Barriers and enablers to the use of seasonal climate forecasts amongst organisations in Europe
Climatic Change
Barriers and enablers to the use of seasonal climate forecasts amongst organisations in Europe
Marta Bruno Soares 0
Suraje Dessai 0
Marta Bruno Soares 0
0 Sustainability Research Institute and the ESRC Centre for Climate Change Economics and Policy, University of Leeds , Leeds , UK
Seasonal climate forecasts (SCF) provide information about future climate variability that has the potential to benefit organisations and their decision-making. However, the production and availability of SCF does not guarantee its use in decision-making per se as a range of factors and conditions influence its use in different decision-making contexts. The aim of this paper is to identify the barriers and enablers to the use of SCF across organisations in Europe. To achieve that, we conducted 75 in-depth interviews with organisations working across eight sectors (including energy, transport, water and agriculture) and 16 countries. The majority of the organisations interviewed do not currently use SCF. This was due to the low reliability and skill of SCF in Europe but also with other non-technical aspects such as the lack of relevance and awareness of SCF in the organisations. Conversely, the main enabler to the use of SCF was the interactions with the providers of SCF. In addition, the level of organisational resources, capacity and expertise were also significant enablers to the use of SCF in organisations. This paper provides the first empirical assessment of the use of SCF in Europe. Such insights provide not only an overview of the existing barriers and enablers to the use of SCF in Europe and how these can be overcome and negotiated to enhance the usability of SCF, but can also help inform the broader and emerging context of climate services development in Europe.
1 Introduction
Adapting to, and managing the risks of, climate variability is crucial particularly in regions and
economic sectors sensitive to climate conditions. Information about future climate variability
can help to inform decision-making by providing a deeper understanding of the risks involved
Electronic supplementary material The online version of this article (doi:10.1007/s10584-016-1671-8)
contains supplementary material, which is available to authorized users.
*
as well as supporting actions to reduce those risks
(Troccoli et al. 2008)
. The availability of
such information however, does not necessarily guarantee its use in decision-making processes
(McNie 2007; Dilling and Lemos 2011; Feldman and Ingram 2009)
. In fact, the conventional
linear model of science (also known as loading-dock model) where information is developed
in the confinements of the scientific community with the expectation that users will find that
information useful and usable has been challenged as ineffectual for decision-making
(Feldman and Ingram 2009; Cash et al. 2006; Lemos 2015)
.
Sarewitz and Pielke (2007)
argue the need to reconcile the supply and demand of science by
bringing together scientists and decision-makers to frame and develop scientific information
that is useful and usable for decision-making
(McNie 2007)
. From a knowledge systems
perspective Cash et al. (2003, 2005) defend the need for producing salient, credible and
legitimate scientific information in order to make it ‘actionable climate knowledge’
(Meinke
et al. 2006)
. Other contributions to this discussion include end-to-end systems
(Agrawala et al.
2001)
and co-production of science and policy
(Lemos and Morehouse 2005)
. These
underlying narratives permeate much of the discussion around the production of climate science and
information and its use in policy and decision-making contexts.
Sitting between weather forecasts and climate change projections, seasonal climate
forecasts (SCF) can appeal to, and benefit, a range of actors and economic sectors (e.g. agriculture,
disaster risk management, health, water management, energy)
(e.g. Patt et al. 2007; Archer
et al. 2007; Barthelmie et al. 2008)
. These forecasts cover Bthe next month up to a year into the
future^ and the information is provided as monthly or seasonal means (Goddard et al. 2012; p.
622). As such, SCF provide a probabilistic estimate of how climatic parameters (e.g.
temperature, rainfall) may develop in the coming months and thus can B(…) help to inform, focus and
thus improve decision making^
(Rickards et al. 2014; p.237)
. This in turn, can help to enhance
operational activities, aid management processes, inform strategic planning, and increase
profitability
(Harrison et al. 2008; Rickards et al. 2014)
.
Recent scientific developments have led to improvements in SCF for Europe
(Scaife et al.
2014; Doblas-Reyes et al. 2013)
. These include for example, the DEMETER and
ENSEMBLES projects which aimed to develop multi-model ensembles for seasonal-to-annual
forecasts
(Palmer et al. 2004; Hewitt 2005; Weisheimer et al. 2009)
. In addition, the World
Meteorological Organization has designated 1 (...truncated)