A critical physical geography (CPG) analysis of climate modeling

My colleague (UCLA PhD Candidate Emma Colven) and I just collaborated on an examination of climate modelling from the perspective of a human and physical geographer. The manuscript, “Bridging the Divide between Human and Physical Geography: Potential Avenues for Collaborative Research on Climate Change”, is up for review now.

From the introduction: “…we examine the possibilities for physical and human geographers to collaborate on the topic of climate modeling. We argue that such efforts hold the potential to construct more democratic forms of climate knowledge, enrich understandings of climate change and more effectively serve goals of social and environmental justice. We first examine geographical research critically examining the production and circulation of climate knowledge. We then then turns to outline what a ‘Critical Physical Geography’ of climate modeling might look like. In particular, we examine the possibilities for geographers to contribute to the production of more democratic climate knowledge through.”

In the process, we generated a lot of material that didn’t make it into the final cut. Among the data I compiled were lists of climate modeling groups; and I find where they come from really interesting. (CPG is about casting a critical eye on how data is compiled and produced, how analyses are carried out, etc. Any scientist should be interested in discovering undetected biases in his/her work — though politics aren’t usually what we mean by bias.)

cmip6_gcms_by_continent
Geographic location of research group contributing GCMs to CMIP6, by continent.

 

I also did a quick comparison of CMIP5 versus CMIP6 contributors (not shown here), and China has surged ahead in a big way with several groups contributing to CMIP6.

cmip6_gcms_by_country
Geographic location of research group contributing GCMs to CMIP6, by host country.

I did a quick search of RCMs (regional climate models, high-resolution models which are embedded within a GCM to supply them with time-varying boundary conditions) on Web of Science and Google Scholar. I picked highly cited papers, which certainly skews the results towards older publications. Nevertheless, the predominance of European RCMs surprised me. (North Africa only ranks second because it tends to be included in ROIs of European RCM experiments.)

rcms_by_country
Geographic location of region analysed by RCMs within a set of 21 published studies returned by WOS and Google Scholar under the query terms “dynamical downscaling” or “regional climate model”.

Data

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