The Role of Regional Climate Projections in Managing Complex Socio-Ecological Systems. Daron, J. D., Sutherland, K., Jack, C., & Hewitson, B. C. 15(1):1–12. ![The Role of Regional Climate Projections in Managing Complex Socio-Ecological Systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Climate is one of many factors to be considered in adapting systems to environmental and societal change and often it is not the most important factor. Moreover, given considerable model inadequacies, irreducible uncertainties, and poor accessibility to model output, we may legitimately ask whether or not regional climate projections ought to have a central role in guiding climate change adaptation decisions. This question is addressed by analysing the value of regional downscaled climate model output in the management of complex socio-ecological systems (SESs) vulnerable to climate change. We demonstrate, using the example of the Dwesa-Cwebe region in South Africa, that the management of such systems under changing environmental and socio-economic conditions requires a nuanced and holistic approach that addresses cross-scale system interdependencies and incorporates ” complexity thinking”. We argue that the persistent focus on increasing precision and skill in regional climate projections is misguided and does not adequately address the needs of society. However, this does not imply that decision makers should exclude current and future generations of regional climate projections in their management processes. On the contrary, ignoring such information, however uncertain and incomplete, risks the implementation of maladaptive policies and practices. By using regional climate projections to further explore uncertainties and investigate cross-scale system dependencies, such information can be used to aid understanding of how SESs might evolve under alternative future societal and environmental scenarios. [Excerpt: Introduction] [\n][...] [\n] In order to simulate the climate system and inform societal decisions, climate scientists rely conventionally on experiments using general circulation models (GCMs) which currently resolve atmosphere and ocean processes at horizontal resolutions of approximately 100-200 km (Randall et al. 2007; Taylor et al. 2012; IPCC 2013). Yet decision makers tasked with adapting systems and processes to climate variability and change are usually more interested in the climate at finer spatial scales (Adger et al. 2005). This scale mismatch causes a problem for both scientists striving to provide defensible model projections and for users who are required to interpret climate model output. To bridge this gap, the climate science community has developed dynamical downscaling approaches using regional climate models (RCMs) and empirical-statistical downscaling (ESD) methodologies (Giorgi 1990; Frey-Buness et al. 1995; Murphy 2000; Benestad 2004; Hewitson and Crane 2006), providing higher spatial resolution information over specific regions of interest. Yet far from simplifying matters, practitioners are now required to interpret both GCM information and regional downscaled model information which is conditioned on the imperfect GCM output. How then might practitioners use this wealth of information for guiding adaptation decisions given the need to consider a wide range of other non-climatic factors? The answer is not straightforward. Here we attempt to provide insight to address this question, acknowledging the uncertainties and constraints associated with the current generation of RCD approaches. We focus on the management of SESs and draw on recent methodological developments in the theory of complex systems to better understand how RCD output might be interpreted and communicated to inform adaptation decisions. [...] [Incorporating regional climate downscaling] [\n][...] [\n] RCMs are able to resolve processes and feedbacks that operate at a sub-grid scale GCM resolution (Giorgi 1990). Furthermore, both RCMs and ESD methods (based on empirically derived functions between large-scale predictors and local-scale predictands) can help us to better understand the how climatic uncertainties manifest themselves at different scales, such as those relevant to SES management decisions. To demonstrate that this is the case, we provide an example of three GCM models, identified here as model 1, 2 and 3 for simplicity,2 and the associated downscaled output over the target region of interest, Dwesa-Cwebe (Fig. 3). [...] [\n] Figure 3 shows a range of projections for annual precipitation change derived from direct GCM output, dynamical downscaling and ESD. In this example, we have not assessed the ability of the chosen models to reliably forecast changes in regional precipitation so we cannot interpret the output directly to inform messages of climate change. However, we can use this example to understand the scale dependence of climate model output. [\n] The direct GCM output for the three models selected all shows a change towards drier conditions over the Dwesa-Cwebe region (Fig. 3a-c), albeit with different magnitudes and slightly different spatial characteristics. The downscaled output provides an additional layer of information that in some cases corroborates the GCM messages but in others shows a contradictory pattern of change. The dynamical downscaling (Fig. 3d-f) demonstrates the propagation of the large-scale dynamical GCM responses, such as a strong drying in the south eastern part of the model domain. Yet the higher resolution output provides additional texture, clearly distinguishing a strong drying signal in the mountainous regions (see Supplementary Materials, Fig. S2) and, for model 2 and 3 at least, wetting across the coastal region north of Dwesa-Cwebe. However, in the region of interest, the dynamical downscaling conveys contrasting messages. [...] [\n] This example serves to demonstrate the added intricacies of considering RCD output. Crucially, however, the example shows that RCD does not simply interpolate GCM fields. While the downscaling relies on the GCM output, the downscaling process can lead to qualitatively different messages about how climate might change in the future compared to those derived from assessing GCM output only. In addition, Fig. 3 shows that different methods of downscaling can result in qualitatively different climate change messages. For example, while the output from the ESD method suggests a possible systematic bias of the GCMs in the coastal region of this particular area of South Africa, it does not show the strong topographical influences that are evident in the dynamical downscaling results. We can expect higher resolution models to disagree (in part) with lower resolution models in areas of complex topography but only through examining information produced at different scales of aggregation can we begin to understand the influence of local and regional factors in the local climate response. [...]
@article{daronRoleRegionalClimate2015,
title = {The Role of Regional Climate Projections in Managing Complex Socio-Ecological Systems},
author = {Daron, Joseph D. and Sutherland, Kate and Jack, Christopher and Hewitson, Bruce C.},
date = {2015},
journaltitle = {Regional Environmental Change},
volume = {15},
pages = {1--12},
issn = {1436-378X},
doi = {10.1007/s10113-014-0631-y},
url = {https://doi.org/10.1007/s10113-014-0631-y},
abstract = {Climate is one of many factors to be considered in adapting systems to environmental and societal change and often it is not the most important factor. Moreover, given considerable model inadequacies, irreducible uncertainties, and poor accessibility to model output, we may legitimately ask whether or not regional climate projections ought to have a central role in guiding climate change adaptation decisions. This question is addressed by analysing the value of regional downscaled climate model output in the management of complex socio-ecological systems (SESs) vulnerable to climate change. We demonstrate, using the example of the Dwesa-Cwebe region in South Africa, that the management of such systems under changing environmental and socio-economic conditions requires a nuanced and holistic approach that addresses cross-scale system interdependencies and incorporates ” complexity thinking”. We argue that the persistent focus on increasing precision and skill in regional climate projections is misguided and does not adequately address the needs of society. However, this does not imply that decision makers should exclude current and future generations of regional climate projections in their management processes. On the contrary, ignoring such information, however uncertain and incomplete, risks the implementation of maladaptive policies and practices. By using regional climate projections to further explore uncertainties and investigate cross-scale system dependencies, such information can be used to aid understanding of how SESs might evolve under alternative future societal and environmental scenarios.
[Excerpt: Introduction]
[\textbackslash n][...]
[\textbackslash n] In order to simulate the climate system and inform societal decisions, climate scientists rely conventionally on experiments using general circulation models (GCMs) which currently resolve atmosphere and ocean processes at horizontal resolutions of approximately 100-200 km (Randall et al. 2007; Taylor et al. 2012; IPCC 2013). Yet decision makers tasked with adapting systems and processes to climate variability and change are usually more interested in the climate at finer spatial scales (Adger et al. 2005). This scale mismatch causes a problem for both scientists striving to provide defensible model projections and for users who are required to interpret climate model output. To bridge this gap, the climate science community has developed dynamical downscaling approaches using regional climate models (RCMs) and empirical-statistical downscaling (ESD) methodologies (Giorgi 1990; Frey-Buness et al. 1995; Murphy 2000; Benestad 2004; Hewitson and Crane 2006), providing higher spatial resolution information over specific regions of interest. Yet far from simplifying matters, practitioners are now required to interpret both GCM information and regional downscaled model information which is conditioned on the imperfect GCM output. How then might practitioners use this wealth of information for guiding adaptation decisions given the need to consider a wide range of other non-climatic factors? The answer is not straightforward. Here we attempt to provide insight to address this question, acknowledging the uncertainties and constraints associated with the current generation of RCD approaches. We focus on the management of SESs and draw on recent methodological developments in the theory of complex systems to better understand how RCD output might be interpreted and communicated to inform adaptation decisions. [...]
[Incorporating regional climate downscaling]
[\textbackslash n][...]
[\textbackslash n] RCMs are able to resolve processes and feedbacks that operate at a sub-grid scale GCM resolution (Giorgi 1990). Furthermore, both RCMs and ESD methods (based on empirically derived functions between large-scale predictors and local-scale predictands) can help us to better understand the how climatic uncertainties manifest themselves at different scales, such as those relevant to SES management decisions. To demonstrate that this is the case, we provide an example of three GCM models, identified here as model 1, 2 and 3 for simplicity,2 and the associated downscaled output over the target region of interest, Dwesa-Cwebe (Fig. 3). [...]
[\textbackslash n] Figure 3 shows a range of projections for annual precipitation change derived from direct GCM output, dynamical downscaling and ESD. In this example, we have not assessed the ability of the chosen models to reliably forecast changes in regional precipitation so we cannot interpret the output directly to inform messages of climate change. However, we can use this example to understand the scale dependence of climate model output.
[\textbackslash n] The direct GCM output for the three models selected all shows a change towards drier conditions over the Dwesa-Cwebe region (Fig. 3a-c), albeit with different magnitudes and slightly different spatial characteristics. The downscaled output provides an additional layer of information that in some cases corroborates the GCM messages but in others shows a contradictory pattern of change. The dynamical downscaling (Fig. 3d-f) demonstrates the propagation of the large-scale dynamical GCM responses, such as a strong drying in the south eastern part of the model domain. Yet the higher resolution output provides additional texture, clearly distinguishing a strong drying signal in the mountainous regions (see Supplementary Materials, Fig. S2) and, for model 2 and 3 at least, wetting across the coastal region north of Dwesa-Cwebe. However, in the region of interest, the dynamical downscaling conveys contrasting messages. [...]
[\textbackslash n] This example serves to demonstrate the added intricacies of considering RCD output. Crucially, however, the example shows that RCD does not simply interpolate GCM fields. While the downscaling relies on the GCM output, the downscaling process can lead to qualitatively different messages about how climate might change in the future compared to those derived from assessing GCM output only. In addition, Fig. 3 shows that different methods of downscaling can result in qualitatively different climate change messages. For example, while the output from the ESD method suggests a possible systematic bias of the GCMs in the coastal region of this particular area of South Africa, it does not show the strong topographical influences that are evident in the dynamical downscaling results. We can expect higher resolution models to disagree (in part) with lower resolution models in areas of complex topography but only through examining information produced at different scales of aggregation can we begin to understand the influence of local and regional factors in the local climate response. [...]},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13840722,climate-change,climate-projections,communicating-uncertainty,downscaling,dynamic-downscaling,featured-publication,global-climate-models,regional-climate-models,science-policy-interface,scientific-communication,uncertainty},
number = {1}
}
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C."],"bibdata":{"bibtype":"article","type":"article","title":"The Role of Regional Climate Projections in Managing Complex Socio-Ecological Systems","author":[{"propositions":[],"lastnames":["Daron"],"firstnames":["Joseph","D."],"suffixes":[]},{"propositions":[],"lastnames":["Sutherland"],"firstnames":["Kate"],"suffixes":[]},{"propositions":[],"lastnames":["Jack"],"firstnames":["Christopher"],"suffixes":[]},{"propositions":[],"lastnames":["Hewitson"],"firstnames":["Bruce","C."],"suffixes":[]}],"date":"2015","journaltitle":"Regional Environmental Change","volume":"15","pages":"1–12","issn":"1436-378X","doi":"10.1007/s10113-014-0631-y","url":"https://doi.org/10.1007/s10113-014-0631-y","abstract":"Climate is one of many factors to be considered in adapting systems to environmental and societal change and often it is not the most important factor. Moreover, given considerable model inadequacies, irreducible uncertainties, and poor accessibility to model output, we may legitimately ask whether or not regional climate projections ought to have a central role in guiding climate change adaptation decisions. This question is addressed by analysing the value of regional downscaled climate model output in the management of complex socio-ecological systems (SESs) vulnerable to climate change. We demonstrate, using the example of the Dwesa-Cwebe region in South Africa, that the management of such systems under changing environmental and socio-economic conditions requires a nuanced and holistic approach that addresses cross-scale system interdependencies and incorporates ” complexity thinking”. We argue that the persistent focus on increasing precision and skill in regional climate projections is misguided and does not adequately address the needs of society. However, this does not imply that decision makers should exclude current and future generations of regional climate projections in their management processes. On the contrary, ignoring such information, however uncertain and incomplete, risks the implementation of maladaptive policies and practices. By using regional climate projections to further explore uncertainties and investigate cross-scale system dependencies, such information can be used to aid understanding of how SESs might evolve under alternative future societal and environmental scenarios. [Excerpt: Introduction] [\\n][...] [\\n] In order to simulate the climate system and inform societal decisions, climate scientists rely conventionally on experiments using general circulation models (GCMs) which currently resolve atmosphere and ocean processes at horizontal resolutions of approximately 100-200 km (Randall et al. 2007; Taylor et al. 2012; IPCC 2013). Yet decision makers tasked with adapting systems and processes to climate variability and change are usually more interested in the climate at finer spatial scales (Adger et al. 2005). This scale mismatch causes a problem for both scientists striving to provide defensible model projections and for users who are required to interpret climate model output. To bridge this gap, the climate science community has developed dynamical downscaling approaches using regional climate models (RCMs) and empirical-statistical downscaling (ESD) methodologies (Giorgi 1990; Frey-Buness et al. 1995; Murphy 2000; Benestad 2004; Hewitson and Crane 2006), providing higher spatial resolution information over specific regions of interest. Yet far from simplifying matters, practitioners are now required to interpret both GCM information and regional downscaled model information which is conditioned on the imperfect GCM output. How then might practitioners use this wealth of information for guiding adaptation decisions given the need to consider a wide range of other non-climatic factors? The answer is not straightforward. Here we attempt to provide insight to address this question, acknowledging the uncertainties and constraints associated with the current generation of RCD approaches. We focus on the management of SESs and draw on recent methodological developments in the theory of complex systems to better understand how RCD output might be interpreted and communicated to inform adaptation decisions. [...] [Incorporating regional climate downscaling] [\\n][...] [\\n] RCMs are able to resolve processes and feedbacks that operate at a sub-grid scale GCM resolution (Giorgi 1990). Furthermore, both RCMs and ESD methods (based on empirically derived functions between large-scale predictors and local-scale predictands) can help us to better understand the how climatic uncertainties manifest themselves at different scales, such as those relevant to SES management decisions. To demonstrate that this is the case, we provide an example of three GCM models, identified here as model 1, 2 and 3 for simplicity,2 and the associated downscaled output over the target region of interest, Dwesa-Cwebe (Fig. 3). [...] [\\n] Figure 3 shows a range of projections for annual precipitation change derived from direct GCM output, dynamical downscaling and ESD. In this example, we have not assessed the ability of the chosen models to reliably forecast changes in regional precipitation so we cannot interpret the output directly to inform messages of climate change. However, we can use this example to understand the scale dependence of climate model output. [\\n] The direct GCM output for the three models selected all shows a change towards drier conditions over the Dwesa-Cwebe region (Fig. 3a-c), albeit with different magnitudes and slightly different spatial characteristics. The downscaled output provides an additional layer of information that in some cases corroborates the GCM messages but in others shows a contradictory pattern of change. The dynamical downscaling (Fig. 3d-f) demonstrates the propagation of the large-scale dynamical GCM responses, such as a strong drying in the south eastern part of the model domain. Yet the higher resolution output provides additional texture, clearly distinguishing a strong drying signal in the mountainous regions (see Supplementary Materials, Fig. S2) and, for model 2 and 3 at least, wetting across the coastal region north of Dwesa-Cwebe. However, in the region of interest, the dynamical downscaling conveys contrasting messages. [...] [\\n] This example serves to demonstrate the added intricacies of considering RCD output. Crucially, however, the example shows that RCD does not simply interpolate GCM fields. While the downscaling relies on the GCM output, the downscaling process can lead to qualitatively different messages about how climate might change in the future compared to those derived from assessing GCM output only. In addition, Fig. 3 shows that different methods of downscaling can result in qualitatively different climate change messages. For example, while the output from the ESD method suggests a possible systematic bias of the GCMs in the coastal region of this particular area of South Africa, it does not show the strong topographical influences that are evident in the dynamical downscaling results. We can expect higher resolution models to disagree (in part) with lower resolution models in areas of complex topography but only through examining information produced at different scales of aggregation can we begin to understand the influence of local and regional factors in the local climate response. [...]","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-13840722,climate-change,climate-projections,communicating-uncertainty,downscaling,dynamic-downscaling,featured-publication,global-climate-models,regional-climate-models,science-policy-interface,scientific-communication,uncertainty","number":"1","bibtex":"@article{daronRoleRegionalClimate2015,\n title = {The Role of Regional Climate Projections in Managing Complex Socio-Ecological Systems},\n author = {Daron, Joseph D. and Sutherland, Kate and Jack, Christopher and Hewitson, Bruce C.},\n date = {2015},\n journaltitle = {Regional Environmental Change},\n volume = {15},\n pages = {1--12},\n issn = {1436-378X},\n doi = {10.1007/s10113-014-0631-y},\n url = {https://doi.org/10.1007/s10113-014-0631-y},\n abstract = {Climate is one of many factors to be considered in adapting systems to environmental and societal change and often it is not the most important factor. Moreover, given considerable model inadequacies, irreducible uncertainties, and poor accessibility to model output, we may legitimately ask whether or not regional climate projections ought to have a central role in guiding climate change adaptation decisions. This question is addressed by analysing the value of regional downscaled climate model output in the management of complex socio-ecological systems (SESs) vulnerable to climate change. We demonstrate, using the example of the Dwesa-Cwebe region in South Africa, that the management of such systems under changing environmental and socio-economic conditions requires a nuanced and holistic approach that addresses cross-scale system interdependencies and incorporates ” complexity thinking”. We argue that the persistent focus on increasing precision and skill in regional climate projections is misguided and does not adequately address the needs of society. However, this does not imply that decision makers should exclude current and future generations of regional climate projections in their management processes. On the contrary, ignoring such information, however uncertain and incomplete, risks the implementation of maladaptive policies and practices. By using regional climate projections to further explore uncertainties and investigate cross-scale system dependencies, such information can be used to aid understanding of how SESs might evolve under alternative future societal and environmental scenarios.\n\n[Excerpt: Introduction]\n\n[\\textbackslash n][...]\n\n[\\textbackslash n] In order to simulate the climate system and inform societal decisions, climate scientists rely conventionally on experiments using general circulation models (GCMs) which currently resolve atmosphere and ocean processes at horizontal resolutions of approximately 100-200 km (Randall et al. 2007; Taylor et al. 2012; IPCC 2013). Yet decision makers tasked with adapting systems and processes to climate variability and change are usually more interested in the climate at finer spatial scales (Adger et al. 2005). This scale mismatch causes a problem for both scientists striving to provide defensible model projections and for users who are required to interpret climate model output. To bridge this gap, the climate science community has developed dynamical downscaling approaches using regional climate models (RCMs) and empirical-statistical downscaling (ESD) methodologies (Giorgi 1990; Frey-Buness et al. 1995; Murphy 2000; Benestad 2004; Hewitson and Crane 2006), providing higher spatial resolution information over specific regions of interest. Yet far from simplifying matters, practitioners are now required to interpret both GCM information and regional downscaled model information which is conditioned on the imperfect GCM output. How then might practitioners use this wealth of information for guiding adaptation decisions given the need to consider a wide range of other non-climatic factors? The answer is not straightforward. Here we attempt to provide insight to address this question, acknowledging the uncertainties and constraints associated with the current generation of RCD approaches. We focus on the management of SESs and draw on recent methodological developments in the theory of complex systems to better understand how RCD output might be interpreted and communicated to inform adaptation decisions. [...]\n\n[Incorporating regional climate downscaling]\n\n[\\textbackslash n][...]\n\n[\\textbackslash n] RCMs are able to resolve processes and feedbacks that operate at a sub-grid scale GCM resolution (Giorgi 1990). Furthermore, both RCMs and ESD methods (based on empirically derived functions between large-scale predictors and local-scale predictands) can help us to better understand the how climatic uncertainties manifest themselves at different scales, such as those relevant to SES management decisions. To demonstrate that this is the case, we provide an example of three GCM models, identified here as model 1, 2 and 3 for simplicity,2 and the associated downscaled output over the target region of interest, Dwesa-Cwebe (Fig. 3). [...]\n\n[\\textbackslash n] Figure 3 shows a range of projections for annual precipitation change derived from direct GCM output, dynamical downscaling and ESD. In this example, we have not assessed the ability of the chosen models to reliably forecast changes in regional precipitation so we cannot interpret the output directly to inform messages of climate change. However, we can use this example to understand the scale dependence of climate model output.\n\n[\\textbackslash n] The direct GCM output for the three models selected all shows a change towards drier conditions over the Dwesa-Cwebe region (Fig. 3a-c), albeit with different magnitudes and slightly different spatial characteristics. The downscaled output provides an additional layer of information that in some cases corroborates the GCM messages but in others shows a contradictory pattern of change. The dynamical downscaling (Fig. 3d-f) demonstrates the propagation of the large-scale dynamical GCM responses, such as a strong drying in the south eastern part of the model domain. Yet the higher resolution output provides additional texture, clearly distinguishing a strong drying signal in the mountainous regions (see Supplementary Materials, Fig. S2) and, for model 2 and 3 at least, wetting across the coastal region north of Dwesa-Cwebe. However, in the region of interest, the dynamical downscaling conveys contrasting messages. [...]\n\n[\\textbackslash n] This example serves to demonstrate the added intricacies of considering RCD output. Crucially, however, the example shows that RCD does not simply interpolate GCM fields. While the downscaling relies on the GCM output, the downscaling process can lead to qualitatively different messages about how climate might change in the future compared to those derived from assessing GCM output only. In addition, Fig. 3 shows that different methods of downscaling can result in qualitatively different climate change messages. For example, while the output from the ESD method suggests a possible systematic bias of the GCMs in the coastal region of this particular area of South Africa, it does not show the strong topographical influences that are evident in the dynamical downscaling results. 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