26
BAMOS
Dec 2017
Article
Linking forecasts and end users: perspectives
from a Pacific Aid Program
Roan D. Plotz and Lynda E. Chambers
Climate and Oceans Support Program in the Pacific (COSPPac), Community Forecasts, National Forecast Services
Group, Bureau of Meteorology, Melbourne, Victoria, Australia.
Email: [email protected]
Introduction
In recent decades there have been significant advances in the
skill of climate predictions (Bauer et al., 2015). While numerical
weather and climate forecasts are pervasive throughout modern
society, many of the world’s regional and remote communities
do not utilise these forecasts to the degree expected (Gilles
and Valdivia, 2009; Pennesi, 2007, 2011). There are many
reasons for this, including: forecast outputs covering too large
an area to be relevant to local communities; an incomplete
understanding or trust of forecasts; and limited access because
they are not disseminated via appropriate media or time scales
(Gilles and Valdivia, 2009; Plotz et al., 2017). As a consequence,
many regional communities remain detached from the recent
technological advances in forecast accuracy, potentially
reducing their adaptive capacity to an increasingly variable
climate.
The impacts of increased climate variability and extremes on
regional communities are already severe across the Pacific
Islands due to their location in vulnerable environments
(e.g., small low lying islands) and reliance upon resource-
based livelihoods (Nakashima et al., 2012). If the benefits of
contemporary forecasts are to reach more vulnerable end users,
forecast accuracy needs to be balanced with timeliness and
relevance and expressed using language understandable to the
end users (Pennesi, 2007, 2011; Plotz et al., 2017). For example,
an urban professional able to access climate information via
multiple media options will likely have very different forecast
needs to that of a rural subsistence farmer on a remote Pacific
Island. For these farmers, a less accurate forecast, arriving on an
accessible medium such as radio or via a community meeting
with sufficient lead time would be more valuable than a highly
accurate but overly technical forecast delivered online after
irrevocable decisions have already been made. These are some
of the reasons why many local and indigenous communities
continue to rely solely on traditional forecasting methods, such
as observations of biological and physical indicators, to interpret
meteorological phenomena even though seasonal climate
forecasts (SCFs) are provided by the National Meteorological
Services (NMSs) in most countries (Gilles and Valdivia, 2009;
Plotz et al., 2017).
Combining seasonal forecasts with traditional
weather and climate knowledge
Evidence suggests that the uptake of contemporary SCFs by
many local communities can be significantly improved when
combined with traditional knowledge (TK) forecasts (Pennesi
2007, 2011; Plotz et al., 2017). Benefits of this approach include
not only the potential to increase understanding through
use of familiar terminology, but also improved spatial and
temporal resolution of SCFs because TK forecasts are typically
more localized and have shorter outlook periods than the SCFs
currently produced by NMSs (Plotz et al., 2017). However, as
TK forecasting methods remain largely undocumented (i.e.,
oral; Figure 1), any attempt to formally combine traditional
and contemporary forecast systems requires an improved
understanding of the communities where TK forecast usage still
remains strong, including which TK indicators are being used
and how widely they apply.
Although many methods for combining TK and contemporary
seasonal forecasts have been proposed, they can be broadly
categorised into two main approaches: Consensus and Science
Integration (see Plotz et al., 2017). The Consensus approach
brings together groups of experts, usually representatives from
the indigenous group who holds the forecasting knowledge
and representatives from the NMS. These two groups discuss
their respective forecasts and form an agreed or consensus
forecast. In the Science Integration approach the TK forecast
is formally (mathematically) combined with a dynamical or
statistical climate model. Formal combination of traditional and
contemporary SCFs has historically been practised in Africa,
with regular meetings occurring between NMSs and indigenous
TK experts (e.g., Kenya, Tanzania, and Uganda; see Plotz et al.
2017). More recently, Pacific Island NMSs have recognised the
value that TK could bring to the communication and uptake of
their climate products after community feedback indicated a
strong regional preference for traditional forecasting m ethods
(Chambers et al., 2017; Chambers and Plotz, 2017).