combined seasonal weather and crop

productivity forecasting system has been demonstrated by Challinor et al for groundnut production in India.

• A working spatial scale for application of dynamical forecasting models has been

identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modelling work for an example of groundnut production in India. Output from such a system will have value for use in trading and export/import decision-making.

• A study for the Australian sugar industry (Clarke et al) demonstrated that one

‘average La Niña event’ can easily cost this industry in excess of $175 million AUD for an industry that generates about $2 billion, annually. (2010 La Niña cost this industry ~$500m). Ultimately most blame for the loss can be placed on a ‘wet end’ to the sugar harvest season and the inability to forecast and plan for it with a sufficient lead time.

• Previous studies have already demonstrated the value of SOI-based (statistical)

‘forecasting systems’ to be worth approximately $10m per annum, on average, just for

• ne small mill production area, through use of seasonal forecasting to adjust mill
• perations, transport logistics, marketing. .
• In major and noteworthy studies by Hammer et al in a number of countries globally

(Australia, Argentina, Canada) they clearly demonstrate that while shifts in forecast probability distributions from the ‘all years’ long-term climatology may not always be great, modest shifts in forecast probability distributions can provide sufficient shift to modify critical decisions, especially in agricultural industries.

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management.

• Studies by Hammer and others, show that feedback evaluations from workshops with groups of growers is

always important, especially to provide indication of the range of management decisions that could or already have been changed as a result of application of seasonal climate forecasting to management

• decisions. They show that one of the key issues in discussing use of seasonal forecasts in management decision

making concerns reliability of outcomes. It is known seasonal (or weather) forecasts are not necessarily precise and the decision-maker faces a ‘sample of one’ from a possible distribution of outcomes.

• Hammer, Potgieter, Carberry, Meinke and Stone are just some of the authors that provide numerous examples of the

value of use of seasonal forecasts. In one example, they demonstrate that adjusting management in response to a forecast, by using simulation models to assess the value of changing planting row spacing, based on seasonal forecasts, will pay-off in the long run with an average ~$28/ha (AUD) average improvement in profit per annum and with profit increasing by about 11% overall – (many cotton farms in Australia are over 10,000ha).

• Hammer et al also show that management decisions do not pay-off on every occasion in response to a seasonal

forecast and suggest that in discussing management responses to forecasts with users that it is imperative to highlight the point that there are no ‘rights’ and ‘wrongs’ with this type of technology — just shifts in probability distributions. There are studies that note the credibility crises that have arisen in some countries when this approach has not been taken!

• Australian authors further consider that advances of the future will be made by better connecting

(agricultural) scientists and practitioners with the science of climate prediction. They note that professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential.

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

• Additional important examples: in Europe, the FP7 EUPORIAS Project (‘European Provision Of Regional Impact

Assessment on a Seasonal-to-decadal timescale’), recently funded by the European commission under the 7th framework program, intends to improve the ability to maximise the societal benefit of these new technologies of seasonal to decadal timescale forecasts. The project works in close relation with a number of European stakeholders with the aim of wishing to develop a few fully working prototypes of climate services addressing the need of specific users.

• The project points out that, given the current status of the use of seasonal climate predictions there are very

few examples of the actual use of this type of predictions to decision-making processes across Europe. The project points out that in other parts of the world the use of seasonal forecasts is more advanced, although its use has been questioned at times due to issues surrounding its credibility, saliency, and legitimacy (see work by Cash and Buizer). .A key comprehensive workshop from the EUPORIAS Project, involving user applications of seasonal forecasts, identifies that, in Europe, the users of seasonal to decadal climate forecasts and information are mainly related to the energy, insurance and transport sectors.

• Users who took part in this EUPORIAS study were “identified as using forecasts as additional information to

climatology to those using this information in operational/dynamical models to support decision-making”. This study points out that annual and decadal climate information is much less used across European sectors/countries.

• Barriers and limitations to the use of seasonal to decadal forecasts, identified by EUPORIAS workshop participants

“revolved around issues of skill and predictability; capacity, relevance, and usability; accessibility and communication; and changing existing practices” (EUPORIAS project milestone, 2013).

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

Health aspects – Malaria. Excellent examples of use of forecasting systems in improving management of health issues are available in Thomson et al (2006).

• The ability to forecast at seasonal timescales anomalously high and low malaria incidences using “dynamically

based, seasonal-timescale, multi-model ensemble predictions of climate”, and applying leading global coupled ocean– atmosphere climate models developed in Europe are demonstrated.

• Successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability

are well established, importantly adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill.

• In years in which the forecast probability distribution is different from that of climatology, malaria decision-

makers can now use this information for improved resource allocation - the ability of a malaria early-warning system to improve resource allocation and assist in the reduction of malaria morbidity and mortality depend on decision-makers’ capacity to make effective use of new information within their own control paradigm.

• The authors focused their efforts on the integration of the multimodel DEMETER forecast system into the routine

epidemic malaria control activities currently promoted by the WHO-AFRO Southern Africa Inter-Country Malaria Team (SAMC) in Zimbabwe and undertaken by the National Malaria Control Unit in Botswana.

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

Success of climate forums. In southern Africa, Vogel and O’Brien (2006) point to the 1997–1998 rainfall season in the region as a good case study that illustrates the success of climate forums and climate forecast information but which also highlights some of the missed opportunities

• f effective use of climate information.

They note that by using various GCMs and statistical model outputs, and by incorporating recent advances in seasonal-to-interannual climate variability research, it was possible to predict the 1997– 1998 El Niño event. This marked the first year that seasonal climate forecasts were widely disseminated in southern Africa. However, unlike the ENSO event of 1982–1983, which was not predicted and had large economic consequences, the onset and related potential impacts of the 1997–1998 event was anticipated by the forecasts. They note that the 1997–1998 El Niño event, coupled with growing concerns about potential climatic change associated with global warming, contributed to the heightened awareness and desire to better manage climate fluctuations. In southern Africa, SARCOF was established in an effort to promote the dissemination of consistent and clear consensus forecasts to the user community, and to minimize the confusion that arises when conflicting forecasts from various sources are heard. Seasonal climate forecasts used in the SARCOF forum provide probabilistic estimates of total rainfall relative to a 30 year period.

WMO Task Team on Climate Risk Management

Part of the Commission for Climatology’s Open Panel of CCI Experts OPACE IV – Climate Information for Adaptation and Risk Management

Additional noteworthy European examples:

• ‘The JPI Climate – research for climate services development’– a European initiative concerning the coordination of

climate research funding encompassing various modules research for climate services development. JPI Climate modules (From Bessembinder and Zölch, 2013). The EUPORIAS project points out that the “JPI Climate Module 2” focuses on research for climate services development in order to ensure: quality assurance of climate services provided, effectiveness of service provided, and standards. This module encompasses two ‘fast-track activities’ (FTA): “FTA 2.1. aims to improve the transfer of data, information and knowledge about climate and climate change to society within Europe”. “Better dissemination requires proper knowledge

• n users’ needs, which set the scope for the relevance of the data/information/knowledge”. “FTA 2.1 involves

understanding what is available; understanding users’ needs; and provision of an improved interface - to achieve this, a number of activities are being pursued including: preparation of an inventory of user requirements (what do users need to know; user perceptions of risk); collection of guidance documents; analysis of existing uses; and recommendations for future research. FTA 2.2 focuses on mapping climate services providers within Europe. A number of definitions can be used to identify specific roles within climate services including: “Climate service” - User driven development and provision of knowledge for understanding climate, climate change and its impacts, as well as guidance in its use to researchers and decision makers in policy and business (extracted from EUPORIAS Report D12.2 Courtesy Prof. Suraje Dessai.)

Current development status of CSIS (new tools, toolkits, etc.) related to GFCS Australia and RAV initiatives

Roger C Stone, University of Southern Queensland, Australia. World Meteorological Organisation, Commission for Agricultural Meteorology.

40 DSS available

Queensland state output – focussing on specific regions – and linking to targeted requirements – pasture growth forecasts include antecedent soil moisture conditions and pasture growth modelling.

Useful to apply seasonal climate forecasting systems that can also be integrated with crop, pasture, and hydrological models.

Commonwealth Bureau

• f Meteorology output

– good standard output

Operational climate services we utilise – examples (also provides examples of the value of varying spatial representation)

to become CLIMATE ARM

For agriculture, a useful aim is to do a complete research analysis utilising the linking role of crop simulation modelling in the application of climate forecasting

• yield of crops (potential yield is

the key output),

• key soil processes (water, N,

carbon)

• surface residue dynamics & erosion
• range of management options
• crop rotations + fallowing
• short or long term effects



Simulate management scenarios



Evaluate outcomes/ risks relevant to decisions



Agricultural Production Systems Simulator (APSIM) simulates

Sow date & SOI Phase

15-Sep Negative 15-Oct Negative 15-Nov Negative 15-Dec Negative 15-Jan Negative

Yield (kg/ha)

6000 5500 5000 4500 4000 3500 3000 2500 2000 1500

‘WhopperCropper’ (now CROPARM)- Farm-level decisions (‘when do I plant my crop’?)

• Utilising seasonal climate forecasts in management and adaptation – eg forecasts of potential

sorghum yields associated with varying climate regimes (example for a ‘consistently negative SOI phase’) – varying management decisions (sowing dates) : example for Miles, Australia. Effect of sowing date on sorghum yield at Miles South QLD with a ‘consistently negative’ SOI phase for September/October (Other parameters - 150mm PAWC, 2/3 full at sowing, 6pl/m2, medium maturity’)

Impact of ENSO

Courtesy D Cobon – 4 month lead time forecasts

Example of an integrated climate/ crop model forecast system – Australian wheat crop forecast for 2014 (OzW heat Model Potgieter, 2009; integrated with climate model of Stone et al, 1996) – issued September, 2014; first available May 2014

Value for commodity traders/ grain suppliers - Assessing grain supply - wheat yield forecast x shire for 2014

QAAFI

Are user decision-support systems always useful?…..

Assisting decision processes for stakeholders? – developing decision-support systems that link climate information, agricultural models and user decisions – make sure they actually add value …

• Decisions related to

estimation of future stocking rates.

• Decisions related to

pasture budgeting monitoring.

• Decisions related to

total grazing pressure.

• Decisions related to

drought preparation.

Discussion support systems

Malaysia –

Courtesy AZHAR ISHAK, Head, Agromet Section, Malaysian Meteorological Department, Ministry of Science, Technology and Innovation Rainfall anomalies - Rainfall Anomaly for August 2011 – given in percentage of normal Anomaly of total accumulated rainfall from Jan-Sept 2011

• 6. Farm Weather Forecast/Advisories
• 5. Agri Weather Services
• 4. ENSO Advisory
• 1. Seasonal Climate Outlook
• 2. Monthly weather review and outlook
• 3. Climate Impact Assessment for Agriculture
• 7. Angat Dam monthly/seasonal forecast inflow

Philippines - PAGASA Climate Services

• 8. Latest rainfall forecast

Courtesy: Ms. Edna L. Juanillo (and Mr Anthony Lucero) Assistant Weather Services Chief PAGASA (Weather Bureau) Philippines

Use of ‘rainfall relative to historical records’ in percentile ranks - red colour years are El Niño, blue are La Niña, and black are ‘neutral’ years.

Packaging information – example from the Philippines – Linking climate, rainfall deficit information and agronomy to provide an

• verall

assessment

Critical information and forecasts in regards to irrigation/water supply from Angat Multi-purpose Dam

Summary RAV, especially Australia:

• Good general seasonal climate product outputs from NMHS’s in the region

(except PNG?).

• Valuable input from collaborating linking agencies: especially State

Department’s of Primary Industries and some universities, CSIRO – provision of specialist extension systems and decision-support systems.

• Strong emphasis on systems science – crop and pasture simulation modelling

and economic modelling in order to produce more targeted, user orientated

• utputs and processes.
• Increasing activity in whole value chain approaches across industry systems

(farm-harvest-mill-trading-export).

• Farmer field schools, Managing for Climate Workshops, Climate Kelpie, high-

level industry conference participation.

• Participative user face-to-face engagement workshops critical – use of

discussion support systems.

• Surface Observatories – Class I and Class II - (559)
• Pilot Balloon- (71)
• Agrometeorological Observatories- (219)
• Hygrometeorological Observatories - (701)
• Non-Departmental Raingauge Stations

 Reporting- (3540)  Non-reporting- (5039)

• Extreme Weather reporting – Storm surge,
• Frost, Heat wave, Hail storm etc.

Conventional Observational Network

Tools: Observation, forecast, data & products.

675 Automatic Weather Stations

127 Agro- AWS 548 AWS Type of Observatory Installe d Propo sed AWS 675 400 ARG 1350 2000 DWR 16 42 Doppler Weather Radar 16 DWRs are installed Products are * Rain intensity * Cumulative rain * Cloud motion winds * Vertical profiles of Temperature, humidity etc. (Res: 0.5x0.5 km) Assimilation of DWR data with AWS

• bservations. (Res: 9x9km )

India's advanced weather satellite INSAT-3D launched in the early hours

• f July 26, 2013 from Kourou, French

Guiana, and has successfully been placed in Geosynchronous orbit. It carries four payloads

• Imager (Six Channels)
• Sounder (Nineteen Channels)
• Data Relay Transponder(DRT)
• Satellite Aided Search and Rescue

(SAS & R) INSAT-3D INDIA’s Advanced

Weather Satellite

Aridity Anomaly Maps

Different Products for AAS

Standard Precipitation Index (SPI) Maps

Bi-weekly Cumulative Weekly

Contours

Actual Mean Maximum Temperature Mean Mac Temperature anomaly Actual Mean Minimum Temperature Relative Humidity Cloud amount Wind Speed Mean Min. Temperature anomaly

Satellite Products used for AAS

NDVI maps at State, National and Progress during the week

Sowing Suitability of crops

Surface soil moisture estimation by passive microwave sensor Surface Soil Moisture from SMOS

Other Products Generated Under GKMS

District-wise rainfall -CRIS Contours for different weather parameters Standard Precipitation Index – to be forecasting using seasonal climate systems

At present all the products are generated at district level, however the work has been started to scale down these products to block level.

• crop type classification
• crop condition assessment
• crop yield estimation
• mapping of soil characteristics
• mapping of soil management

practices

• compliance monitoring

(farming practices)

RDBMS

MODELS

GIS

Remote sensing

Weather

Crop Information

Use of GIS software for generation of Agromet Products for location specific advisories

Soil Moisture estimation at Block level in Koriya District of Chattisgarh

NDVI at Block level in Koriya District of Chattisgarh

THANK YOU