Huggel, C; Salzmann, N; Angulo, L; Calanca, P; Diaz, A; Encinas, C; Jonas, T; Juarez, H; Jurt, C; Konzelmann, T;Lagos, P; Robledo, C; Rohrer, M; Silverio, W; Zappa, M (2009). A new Peruvian-Swiss Program on climate changeadaptation: advancing towards integrative climate change research, implementation and science-policy dialogue.Newsletter of the Mountain Research Initiative, 2:11-14.Postprint available at:http://www.zora.uzh.chPosted at the Zurich Open Repository and Archive, University of Zurich.http://www.zora.uzh.chOriginally published at:Newsletter of the Mountain Research Initiative 2009, 2:11-14.Huggel, C; Salzmann, N; Angulo, L; Calanca, P; Diaz, A; Encinas, C; Jonas, T; Juarez, H; Jurt, C; Konzelmann, T;Lagos, P; Robledo, C; Rohrer, M; Silverio, W; Zappa, M (2009). A new Peruvian-Swiss Program on climate changeadaptation: advancing towards integrative climate change research, implementation and science-policy dialogue.Newsletter of the Mountain Research Initiative, 2:11-14.Postprint available at:http://www.zora.uzh.chPosted at the Zurich Open Repository and Archive, University of Zurich.http://www.zora.uzh.chOriginally published at:Newsletter of the Mountain Research Initiative 2009, 2:11-14.

This article introduces Te Kahu Huruhuru o Kohikohi, a Kaupapa Māori methodology developed to address the need for Indigenous perspectives in climate change research. Climate change policy has traditionally relied on quantitative methods, often neglecting Māori worldviews and holistic environmental understandings. In Aotearoa New Zealand, Māori values, knowledge and practices are often overlooked in climate planning, despite their role as kaitiaki of the land. This methodology bridges Māori ways of knowing with Western research, drawing on whakapapa and incorporating Kaupapa Māori and bricolage to create an adaptable, multiperspective approach. The kahu huruhuru metaphor represents the diverse experiences of Kāi Tahu ki Murihiku and other Māori communities in responding to climate change. By honouring Māori epistemologies and engaging in community dialogue, the methodology offers a decolonising approach for climate change adaptation, combining Western and Indigenous perspectives for meaningful solutions.

In the study of ecological and evolutionary responses to climate change, there has been a growing emphasis recently on complexity deriving from biological interactions [1-3]. Such complexity reflects the dynamics of interactions occurring among levels of organization and across scales of space and time. It therefore poses enormous challenges for the management of global environments and resources over the next century. Climate Change Responses is dedicated to publishing research that addresses this complexity and confronts these challenges to understanding biological responses to climate change. With the launch of this journal, we aim to emphasize the frontiers in climate change research, including, for instance, understanding interactions among individuals and species, bi-directional interactions between abiotic and biotic components of natural systems, and evolutionary capacities to respond to changing environments through adaptation and phenotypic plasticity. The probability that the observed changes in climate since the mid 20 century are not caused by human activity is now diminishingly small [4]. Human activity has therefore led to changes in several environmental variables, notably temperature, dissolved oceanic CO2 level, and rainfall patterns. In addition to their individual impact, environmental drivers interact with each other, thereby exacerbating their effects on biological systems. For example, concurrent increases in temperature and CO2 levels can interact to determine metabolism and gene expression profiles in marine invertebrates [5]. Similarly, together temperature and UV-B radiation can determine performance and life history traits in animals [6]. Quantifying climate change at the spatial level at which it is most relevant for ecological responses is, however, far from straightforward [7]. This is because climate change predictions are mostly made at a global scale over relatively long time periods. However, Helmuth et al. [7]

A scientific consensus that humans are influencing the climatewill be behind any agreements on greenhouse-gas reductions nextmonth. But how can climate research have an optimal influenceon climate policy in the future?

Experiences with an algorithmic technique—cross-impact balances (CIB)—for exploring scenarios rather than relying solely upon expert intuitions are discussed. With CIB, two types of uncertainty for climate change research have been explored: (1) socio-technical uncertainties not represented explicitly in integrated assessment models (sometimes called “context scenarios”) and (2) sampling the space of possible futures to model. By applying CIB retrospectively and prospectively to two global socio-economic scenario exercises for climate change research (the Special Report on Emissions Scenarios and the Shared Socioeconomic Pathways), CIB proved instructive in two ways. First, CIB revealed system behaviors that were not obvious when social variables, such as quality of governance, were not captured explicitly by integrated assessment models. Second, CIB can algorithmically rank different plausible futures according to their self-consistency. These two capabilities have raised awareness about the limitations of accepting what may be “obvious” to model, as practices that focus solely on quantitative variables or rely upon intuitions for scenario analysis may result in detailed analyses of only a subset of important policy-relevant futures. From these experiences, systematic methods like CIB are recommended in conjunction with more detailed modeling to develop integrated socio-technical scenarios in energy-economy research.

Climate change is a worldwide issue that can influence the way of life of all living beings. This study aims to perform a bibliometric analysis of climate crisis and climate change scientific studies. Bibliometric analyses give an in-depth assessment of the literature's publications on the subject, the identification of scientific research trends on the subject, the evaluation of researcher collaboration, and the evaluation of significant issues. The study is qualitative research, and a bibliometric research method was used. The research data was first accessed on 04 August 2022 (Time: 14:34) from the "Web of Science" database as an online search. However, some data were revised on 18 July 2023 (Time: 15:00) using the same database in order to include up-to-date data in the study. The obtained data were transferred to VOSviewer software and analyzed. According to the survey, climate-related articles most used keywords include climate change, climate crisis, sustainability, environment, climate justice, and Anthropocene. Most of the studied papers are from many disciplines, such as environmental sciences, meteorology atmospheric studies, ecology, geosciences multidisciplinary, and environmental studies. When the publications on climate catastrophe are examined by country, the most cited countries are England, Canada, United States, Sweden, and Norway. As a result, international scientific collaboration and data exchange are critical for a successful battle against climate change and the climate crisis. Collaboration and information exchange between disciplines can result in more effective and inclusive solutions. Encouraging studies in other languages and knowing common terminology can help to promote global collaboration. The examination and assessment of scientific findings are vital in enhancing societal awareness and resilience, as well as in developing long-term policy

Historically, scenarios have served multiple uses for climate-related research and decisionmaking. Scenarios provide an essential scientific method for exploring possible future climatse change and its impacts. They also help inform consideration of decisions regarding mitigation and adaptation. Over the last several decades, the climate change research community has produced two widely-used sets of scenarios. The development of a new scenario set is now underway. This volume reports on a novel and central component of this effort – the development of shared socio-economic development pathways (SSPs). Emissions scenarios, a precursor of today’s SSPs, have described future emissions of greenhouse gases (GHGs) and other short-lived and long-lived forcings of the physical climate system. Such emissions scenarios have evolved substantially over time, driven in large part by improvements in the integrated assessment models (IAMs) used to create them. Whereas the earliest scientific assessments, such as the first IPCC scientific assessment (IPCC 1990), used a Climatic Change (2014) 122:351–361 DOI 10.1007/s10584-013-0982-2

Climate change is a scientific topic rarely addressed in Swiss protected areas. Starting from a survey of the spatial distribution of research projects addressing climate change in protected areas derived from Parkforschung.ch data, this report highlights some of the main issues that climate change science is facing in developing research interest in the field. The sources of information are expert interviews carried on during 2018.

This study maps current understanding and research trends on the human dimensions of climate change (HDCC) in the eastern and central Canadian Arctic. Developing a systematic literature review methodology, 117 peer reviewed articles are identified and examined using quantitative and qualitative methods. The research highlights the rapid expansion of HDCC studies over the last decade. Early scholarship was dominated by work documenting Inuit observations of climate change, with research employing vulnerability concepts and terminology now common. Adaptation studies which seek to identify and evaluate opportunities to reduce vulnerability to climate change and take advantage of new opportunities remain in their infancy. Over the last 5 years there has been an increase social science-led research, with many studies employing key principles of community-based research. We currently have baseline understanding of climate change impacts, adaptation, and vulnerability in the region, but key gaps are evident. Future research needs to target significant geographic disparities in understanding, consider risks and opportunities posed by climate change outside of the subsistence hunting sector, complement case study research with regional analyses, and focus on identifying and characterizing sustainable and feasible adaptation interventions.

This article explores the transformative role of artificial intelligence and machine learning in tackling climate change. It highlights how advanced computational techniques enhance our understanding and response to environmental shifts. Machine learning algorithms process vast climate datasets, revealing patterns that traditional methods might overlook. Deep learning neural networks, particularly effective in climate research, analyze satellite imagery, climate sensor data, and environmental indicators with unprecedented accuracy. Key applications include predictive modeling of climate change impacts. Using convolutional and recurrent neural networks, researchers generate high-resolution projections of temperature rises, sea-level changes, and extreme weather events with remarkable precision. AI also plays a vital role in data integration, synthesizing satellite observations, ground-based measurements, and historical records to create more reliable climate models. Additionally, deep learning algorithms enable real-time environmental monitoring, tracking changes like deforestation, ice cap melting, and ecosystem shifts. The article also highlights AI-powered optimization models in mitigation efforts. These models enhance carbon reduction strategies, optimize renewable energy use, and support sustainable urban planning. By leveraging machine learning, the research demonstrates how AI-driven approaches offer data-backed solutions for climate change mitigation and adaptation. These innovations provide practical strategies to address global environmental challenges effectively.