Framing Climate Services: Logics, Actors, and Implications for Policies and Projects

Lugen, Marine

doi:10.3390/atmos11101047

Cited by 8 publications

(6 citation statements)

References 112 publications

(180 reference statements)

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“…Advancements in CIS, especially in more advanced countries, continue to prove that when individuals, communities, and institutions get easy access to timely, predictive, reliable, and usable climate information, they become better equipped and capable to engage in proactive management of climate change risks for effective adaptation (Martínez et al, 2012;Guido et al, 2019). CIS, also referred to as climate services (Lugen, 2020), have since evolved to become an unavoidable prerequisite in CRM for adaptation decision-making (Hellmuth et al, 2011;Popoola et al, 2020).…”

Section: Climate Information Services As Adaptation Learningmentioning

confidence: 99%

“…"Knowing your context" has thus emerged as an organizing principle in CIS and requires scientists, and all involved in the development of climate data be guided by a desire to learn and a disposition to value local realities, differences, and particularities (Brugger et al, 2015). CIS is thus inherently a capacity building mechanism (Martínez et al, 2012); it is about knowledge development and learning to respond directly to CRM and adaptation learning requirements in local communities (Lugen, 2020).…”

Section: Lugen (2020)mentioning

confidence: 99%

“…Of particular concern is the lack of functional climate information services (CIS) regimes to provide well-distilled, timely, relevant, accessible, and usable climate data to inform decision-making in key climate-sensitive sectors and at different levels of governance (Clements et al, 2013;Lugen, 2020).The lack of climate data to support adaptation-decision making is unfortunately widespread and remains a major barrier to current climate adaptation and sustainable development efforts in Africa (Laddey, 2011;Jones et al, 2015;Williams et al, 2021). Development decisions, for the most part, are made devoid of critical data to serve as evidence for well-informed interventions (Orlove et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Climate-Informed Decision-Making in Data-Poor Environments: Managing Climate Risk Through Citizen Science Networks

et al. 2022

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Climate change impacts have become a verifiable reality in most communities in Africa and have already shown its ruthlessness in derailing modest gains made toward sustainable development. While evidence of climate change impacts abounds, especially in key climate-sensitive sectors, not many people living in affected communities have the requisite knowledge, understanding and capacity to respond to emerging impacts. Most communities in Ghana and Africa, broadly, lack the requisite climate change knowledge resources to inform adaptation choices. Adaptation decision-making, in most cases, is reactive, speculative, and based on flawed assumptions and understandings of the climate change phenomenon. This is essentially because most countries lack the capacity to make climate-informed decisions which is also a function of the pervasive lack of efficient climate information services regime across Africa. The paucity of climate change knowledge and associated climate information services is undoubtedly an issue of institutional capacity; however, it is also a function of an enduring culture—a poor attitude toward data collection and application—in decision-making processes. Data-poor environment, or data-poverty, as implied in this work, therefore, broadly describes the absence of a data management culture in decision-making processes; however, specifically to climate change, it describes the lack of functional climate information services regime in local communities in Africa and how such omissions impede the ability of countries to make climate-informed decisions to support adaptation and resilience building. Focusing on Ghana, the paper problematizes the lack of climate information in local communities. The paper argues that Africa's climate crisis is as much a knowledge and learning challenge which requires new and innovative learning approaches to build capacities to facilitate the making of data-driven and climate-informed adaptation decisions in local communities. The paper, therefore, foregrounds citizen-science networks as avenues for community-focused and community-based climate knowledge co-producing mechanisms.

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Section: Climate Information Services As Adaptation Learningmentioning

confidence: 99%

Section: Lugen (2020)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Climate-Informed Decision-Making in Data-Poor Environments: Managing Climate Risk Through Citizen Science Networks

et al. 2022

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“…Supply and demand for climate information have been continuous and arguably growing for decades (Hecht, 1984;Lourenço et al, 2016;Lugen, 2020;Findlater et al, 2021). In this sector of activity, simulations from physics-based global and regional climate models (GCMs and RCMs) play an important role, models being generally viewed as the best source for plausible values of future change in key climate variables (Flato et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

The distinct problems of physical inconsistency and of multivariate bias involved in the statistical adjustment of climate simulations

Alavoine

Grenier

2022

Intl Journal of Climatology

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Bias adjustment of numerical climate model simulations involves several arguments wherein the notion of physical inconsistency is referred to, either for rejecting the legitimacy of bias adjustment in general or for justifying the necessity of sophisticated multivariate techniques. However, this notion is often mishandled, in part because the literature generally proceeds without defining it. In this context, the central objective of this study is to clarify and illustrate the distinction between physical inconsistency and multivariate bias, by investigating the effect of bias adjustment on two different kinds of intervariable relationships, namely a physical constraint expected to hold at every step of a time series and statistical properties that emerge with potential bias over a climatic timescale. To this end, 18 alternative bias adjustment techniques are applied on 10 climate simulations at 12 sites over North America. Adjusted variables are temperature, pressure, relative humidity and specific humidity, linked by a thermodynamic constraint. The analysis suggests on the one hand that a clear instance of potential physical inconsistency can be avoided with either a univariate or a multivariate technique, if and only if the bias adjustment strategy explicitly considers the physical constraint to be preserved. On the other hand, it also suggests that sophisticated multivariate techniques alone are not complete adjustment strategies in presence of a physical constraint, as they cannot replace its explicit consideration. By involving common bias adjustment procedures with likely effects on diverse basic statistical properties, this study may also help guide climate information users in the determination of adequate bias adjustment strategies for their research purposes.

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“…Supply and demand for climate information have been continuous and arguably growing for decades (Hecht 1984;Lourenço et al, 2016;Lugen 2020;Findlater et al, 2021). In this sector of activity, simulations from physics-based global and regional climate models (GCMs and RCMs) play an important role, models being generally viewed as the best source for plausible values of future change in key climate variables (Flato et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

The distinct problems of physical inconsistency and of multivariate bias potentially involved in the statistical adjustment of climate simulations

Alavoine¹,

Grenier²

2021

Preprint

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Bias adjustment of numerical climate model simulations involves several technical and epistemological arguments wherein the notion of physical inconsistency is often referred to, either for rejecting the legitimacy of bias adjustment in general or for justifying the necessity of sophisticated multivariate techniques. However, this notion is often mishandled, in part because the literature generally proceeds without defining it. In this context, the central objective of this study is to clarify and illustrate the distinction between physical inconsistency and multivariate bias, by investigating the effect of bias adjustment on two different kinds of inter-variable relationships, namely a physical constraint expected to hold at every step of a time series and statistical properties that emerge with potential bias over a climatic time scale. The study involves the application of 18 alternative bias adjustment techniques on 10 climate simulations and over 12 sites across North America. Adjusted variables are temperature, pressure, relative humidity and specific humidity, linked by a thermodynamic constraint. The analysis suggests on the one hand that a clear instance of potential physical inconsistency can be avoided with either a univariate or a multivariate technique, if and only if the bias adjustment strategy explicitly considers the physical constraint to be preserved. On the other hand, it also suggests that sophisticated multivariate techniques alone aren’t complete adjustment strategies in presence of a physical constraint, as they cannot replace its explicit consideration. As a supplementary objective, this study relates common optional adjustment procedures with likely effects on diverse basic statistical properties, as an effort to guide climate information users in the determination of adequate bias adjustment strategies for their research purposes.

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Framing Climate Services: Logics, Actors, and Implications for Policies and Projects

Cited by 8 publications

References 112 publications

Climate-Informed Decision-Making in Data-Poor Environments: Managing Climate Risk Through Citizen Science Networks

Climate-Informed Decision-Making in Data-Poor Environments: Managing Climate Risk Through Citizen Science Networks

The distinct problems of physical inconsistency and of multivariate bias involved in the statistical adjustment of climate simulations

The distinct problems of physical inconsistency and of multivariate bias potentially involved in the statistical adjustment of climate simulations

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