Quantifying the value of historical climate knowledge and climate forecasts using agricultural systems modelling

A well tested agricultural systems model was used together with 114 years of historical climate data to study the performance of a dryland wheat–fallow system as impacted by climate variations and nitrogen input levels in southeast Australia, and to investigate the value of: (1) historical climate knowledge, (2) a perfect climate forecast, and (3) various forecasts of targeted variables. The potential value of historical climate records increases exponentially with the number of years of data. In order to confidently quantify the long term optimal nitrogen application rate at the study site at least 30 years of climate data are required. For nitrogen management only, the potential value of a perfect climate forecast is about $54/ha/year with a reduction of excess nitrogen application of 20 kg N/ha/year. The value of an ENSO based forecast system is $2/ha/year. Perfect forecasting of three or six categories of growing season rainfall would have a value of $10–12/ha/year. Perfect forecasts of three or six categories of simulated crop yield would bring about $33–34/ha/year. Choosing integrated variables as a forecasting target, for example crop yield derived from agricultural modelling, has the potential to significantly increase the value of forecasts.     

Policy innovation in a changing climate: Sources,patterns and effects

States have been widely criticized for failing to advance the international climate regime. Many observers now believe that a “new” climate governance is emerging through transnational and/or local forms of action that will eventually plug the resulting governance gaps. Yet states, which remain oddly absent from most discussions of the “new” governance, will remain key players as governance becomes more polycentric. This paper introduces a special issue that explores the ability of states to rise to these interconnected challenges through the analytical prism of policy innovation. It reveals that policy innovation is much more multi-dimensional than is often thought; it encompasses three vital activities: invention (centering on the ‘source’ of new policy elements), diffusion (that produces different ‘patterns’ of policy adoption), and the evaluation of the ‘effects’ that such innovations create in reality. The papers, which range from qualitative case studies to large ‘n’ quantitative studies, offer new insights into the varied roles that states play in relation to all three.They show, for instance that: the policy activity of states has risen dramatically in the past decade; that state innovation is affected to similar degrees by internal and external factors; and that policies that offer flexibility to target groups on how to meet policy goals are most effective but that voluntary reporting requirements are ineffective. This paper draws upon these and many other insights to offer a much more nuanced reflection on the future of climate governance; one that deservedly puts states at the front and center of analysis.     

Recent advances in mountain climate research

The paper provides a brief overview of recent advances in selected areas of mountain climate research. It addresses the contrasting vertical precipitation gradients in the Alps and in central Asia, snow line in the Alps, orographic precipitation in North America, the Mesoscale Alpine Programme wind studies, automatic weather stations in mountains, satellite remote sensing of glacier changes, and temperature change at high elevations. The evidence for altitudinal differences in the temperature response to recent warming is discussed.     

南京信息工程大学气候与气候变化研究进展回顾

简要回顾了南京信息工程大学建校60 a来在气候与气候变化方向的研究历程,总结了南京信息工程大学（简称南信大）气候学科在辐射气候、山地气候、应用气候、气候诊断与预测、统计气候、气候变化与区域响应及其未来预估等方面的重要研究成果。     

Geographical imbalances and divides in the scientific production of climate change knowledge

Studies on scientific production of climate change knowledge show a geographical bias against the developing and more vulnerable regions of the world. If there is limited knowledge exchange between regions, this may deepen global knowledge divides and, thus, potentially hamper adaptive capacities. Consequently, there is a need to further understand this bias, and, particularly, link it with the exchange of knowledge across borders. We use a world-wide geographical distribution of author affiliations in >15,000 scientific climate change publications to show that (1) research production mainly takes place in richer, institutionally well-developed countries with cooler climates and high climate footprints, and (2) the network of author affiliations is structured into distinct modules of countries with strong common research interests, but with little knowledge exchange between modules. These modules are determined mainly by geographical proximity, common climates, and similar political and economic characteristics. This indicates that political-economic, social and educational-scientific initiatives targeted to enhance local research production and collaborations across geographical-climate module borders may help diminish global knowledge divides. We argue that this could strengthen adaptive capacity in the most vulnerable regions of the world.     

Evaluating knowledge exchange in interdisciplinary and multi-stakeholder research

Interdisciplinary and multi-stakeholder research is increasingly being promoted and implemented to enhance understanding of global environment change, identify holistic policy solutions, and assist implementation. These research activities are social processes aiming to enhance the exchange and translation of knowledge. Emphasis on the design and management of knowledge exchange is increasing, but learning about how to do this better is hampered by lack of conceptual development and appropriate methods to evaluate complex and multifaceted knowledge exchange processes. This paper therefore develops principles for the evaluation of knowledge exchange in interdisciplinary, multi-stakeholder environmental change research. The paper is based on an analysis of 135 peer-reviewed evaluations of knowledge exchange from diverse disciplines. The results indicate strong relationships between the field of study (e.g. health care, environmental management), the way knowledge and knowledge exchange were conceptualised and implemented, the approach used for the evaluation, and the outcomes being evaluated. A typology of seven knowledge exchange evaluations is presented to guide discussions about the underlying assumptions of different approaches to knowledge exchange and its evaluation. Five principles for knowledge exchange evaluation are also identified: (i) design for multiple end users; (ii) be explicit about why a particular approach to knowledge exchange is expected to deliver its outcomes; (iii) evaluate diverse outcomes; (iv) use evaluations as part of the process of delivering knowledge exchange; and (v) use mixed methods to evaluate knowledge exchange. We conclude that a catch-all approach to evaluation is neither appropriate nor desirable. Instead, approaches that focus on understanding the underlying processes of knowledge exchange, assess the relative contribution of other factors in shaping outcomes in addition to knowledge exchange, and that involve multiple stakeholders in implementing evaluations, will be the most appropriate for evaluating knowledge exchange in interdisciplinary global environmental change research.     

Amazonian climate: results and future research

Summary Some of the results from the climate component of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), which are presented in this Special Issue are summarised. Recent advances in Amazonian climate modelling are also discussed. There is a range of papers which fall into three groups: surface fluxes and boundary layer growth; convection, clouds and rainfall; and climate modelling. The new insight given by this work is discussed and an argument is made for future research to employ a wider approach to Amazonian climate modelling.     

Harnessing scientific and local knowledge to face climate change in small-scale fisheries

Small-scale fisheries in developing regions are particularly vulnerable to climate change, but the assessment of climate-induced changes and impacts are often hampered by the data poor-situation of these social-ecological systems. Based on 40 years of scientific and local ecological knowledge, we provide a coherent narrative about the effects of a marine hotspot of climate change on a small-scale fishery across different geographical and temporal scales. We applied a mixed-methods approach to assess biophysical changes, social-ecological impacts, and the incremental spectrum of actions implemented at multiple levels to increase the adaptive capacity of a small-scale clam fishery. The warming hotspot here analyzed was the fastest-warming region in the South Atlantic Ocean. Long-term changes in wind intensity and direction were also noticeable at a regional scale. Both sea surface temperature and winds showed a clear shifting pattern in the late 1990 s. These climate-related stressors determined ecosystem and targeted population changes (e.g. clam mass mortalities, slow stock recovery rates after ecological shocks, habitat narrowing), and favored harmful algal bloom-forming organisms. Climate-induced drivers also affected the human component of the social-ecological system, preventing fishers from securing a fulltime livelihood and limiting the fishery economic potential. Adaptive responses at multiple levels provided some capacity to address climate change effects, and transformative pathways are being taken to adapt to climate-induced changes over the long-term. Transformative changes were fostered by the local perception of environmental change, shared narratives, sustained scientific monitoring programs, and the interaction between knowledge systems, facilitated by a bridging organization within a broader process of governance transformation. The combination of autonomous adaptations (based on linking social capital and fishery leaders agency) and government-led adaptations were essential to face the challenges imposed by climate change. Our results serve as a learning platform to anticipate threats and envision solutions to a wide range of small-scale fisheries in fast-warming regions worldwide.     

短期气候预测试验研究的进展

本文回顾了用大气环流模式及大气-海洋耦合模式进行短期气候预测试验研究的进展情况,综述了试验研究得出的一些初步结论,文中也介绍了我国在这方面进行的一些初步试验研究的结果,供短期气候预测研究工作的参考。     

Classifying knowledge on climate change impacts, adaptation, and vulnerability in Europe for informing adaptation research and decision-making: A conceptual meta-analysis

Knowledge on climate change impacts, adaptation, and vulnerability is fragmented, because it is found in disparate case studies which use inconsistent terminology and focus on distinct aspects relevant to adaptation. While large-scale syntheses such as the Assessment Reports of the Intergovernmental Panel on Climate Change provide a high level overview and are useful for international decision-making, there is a need for systematic and flexible access to this research-based knowledge in order to aid future adaptation research and decision-making. Against this background, we present a ‘conceptual’ meta-analysis, a novel approach to meta-analyse studies on climate change impacts, adaptation, and vulnerability in Europe. The meta-analysis results in a classification scheme for relating the diverse studies. This scheme consists of (i) a classification of studies according to the type of adaptation-relevant results they produce and (ii) a hierarchical classification of the regional and thematic context of studies. The implementation of this scheme, for example in the form of a database, overcomes some of the identified gaps of current adaptation knowledge representation. We furthermore present a quantitative analysis of the classified studies that exemplifies how the developed classification scheme can be applied to get a systematic and quantitative overview of the knowledge they contain. Thus, the conceptual meta-analysis and the classification scheme represent a first step towards a systematisation of knowledge on climate change impacts, adaptation, and vulnerability and may be seen as a useful complement to qualitative literature reviews.     

Spatial grids for hurricane climate research

The authors demonstrate a spatial framework for studying hurricane climatology. The framework consists of a spatial tessellation of the hurricane basin using equal-area hexagons. The hexagons are efficient at covering hurricane tracks and provide a scaffolding to combine attribute data from tropical cyclones with spatial climate data. The framework’s utility is demonstrated using examples from recent hurricane seasons. Seasons that have similar tracks are quantitatively assessed and grouped. Regional cyclone frequency and intensity variations are mapped. A geographically-weighted regression of cyclone intensity on sea-surface temperature emphasizes the importance of a warm ocean in the intensification of cyclones over regions where the heat content is greatest. The largest differences between model predictions and observations occur near the coast. The authors suggest the framework is ideally suited for comparing tropical cyclones generated from different numerical simulations.     

Sources of systematic errors in the climatology of a regional climate model over Europe

 Two ten-year simulations made with a European regional climate model (RCM) are compared. They are driven by the same observed sea surface temperatures but use different lateral boundary forcing. For one simulation, RCM AMIP, this forcing is obtained from a standard integration of a global general circulation model (GCM AMIP), whereas for the other simulation, RCM ASSIM, it is derived from a time series of operational analyses. The archive of analysis fields (surface pressure plus winds and temperatures on various pressure levels) is not sufficiently comprehensive to provide directly the full set of driving fields required for the RCM (in particular, no moisture fields are present), so these are obtained via a GCM integration, GCM ASSIM, in which the model is continuously relaxed towards the analysis fields using a data assimilation technique. Errors in RCM AMIP can arise either from the internal RCM physics or from errors in the lateral boundary forcing inherited from GCM AMIP. Errors in RCM ASSIM can arise from the internal RCM physics or the boundary moisture forcing but not from the driving circulation. Although previous studies have considered RCM integrations driven either by output from standard GCM integrations or operational analyses, our study is the first to compare parallel integrations of each type. This allows the total systematic error in an RCM integration driven by standard GCM output to be partitioned into components arising from the driving circulation and the internal RCM physics. These components indicate the scope for reducing regional simulation biases by improving either the driving GCM or the RCM itself. The results relate mainly to elements of surface climate likely to be influenced by both the driving circulation and regional physical processes operating in the RCM. For cloud cover, errors are found to arise largely from the internal RCM physics. Values are too low despite a positive relative humidity bias, indicating shortcomings in the parametrisation scheme used to calculate cloud cover. In summer, surface temperature and precipitation errors are also explained principally by regional processes. For example excessive solar heating leads to anomalously high surface temperatures over southern Europe and excessive drying of the soil reduces precipitation in the south eastern sector of the domain. The lateral boundary forcing reduces precipitation in the south eastern sector of the domain. The lateral boundary forcing also exerts some influence, mainly via a tropospheric cold bias which partially offsets the warming over southern Europe and also increases precipitation. In other seasons the lateral boundary forcing and the regional physics both contribute significantly to the errors in surface temperature and precipitation. In winter the boundary forcing (apart from moisture) is responsible for about 60% of the total error variance for temperature and about 40% for precipitation, due to the cold bias and circulation errors such as a southward shift in the storm track. The remaining errors arise from the regional physics, although for precipitation an excessive supply of moisture from the lateral boundaries also contributes. The skill of the mesoscale component of the surface temperature and precipitation distributions exceeds previous estimates, due to more realistic observed climatology. The mesoscale patterns are very similar in the two RCM simulations indicating that errors in the simulation of fine scale detail arise mainly from inadequate representations of local forcings rather than errors in the large-scale circulation. Circulation errors in RCM AMIP (e.g. cold bias, southward shift of storm track) are also present in GCM AMIP, but are largely absent in RCM ASSIM except in summer. This confirms evidence from previous work that the key to reducing most circulation errors in the RCM lies in improving the driving GCM. Regional processes only make a major contribution to circulation errors in summer, when reduced advection from the boundaries allows errors in surface temperature to be transmitted more effectively into the troposphere. Finally, we find evidence of error balances in the GCM which act to minimise biases in important climatological variables. This reflects tuning of the model physics at GCM resolution. In order to achieve simultaneous optimisation of the RCM and GCM at widely differing resolutions it may be necessary to introduce explicit scale dependences into some aspects of the physics. Received: 17 September 1997/Accepted: 10 March 1998     

How qualitative approaches matter in climate and ocean change research: Uncovering contradictions about climate concern

There is growing acknowledgement of the need for both quantitative and qualitative methods to unravel complex human-environment interactions and inform a more advanced move towards global sustainability. Nonetheless, qualitative methods still play an understated role in climate and ocean change research. One important reason for this are continuing tendencies in the natural sciences to value ‘hard’ and value-free quantitative approaches over ‘soft’ and value-laden qualitative approaches. This paper argues that to overcome such methodological reservations, it is necessary to inform not only about the key characteristics of qualitative research but also – and this has received little attention – about the concrete empirical insights that can be gained from qualitative as opposed to quantitative data, despite sharing the same research focus.The environmental literature still lacks relevant examples from fieldwork that explain in detail how exactly decisive information is elicited from specific qualitative datasets, thereby illustrating how qualitative approaches matter. This paper seeks to help fill this gap by demonstrating to sceptical quantitative researchers the necessity and added value of integrating qualitative data in global environmental change research and highlighting impeding factors. This is done by presenting empirical findings about climate and ocean change adaptation in Norwegian coastal fisheries and elucidating how different qualitative interview techniques reveal that fishers who initially state that they do not worry about climate change actually do worry, and vice versa. Self-categorisation theory from social psychology is used to better explain such contradictory statements. Detecting salient but masked climate concern and understanding the reasons behind it are crucial for avoiding misleading conclusions and effectively tailoring adaptation strategies to the requirements of specific audiences.     

Addressing climate services in SouthAmerican Chaco region through a knowledge coproduction process

When engaging in an interdisciplinary and intersectoral knowledge coproduction process, what premises should (academic and non-academic) participants consider to prevent power dynamics and divergent interests from becoming epistemological obstacles (Bachelard, 1974)? What methodological devices should be adopted to enable a productive dialogue between heterogeneous actors and knowledge? Despite the plethora of literature on the relevance of participatory approaches and the promotion of open science to produce socially meaningful knowledge, most works neglect central challenges present in any coproduction experience. Namely, how to deal with power dynamics, how to challenge the identity anchors of the participants and how to ensure the epistemological conditions for initiating long-term collaboration. Hence, based on these concerns, this paper puts forth the theoretical-methodological basis of a knowledge coproduction approach for the development of climate services for family farming. In addition, we present two major results achieved in the framework of a coproduction process involving academic and non-academic participants: the development of a community-based rainfall monitoring network in the eastern region of the South American Gran Chaco and the codesign of a smartphone application.