PAPER
Information Retrieval for Climate Impact
Report on the MANILA24 Workshop
Maarten de Rijke
University of Amsterdam
The Netherlands
[m.derijke@uva.nl](mailto:m.derijke@uva.nl)
Bart van den Hurk
Deltares
The Netherlands
[bart.vandenhurk@deltares.nl](mailto:bart.vandenhurk@deltares.nl)
Flora Salim
UNSW Sydney
Australia
[flora.salim@unsw.edu.au](mailto:flora.salim@unsw.edu.au)
Alaa Al Khourdajie, Nan Bai, Renato Calzone, Declan Curran, Getnet Demil, Lesley Frew, Noah Gießing, Mukesh Kumar Gupta, Maria Heuss, Sanaa Hobeichi, David Huard, Jingwei Kang, Ana Lucic, Tanwi Mallick, Shruti Nath, Andrew Okem, Barbara Pernici, Thilina Rajapakse, Hira Saleem, Harry Scells, Nicole Schneider, Damiano Spina, Yuanyuan Tian, Edmund Totin, Andrew Trotman, Ramamurthy Valavandan, Dereje Workneh, Yangxinyu Xie [∗]
Abstract
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected – information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts. 1 Introduction
Human-induced climate change, including more frequent and intense extreme events, has caused widespread adverse impacts and related losses and damages to nature and people, beyond natural climate variability. The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for assessment of climate change. Approximately every six years, the IPCC releases an assessment report on the different aspects, drivers, and impacts of climate change based on an assessment of the literature. The IPCC report has contributions from three different working groups. In particular, Working Group II (WGII) of the IPCC “assesses the impacts, adaptation, and vulnerabilities related to climate change, from a world-wide to a regional view of ecosystems and biodiversity, and of humans and their diverse societies, cultures and settlements. ∗ Affiliation not shown for all authors due to space limitations (see Appendix A for details). It considers their vulnerabilities and the capacities and limits of these natural and human systems to adapt to climate change and thereby reduce climate-associated risks together with options for creating a sustainable future for all through an equitable and integrated approach to mitigation and adaptation efforts at all scales”. Fully using the available knowledge on emerging climate change impacts is key to informing global policy processes as well as regional and local risk assessments and on-the-ground action on climate adaptation. The exponential growth in peerreviewed scientific publications on climate change is pushing manual expert assessments to their limits. While literature aggregated on the level of continents or world regions might be useful to the global policy process, informing concrete climate adaptation typically requires localized and contextualized information on climate impacts. Tracking the effectiveness and progress of adaptation actions has proven difficult – any attempt to track adaptation progress will need to be capable of rapidly handling large and varied datasets and literature sources, while acknowledging highly localized and contextualized information. This workshop report brings together researchers from the information retrieval, natural language processing, systematic review, and climate science communities in an attempt to develop an agenda to advance information retrieval for climate impact assessment. We begin by dissecting the problem: what is the information need addressed by the IPCC WGII? We then switch to methodologies, and in particular systematic reviews. We review different resources available (and/or needed) to support information retrieval for climate impact, including test sets and implementations. We then address the question of how to make new technological advances in information retrieval work as part of the IPCC WGII assessment workflow. The paper concludes with abroader perspective. To remain focused, our discussion and analysis is centered around IPCC WGII and its mission – we believe, however, that many of our questions and suggestions have the potential to contribute to the workflows of other IPCC working groups and task forces. 2 The Information Need
The IPCC aims to provide governments at all levels with scientific information that they can use to develop climate policies. The IPCC is divided into three working groups (WGs). WGI deals with the physical science basis of climate change, WGII with climate change impacts, adaptation and vulnerability, and WGIII with mitigation of climate change. The IPCC does not conduct its own research, run models, or make measurements of climate or weather phenomena. Its role is to assess the scientific, technical and socio-economic literature relevant to understanding climate change, its impacts, future risks, and options for adaptation and mitigation. Author teams assess all such information from any source that is to be included in the report. As pointed out in the introduction, approximately every six years, the IPCC releases a series of reports on the different aspects of climate change based on large-scale assessment of all the latest literature. In early 2022, as part of the IPCC’s sixth assessment report, the IPCC released the report of WGII on impacts, adaptation and vulnerability, which “assesses the impacts of climate change, looking at ecosystems, biodiversity and human communities at global and regional levels. It also reviews vulnerabilities and the capacities and limits of the natural world and human societies to adapt to climate change”. The report covers ecosystems, sectors, sustainable development goals, and regions, an integrated technical summary, and a summary for policymakers. The AR6 WGII report pulls together evidence and findings from more than 34,000 journal papers and reports; it is written by 270 authors from 67 countries.
The assessment report currently in development faces a twin challenge: assessing and synthesizing a fast-growing amount of literature related to climate change, and addressing relevant topics and areas where little data is available in the published literature. We believe that information retrieval can potentially help address both aspects, by (i) helping IPCC author teams sift through the literature, and (ii) exposing relevant information previously inaccessible to author teams due to various barriers (language, etc.). 2.1 Proposed Research
We propose an agenda driven by the ambition to produce open evidence synthesis, based on principles of transparent information gathering, curation, traceability, and information quality. Fairness and mitigating different types of bias (e.g., geographic, cultural) are important proposed research lines. Specific attention is to be paid to underrepresented sources of information and communities. Existing AI tools work well with large amounts of data, so have the potential to exacerbate imbalances in data and literature coverage. How do we make sure AI can help with topics or areas where less information is available? Another line of research has to do with the data sources used for evidence synthesis: (i) (capturing and) using domain-specific information, (ii) the usage of grey literature, i.e., the diverse and heterogeneous body of material available outside, and not subject to, traditional academic peerreview processes such as preprints and policy documents, (iii) Indigenous Knowledge and local knowledge, especially if it is not available online, and (iv) (qualitative) data collected through grass roots and/or citizen data science efforts. It is important that retrieval and analysis models can handle multi-modal data (i.e., text, numerical, images, etc.). Only published data currently goes into the IPCC review process, and this is will not cover, e.g., remote sensing data that has no description and interpretation in the literature. The final direction concerns important guardrails that apply to the evidence synthesis process. One is that there is zero tolerance for black boxes – this is important to avoid undermining the credibility of the synthesis with policy makers and governments. An important requirement for “unconventional” sources of information is its quality assessment; IPCC authors need to defend the appropriateness of the use of non-peer reviewed literature; we need to elaborate on this quality assessment of non peer-reviewed literature. And another concerns the carbon emissions when choosing information retrieval (IR) approaches (i.e., energy requirements of using large language models (LLMs)). Do benefits of the model/data/research outweigh the carbon costs? 2.2 Research Challenges
These general research questions manifest themselves along the entire evidence synthesis chain and motivate abroad range of concrete research directions to be investigated concerning the information need that IPCC addresses. Some of these have to do with the scope of the evidence synthesis, some concern data acquisition and quality assessment, and some with contextual factors. Data gathering and sharing. If more diverse sources of evidence are integrated into the evidence synthesis process, how can we ensure open, transparent data collection and sustainable sharing? Data quality. A common suggestion is that evidence should include more diverse sources. Evidence based on solely grey literature should be supported by other evidence lines. However, combining datasets of different structure or manually annotating unstructured data is time-intensive, and grey literature in particular is difficult to work with. How can local expert knowledge be embedded in the evidence synthesis process, while ensuring that data/literature imbalances are not exacerbated?
How can we use generative AI to produce effective communication material for different communities? Characterizing the literature If the input to the synthesis process diversifies, what is the process of search? How, using which credibility criteria, should we determine corpora of scientific and “non-traditional” research outputs (e.g., Indigenous Knowledge), grey literature (e.g., government reports), and select sources to include? The labeling bottleneck Traditionally, assessment reports relied on domain experts who identified and selected, often in teams, documents for relevance and for specific aspects of climate impact as part of the evidence gathering and synthesis process. The rapid growth of scientific literature has made this increasingly challenging. Can evidence gathering and screening be automated with recent advances in the use of LLMs for relevance labeling? Can information extraction tasks be off-loaded to LLMs? And to which degree can LLMs be used to compare and synthesize extracted evidence? Trustworthy synthesis IPCC’s assessment reports inform policy-makers. They provide a scientific basis for governments at all levels to develop climate related policies. The importance of understanding how to synthesize information with varying degrees of fidelity cannot be overstated. For example, in the climate model intercomparison project organizations can submit their cli
2 mate models, which others can use to do climate simulations, usually by compute averages over all the different models. In CMIP6, there were 50+ models; some were “too hot” and averaging would therefore overestimate temperature increase. IPCC6 acknowledged this, stating that models should be weighted according to fidelity, not just blindly synthesized. IPCC’s reports, and the processes and systems underlying them, need to be trustworthy. From a technological point of view, a process or technology can be trustworthy for intrinsic reasons, e.g., because a sufficient level of transparency of the process and technology are provided.
Or it can be trustworthy for extrinsic reasons, i.e., because we have ways of probing it for properties such as accuracy, reliability, repeatability, resilience, and safety. How can these intrinsic and extrinsic approaches be developed for large-scale evidence synthesis? Living evidence Evidence changes over time. What are effective update protocols? The temporal dimension plays a big part in evidence outcomes. Is the amount of literature that is to be assessed in a given IPCC cycle still manageable and can it be expected to be representative for the target time period? What would the requirements be for self-updating synthesis systems? How should we preserve previous versions? Human in the loop synthesis How can we measure the cost, and benefit, of having humans in the evidence synthesis loop? How should we provide feedback to the systems and who provides this feedback? And vice versa, how do we transfer tasks from systems to people, or trigger interventions from people in complex evidence synthesis tasks? Can interactive or collaborative frameworks from other tasks be adopted? 3.3 Obstacles and Risks
We currently know very little about the extent to which automation of key evidence synthesis steps such as query automation, screening automation, and automation of the aggregation results are effective for evidence synthesis in the climate impact domain, and what little we know has usually been learned on English language scientific publications. An important obstacle is the lack of data and benchmarks (especially Indigenous Knowledge, and low-resource languages) and lack of training corpus for these languages. An important risk is limited success in community engagement, across disciplines and across different types of stakeholders.
It is important to do this in the right way by adhering to responsible practices, even though they may not have been established for all types of (non-traditional) domain experts and underrepresented groups. 4 Resources
Progress in the development of effective methods to automate all steps in systematic reviews has been greatly facilitated by the availability of shared resources. In evidence synthesis in healthcare and medicine, for instance, we have witnessed an explosion of methods due to shared tasks and datasets. Prominent examples are the CLEF Technology Assisted Reviews task and the CSMeD: Meta-dataset for systematic review automation evaluation. In information retrieval for climate impact there is an urgent need for similar standardized datasets and test collections. 4.1 Proposed Research
Support for the creation of shared resources (data, code, evaluation methods) requires an understanding of the information and steps that are needed to accomplish the assessment goals, of the (inter)actions of evidence reviewers working towards the goals. This has two sides: (i) collecting information on current assessment practices, and (ii) analyzing data, process and options for automation. It is instructive to revisit the main steps in the systematic review process – querying, screening, and aggregating – and see what progress has been enabled using shared resources and large language models in recent years. The querying stage typically involves steps such as query formulation, query refinement, and query representation. As an example of recent progress using LLMs and building shared resources in the context of querying, Wang et al. explore the use of LLMs in the medical domain to follow instructions and generate queries with high-precision, while trading this off for recall. Using resources from the CLEF initiative for evaluation, the authors find that LLMs are a valuable tool for rapid reviews where time is a constraint and trading off higher precision for lower recall is acceptable. The screening stage typically involves screening prioritization, cut-off prediction, and document classification. As another example of recent progress facilitated by the CLEF e-Health, the Text Retrieval Conference (TREC) Total Recall benchmarking activity, and the TREC Legal
resources, Stevenson and Bin-Hezam propose a stopping method based on point processes and test the robustness of a wide range of statistical and neural stopping methods in a series of contrastive experiments.
Finally, the aggregating stage typically involves information extraction, result synthesis, statistical analyses, and dissemination. As an example of recent progress that uses LLMs and builds on shared resources, Shaib et al. examine the potential of LLMs to synthesize (that is, aggregate the findings presented in) multiple medical articles in away that accurately reflects the evidence. Using articles indexed in the Trialstreamer database and meta-analyses from the Cochrane Library, the authors conclude that human-written summaries in the (bio)medical domain require a level of synthesis that is not yet captured by today’s LLMs. Do these findings generalize from the medical domain to information retrieval for climate impact and to all aspects of the systematic review workflow? What are the resources needed to facilitate similar advances in information retrieval for climate impact and how do we create them? 3 [https://www.clef-initiative.eu](https://www.clef-initiative.eu) 4 https://github.com/CLEF-TAR 5 [http://plg.uwaterloo.ca/~gvcormac/total-recall/](http://plg.uwaterloo.ca/~gvcormac/total-recall/) 6 [https://trec-legal.umiacs.umd.edu/](https://trec-legal.umiacs.umd.edu/) 7 [https://trialstreamer.ieai.robotreviewer.net](https://trialstreamer.ieai.robotreviewer.net) 8 [https://www.cochranelibrary.com/](https://www.cochranelibrary.com/) 4.2 Research Challenges
Resources in support of information retrieval for climate impact can be addressed at two levels – at the level of individual resources and at the aggregate level of multiple resources. First level, at the level of individual resources, we see the following research challenges. 4.2.1 Individual resources
Document collections Several collections relevant to support research into information for climate impact have been used in recent years. White literature collections used include a subset of the Semantic Scholar Open Research Corpus consisting of 600K climate-related articles, subsets (abstracts, titles and metadata (no full text)) from the Web of Science Core Collections databases consisting of 375K and 565K articles. OpenAlex is another option. Grey literature of different sorts has also been used in the context of climate science, even though for IPCC not all types of grey literature (e.g., tweets and media coverage) are considered eligible. Bai et al. share a collection of 60K (multimodal) tweets covering different climate change stances. The GDELT climate news narrative datasets, covering 2009 to 2020, represent television, language, the global perspective and context dating back from half a decade to a decade in all. Frew et al. use web archives and query logs to demonstrate how conflicting climate change stances between government administrations were correlated with content drift and lack of public access to climate information webpages over time. How can we create a large-scale, heterogeneous corpus for climate impact, a corpus, moreover that would support a heterogeneous benchmark containing diverse climate-related tasks? How do we determine the usefulness of including non-peer reviewed scientific reports, theses, policy documents in corpus and benchmark development? How can we create benchmarks, for both static and tracking tasks? Knowledge sources Knowledge sources play an important role in making sense of both white and grey climate literature. Example knowledge sources used to understand climate science literature at scale include KnowWhereGraph, a geo-knowledge graph that is based on existing standards like RDF, OWL and GeoSPARQL, incorporates custom ontologies, and uses a hierarchical grid for spatial representations; it is built upon many open-source spatial datasets, including an expert knowledge graph built using scientific publication/papers on disaster relief. Two other important knowledge sources for anomaly and unusual event detection are the iNaturalist biological species dataset and the local environmental observer (LEO) network. How can we use these resources to enrich white and grey literature at scale and enable different types of semantic search? 9 [https://openalex.org/.](https://openalex.org/) 10 11 12 13 [https://www.ogc.org/publications/standard/geosparql/](https://www.ogc.org/publications/standard/geosparql/) 14 [https://knowwheregraph.org/graph/](https://knowwheregraph.org/graph/) 15 [https://www.inaturalist.org/observations?placeid=any&subview=map](https://www.inaturalist.org/observations?placeid=any&subview=map) 16 [https://www.leonetwork.org/en/docs/about/about](https://www.leonetwork.org/en/docs/about/about) Tools and technology Various domain-specific resources and tools have been developed for information retrieval for climate impact recently. For example, ClimateBert is transformer-based language model that is further pretrained on over 2 million paragraphs of climate-related texts, crawled from various sources such as common news, research articles, and climate reporting of companies. WildfireGPT is an LLM designed to support the analysis of wildfire data, by a diverse set of end users, including researchers and engineers. WildfireGPT is an interesting example of a domain-specific resource not just because of its use cases but also because it uses abroad range of domain-specific data. Thulke et al. introduce ClimateGPT, a family of large language models designed for understanding and responding to information about climate change; the paper demonstrates the effectiveness of adapting a strong general-purpose LLM through continued pre-training on a curated climate corpus and fine-tuning it with high-quality, human-expert-involved instruction data. Tian et al. propose a retrieval-reranking framework that uses LLMs to identify and recommend semantically and spatiotemporally similar unusual environmental events described in news articles and web posts. By integrating embedding-based semantic search with a geo-time re-ranking strategy that integrates multi-faceted criteria including spatial proximity, temporal association, semantic similarity, and category-instructed similarity, the work demonstrates how LLMs can support climate event mining in a real-world platform setting. Mallick et al. describe an end-to-end decision-support tool that accurately identifies location information within textual documents, enabling extraction of geographic-specific climate trends and topics, to provide a concise understanding of geographic-specific climate change trends. How can tools and technology with such far-reaching potential be made accessible to everyone, not just the technologically privileged?
Rajapakse et al. describe a widely used library designed to simplify the training, evaluation, and usage of transformer models that operationalizes the idea of “open-source for all” with adoption in materials science, environmental science, and healthcare. Can we put this perspective to work for all tools and technology (or technology components) that help make sense of information related to climate impact? 4.2.2 Aggregating across multiple resources
There are multiple research challenges that concern the development or usage at different levels of aggregation. Transfer & generalization The first challenge concerns the use of the broad range of advances and resources realized in language technology in recent years. How can we transfer these advances to the domain and tasks of information retrieval for climate impact with minimal effort to reduce costs and accelerate the uptake while achieving reliable performance? Can we take the findings from evidence synthesis in technology assisted reviews in the medical or legal domain and transfer these to the climate domain? Can these questions be turned into few-shot learning problems? Benchmarking The lack of standardized benchmarks and datasets for validating and comparing information retrieval models for climate impact poses significant challenges to the development, assessment, and comparison of information retrieval technologies in this field. Can we use (very) limited human labeling and rely on LLM-generated labels where large quantities of labels are needed for test collection creation? To which degree would a weak-tostrong alignment perspective be helpful for creating the required benchmarking resources? Stacking up Recent years have witnessed great progress in the performance of individual components that make up a systematic review workflow – in querying, screening, and aggregating. Do improvements in the components lead to improvements in the overall assessment process? How do issues such as bias, reproducibility, and performance emerge in complex retrieval for impact pipelines? How can the full review pipeline be optimized effectively? Is the field ready to explore end-to-end learnable approaches to information retrieval for climate impact such as generative information retrieval? Scaling up Managing and processing the vast amounts of heterogeneous data from diverse sources such as sensors, climate models, literature and satellite data poses significant challenges. In the medical domain, organizations such as Cochrane and the Joanna Briggs Institute offer extensive methodological guidance for dealing with large-scale collections, putting a large emphasis on the use of software such as Covidence or JBI Sumari. How do such tools scale up to the size and heterogeneity of materials related to climate impact? Furthermore, linking text data from white and grey literature that describes a climate event to relevant physical measurements about the event, like temperature, rainfall, audio-visual recordings, etc. at a world-wide scale is a challenge that goes far beyond the entity linking challenges that the information retrieval community addresses today. 4.3 Obstacles and Risks
Collaboration among researchers in information retrieval, natural language processing, data engineering, and climate science, developers, and methodologists is essential for advancing the field of information retrieval for climate impact. The fact that these communities and sub-communities – with different sharing and evaluation cultures – are largely disconnected, is a risk that may hinder progress in the development and uptake of resources and benchmarks. 5 Usage
Recall the core steps from IPCC’s overall evidence synthesis process – querying, screening, and aggregating. In each of these steps it employs provenance tracking as a core component of the workflow. By documenting the origin and reliability of the evidence, WGII provides a foundation for informed decision-making on climate change impacts, adaptation, and vulnerability. 17 [https://www.covidence.org](https://www.covidence.org) 18 [https://sumari.jbi.global](https://sumari.jbi.global) 5.1 Proposed Research
IPCC’s way-of-working (with a strong emphasis on provenance tracking) comes with several key challenges. First of all, the exponential growth and increasing complexity of scientific literature complicates the IPCC’s mandate to conduct “comprehensive, objective, open and transparent” assessment in line with the IPCC principles, an issue that is exacerbated by the workload required from scientists to engage with assessment. Second, the length of the IPCC reports poses significant challenges to access the detailed underlying evidence in the main reports. For example, in the most recent assessment round, the two main reports exceed 2,000 pages each, with one exceeding 3,000 pages. Including additional specialized reports and summaries, a total of ∼12,000 pages was reached. The sheer volume hinders the effective dissemination of important scientific findings that do not feature in the summaries. Assuming that the community manages to make progress along the lines suggested in Section 2 (on understanding IPCC’s information need), Section 3 (on methods for addressing IPCC’s information needs), and Section 4 (on resources to implement and assess those methods), how and where can the resulting advances be made to work for IPCC’s assessments, given the challenges outlined above? We need to navigate the vast and ever-expanding corpus of white and grey literature on climate in an efficient manner to enable comprehensive and credible consensus building processes. How to use tools to effectively communicate the extensive findings in the lengthy IPCC reports while maintaining the integrity of the assessments? How to determine the weight of heterogeneous evidence, complexity, uncertainty and the level of confidence? 5.2 Research Challenges
Al Khourdajie summarize several current lines of work to structure the assessment work, in away that addresses the questions listed above and that provides connecting points for the agendas in Section 2, 3 and 4. The IPCC scoping process could use evidence maps to highlight key topics. Once outlines are agreed, topic discovery could help authors of assessment reports plan drafts by clustering publications and topics. A hybrid exploration approach could then be used to inform assessment drafting.
This requires effective communication and collaboration. 6 Final Note
Climate change is real. The information retrieval community has a unique opportunity to inform and foster a collaborative ecosystem of researchers and practitioners that contribute to effective information retrieval solutions in support of research and decision-making to help address the reality of climate change impacts. The agenda setting activities of the MANILA24 workshop were meant to do just that. Preparations for MANILA25, the second edition of the workshop, to be held at SIGIR 2025, are well under way, continuing the goal of developing, maintaining, and executing an effective research agenda for information retrieval for climate change impacts. Acknowledgements
We would like to thank the organizers of SIGIR 2024 for hosting the MANILA24 workshop. AAK was supported by the European Union’s Horizon Europe research and innovation programme. GD was funded by the Academy of Finland Digital Water Flagship Project, European Chist Era Waterline Project. LF was funded by the Old Dominion University Division of Student Engagement & Enrollment Services, Graduate Student Travel Award. MH was funded by the Dutch Research Council. SH acknowledges the support of the Australian Research Council Centre of Excellence for the Weather of the 21st Century.
JK was supported by the Dutch Research Council. TM acknowledges support by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy. TR acknowledges the support of the Dreams Lab, a collaboration between Huawei Finland, the University of Amsterdam, and the Vrije Universiteit Amsterdam. MdR was funded by the Dutch Research Council and the European Union’s Horizon Europe research and innovation program. HS is supported by SmartSat CRC. FS acknowledges the support of the ARC Centre of Excellence for Automated Decision-Making and Society and SmartSat CRC.
DS was funded by the Australian Research Council and the ARC Centre of Excellence for Automated Decision-Making and Society. YT was funded by the U.S. National Science Foundation, Google.org’s Impact Challenge for Climate Innovation Program, and OpenAI’s Researcher Access Program. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors. A Authors and Affiliations
This is a list of authors of this publication. Workshop organizers:
Maarten de Rijke (University of Amsterdam, The Netherlands)
Bart van den Hurk (Deltares, The Netherlands)
Flora Salim (UNSW Sydney, Australia)
Others who contributed to this publication as well:
Alaa Al Khourdajie (Imperial College London, UK and International Institute for Applied System Analysis (IIASA), Austria; workshop participant)
Nan Bai (Delft University of Technology and Wageningen University and Research, The Netherlands; workshop participant) Renato Calzone (ILUSTRE/Tilburg University, The Netherlands; program committee member)
Declan Curran (UNSW Sydney, Australia; workshop participant)
Getnet Demil (University of Oulu, Finland; workshop participant)
Lesley Frew (Old Dominion University, USA; workshop participant)
Noah Gießing (FIZ Karlsruhe/MARDI, Germany; workshop participant)
Mukesh Kumar Gupta (Singapore Management University, Singapore; workshop participant)
Maria Heuss (University of Amsterdam, The Netherlands; program committee member)
Sanaa Hobeichi (UNSW Sydney, Australia; program committee member)
David Huard (Ouranos, Canada; workshop participant)
Jingwei Kang (University of Amsterdam, The Netherlands; workshop participant)
Ana Lucic (University of Amsterdam, The Netherlands; program committee member)
Tanwi Mallick (Argonne National Laboratory, USA; workshop participant)
Shruti Nath (AOPP/University of Oxford, UK; program committee member)
Andrew Okem (Deltares, The Netherlands; workshop participant)
Barbara Pernici (Politecnico di Milano, Italy; workshop participant)
Thilina Rajapakse (University of Amsterdam, The Netherlands; workshop participant)
Hira Saleem (UNSW Sydney, Australia; workshop participant)
Harrisen Scells (University of T¨ubingen, Germany; workshop participant)
Nicole Schneider (University of Maryland, USA; workshop participant)
Damiano Spina (RMIT University, Australia; workshop participant)
Yuanyuan Tian (Arizona State University, USA; workshop participant)
Edmund Totin (Universit´e Nationale d’Agriculture and World Vegetable Center; workshop participant)
Andrew Trotman (University of Otago, New Zealand; workshop participant)
Ramamurthy Valavandan (Deltares, The Netherlands; workshop participant)
Dereje Workneh (Howard University, USA; workshop participant)
Yangxinyu Xie (University of Pennsylvania, USA; workshop participant)
B Process
This paper grew out of the half-day SIGIR 2024 workshop on Information Retrieval for Climate Impact, which was held at SIGIR 2024 on July 18, 2024. The workshop was organized by BvdH, MdR, FS, who invited a number of researchers from the information retrieval and climate science domains to form the program committee. The workshop itself consisted of two parts. The first part was focused on gaining abetter understanding of the information retrieval challenges faced by IPCC WGII, with four presentations on the topics underlying the four
– core sections of this paper as well as eight presentations that were selected based on an open call for contributions. The second part of the workshop consisted of a collaborative writing session, with four groups working on the topics underlying the four core sections of the paper, using “seed topics” contributed by members of the program committee. The workshop organizers used the seed topics, presentations, and outputs of the writing session to draft a first version of the paper, which was then shared with the author team for review and editing.