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This systematic meta-narrative literature review aims to explore the narratives of trust evident in literature on public (mis)trust relating to climate science published up until May 2021, and to present the main findings from these papers. We identified six narratives of trust: attitudinal trust, cognitive trust, affective trust, contingencies of trust, contextual trust and communicated trust. The papers' main findings spanned theoretical conclusions on the importance of positionality to trust and morality to trustworthiness, to qualitative findings that the scientific community was mainly trusted, to quantitative findings that explored how trust functioned as an independent, dependent or mediating variable. This literature review sheds important light on the interrelationship between climate science and publics, highlights areas for further research, and in its characterisation of trust narratives provides a language for conceptualising trust that can further interdisciplinary engagement. [ABSTRACT FROM AUTHOR]

Background: Information about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3–9 days from the measurement). Recent technological developments have made possible automatic pollen measurements, which are available at high temporal resolution and in real time, although currently only scattered in a few locations across Europe. Materials & Methods: To promote the development of an extensive network across Europe and to ensure that this network will respond to end user needs, a stakeholder workshop was organised under the auspices of the EUMETNET AutoPollen Programme. Participants discussed requirements for the groups they represented, ranging from the need for information at various spatial scales, at high temporal resolution, and for targeted services to be developed. Results: The provision of real‐time information is likely to lead to a notable decrease in the direct and indirect health costs associated with allergy in Europe, currently estimated between €50–150 billion/year.1 Discussion & Conclusion: A European measurement network to meet end user requirements would thus more than pay for itself in terms of potential annual savings and provide significant impetus to research across a range of disciplines from climate science and public health to agriculture and environmental management. [ABSTRACT FROM AUTHOR]

In France and Germany, significant networks of local initiatives have developed around energy issues. This article brings practitioners' viewpoints into discussion. It presents the way in which two of these networks – the CLER-network for the energy transition (France) and the 100% – Regions network (Germany) – each identifies itself, describes its action and build-up process in relation to government action, and conceives of justice issues in the field of climate energy policy. The analysis shows that these two networks share a lot, including political ends (localisation of energy production, empowerment of rural territories), but practice quite differently as intermediaries for local initiatives. This suggests overcoming the transition management vs grassroots divide present in the literature and paying more attention to hybrids. It also brings emphasis on a type of intermediary's work, which has not been covered by the academic literature, namely that of tinkering with multiple supports and embedding action in local-national-supranational networking in order to maintain continuity. In discussing justice dimensions, we emphasize that motives or ends of action are only reflectively related to ideas of justice: justice issues seem bound up with a politics of assembling, either as a marker of federative action (all fighting for justice) or as a potentially divisive issue. [ABSTRACT FROM AUTHOR]

Understanding carbon sources and sinks across the Earth's surface is fundamental in climate science and policy; thus, these topics have been extensively studied but have yet to be fully resolved and are associated with massive debate regarding the sign and magnitude of the carbon budget from global to regional scales. Developing new models and estimates based on state-of-the-art algorithms and data constraints can provide valuable knowledge and contribute to a final ensemble model in which various optimal carbon budget estimates are integrated, such as the annual global carbon budget paper. Here, we develop a new atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) coupled with the GEOS-Chem global transport model to infer surface-to-atmosphere net carbon fluxes from Orbiting Carbon Observatory-2 (OCO-2) V10r XCO 2 retrievals. The 4D-LETKF algorithm is adapted to an OCO-2-based global carbon inversion system for the first time in this work. On average, the mean annual terrestrial and oceanic fluxes between 2015 and 2020 are estimated as - 2.02 and - 2.34 GtC yr -1 , respectively, compensating for 21 % and 24 %, respectively, of global fossil carbon dioxide (CO 2) emissions (9.80 GtC yr -1). Our inversion results agree with the CO 2 atmospheric growth rates reported by the National Oceanic and Atmospheric Administration (NOAA) and reduce the modeled CO 2 concentration biases relative to the prior fluxes against surface and aircraft measurements. Our inversion-based carbon fluxes are broadly consistent with those provided by other global atmospheric inversion models, although discrepancies still occur in the land–ocean flux partitioning schemes and seasonal flux amplitudes over boreal and tropical regions, possibly due to the sparse observational constraints of the OCO-2 satellite and the divergent prior fluxes used in different inversion models. Four sensitivity experiments are performed herein to vary the prior fluxes and uncertainties in our inversion system, suggesting that regions that lack OCO-2 coverage are sensitive to the priors, especially over the tropics and high latitudes. In the further development of our inversion system, we will optimize the data-assimilation configuration to fully utilize current observations and increase the spatial and seasonal representativeness of the prior fluxes over regions that lack observations. [ABSTRACT FROM AUTHOR]

A comparison of retrieved total column ozone amounts TCO between the Pandora #34 spectrometer system and the Dobson #061 spectrophotometer from direct-sun observations was performed on the roof of the Boulder, Colorado NOAA building. This paper, part of an ongoing study, covers a one-year period starting on 17 December 2013. Both the standard Dobson and Pandora total column ozone TCO retrievals required a correction TCOcorr=TCO (1+C(T )) using the effective climatology derived ozone temperature T to remove a seasonal difference caused by using a fixed temperature in each retrieval algorithm. The respective corrections C(T ) are CPandora = 0.00333(T-225) and CDobson = -0.0013(T-226.7) per K. After the applied corrections removed the seasonal retrieval dependence on ozone temperature, TCO agreement between the instruments was within 1% for clear-sky conditions. For clear-sky observations, both co-located instruments tracked the day-to-day variation in total column ozone amounts with a correlation of r² = 0.97 and an average offset of 1.1±5.8 DU. In addition, the Pandora data showed 0.3% annual average agreement with satellite overpass data from AURA/OMI (Ozone Monitoring Instrument) and 1% annual average offset with Suomi-NPP/OMPS (Suomi National Polar-orbiting Partnership, the nadir viewing portion of the Ozone Mapper Profiler Suite). [ABSTRACT FROM AUTHOR]

Lorenz (1963) provided the foundation of chaos theory and inspired a fundamental reappraisal of systems’ nonlinearity in many disciplines, including physical geography. I will provide a brief overview of chaos in nonlinear systems as documented in Lorenz (1963), including the major tenants of chaos theory, followed by a discussion of the effects of chaos theory within meteorology and climate sciences, geomorphology, and ecology and biogeography. In general, Lorenz (1963) provided the intellectual framework for reconsidering the predictability of many physical systems. [ABSTRACT FROM AUTHOR]

Focal point of this review is to provide a comparative display of qualitative and quantitative methodologies employed for the appraisal of interacting energy and climate policies, underlying their key features while presenting the most critical issues and limitations not addressed so far. Qualitative approaches provide a descriptive explanatory analysis of often non-quantifiable process in policy interactions, whereas modeling approaches provide numerical data estimating the extent of policy interaction impacts. Quantitative methods work best for narrowly specified policy combinations, while contextual implications and cause-impact effects are explained further via qualitative ones. In addition most evaluations so far adopt a rational view of policies and policy interactions leaving out a systemic evaluation of the institutionalism of interacting policies. Research analysis of energy and climate policy interactions is still young in comparison to the broad field of policy evaluation and impact assessment. However infants inherently tend to grow. Endeavors for a methodological framework that would allow for a systematic exchange of data between qualitative and quantitative approaches and would also include the relevance of the context as well as key casual relationships behind policy combinations, would provide the basis for further growth of knowledge in the field. [ABSTRACT FROM AUTHOR]

This bibliometric study of a large publication set dealing with research on climate change aims at mapping the relevant literature from a bibliometric perspective and presents a multitude of quantitative data: (1) The growth of the overall publication output as well as (2) of some major subfields, (3) the contributing journals and countries as well as their citation impact, and (4) a title word analysis aiming to illustrate the time evolution and relative importance of specific research topics. The study is based on 222,060 papers (articles and reviews only) published between 1980 and 2014. The total number of papers shows a strong increase with a doubling every 5–6 years. Continental biomass related research is the major subfield, closely followed by climate modeling. Research dealing with adaptation, mitigation, risks, and vulnerability of global warming is comparatively small, but their share of papers increased exponentially since 2005. Research on vulnerability and on adaptation published the largest proportion of very important papers (in terms of citation impact). Climate change research has become an issue also for disciplines beyond the natural sciences. The categories Engineering and Social Sciences show the strongest field-specific relative increase. The Journal of Geophysical Research, the Journal of Climate, the Geophysical Research Letters, and Climatic Change appear at the top positions in terms of the total number of papers published. Research on climate change is quantitatively dominated by the USA, followed by the UK, Germany, and Canada. The citation-based indicators exhibit consistently that the UK has produced the largest proportion of high impact papers compared to the other countries (having published more than 10,000 papers). Also, Switzerland, Denmark and also The Netherlands (with a publication output between around 3,000 and 6,000 papers) perform top—the impact of their contributions is on a high level. The title word analysis shows that the term climate change comes forward with time. Furthermore, the term impact arises and points to research dealing with the various effects of climate change. The discussion of the question of human induced climate change towards a clear fact (for the majority of the scientific community) stimulated research on future pathways for adaptation and mitigation. Finally, the term model and related terms prominently appear independent of time, indicating the high relevance of climate modeling. [ABSTRACT FROM AUTHOR]

Weather radars have been widely used to detect and quantify precipitation and nowcast severe weather for more than 50 years. Operational weather radars generate huge three-dimensional datasets that can accumulate to terabytes per day. So it is essential to review what can be done with existing vast amounts of data, and how we should manage the present datasets for the future climatologists. All weather radars provide the reflectivity factor, and this is the main parameter to be archived. Saving reflectivity as volumetric data in the original spherical coordinates allows for studies of the three-dimensional structure of precipitation, which can be applied to understand a number of processes, for example, analyzing hail or thunderstorm modes. Doppler velocity and polarimetric moments also have numerous applications for climate studies, for example, quality improvement of reflectivity and rain rate retrievals, and for interrogating microphysical and dynamical processes. However, observational data alone are not useful if they are not accompanied by sufficient metadata. Since the lifetime of a radar ranges between 10 and 20 years, instruments are typically replaced or upgraded during climatologically relevant time periods. As a result, present metadata often do not apply to past data. This paper outlines the work of the Radar Task Team set by the Atmospheric Observation Panel for Climate (AOPC) and summarizes results from a recent survey on the existence and availability of long time series. We also provide recommendations for archiving current and future data and examples of climatological studies in which radar data have already been used. [ABSTRACT FROM AUTHOR]

A compilation of several cruises data from 1998 to 2013 was used to derive polynomial fits that estimate total alkalinity (AT) and total inorganic carbon (CT) from measurements of salinity and temperature in the Mediterranean Sea surface waters. The optimal equations were chosen based on the 10-fold cross validation results and revealed that a second and third order polynomials fit the AT and CT data respectively. The AT surface fit showed an improved root mean square error (RMSE) of ±10.6 μmol kg-1. Furthermore we present the first annual mean CT parameterization for the Mediterranean Sea surface waters with a RMSE of ±14.3 μmol kg-1. Excluding the marginal seas of the Adriatic and the Aegean, these equations can be used to estimate AT and CT in case of the lack of measurements. The seven years averages (2005-2012) mapped using the quarter degree climatologies of the World Ocean Atlas 2013 showed that in surface waters AT and CT have similar patterns with an increasing eastward gradient. The surface variability is influenced by the inflow of cold Atlantic waters through the Strait of Gibraltar and by the oligotrophic and thermohaline gradient that characterize the Mediterranean Sea. The summer-winter seasonality was also mapped and showed different patterns for AT and CT. During the winter, the AT and CT concentrations were higher in the western than in the eastern basin, primarily due to the deepening of the mixed layer and upwelling of dense waters. The opposite was observed in the summer where the eastern basin was marked by higher AT and CT concentrations than in winter. The strong evaporation that takes place in this season along with the ultra-oligotrophy of the eastern basin determines the increase of both AT and CT concentrations. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) could revolutionize our ability to understand and address climate change. Studies to date have focused on specific AI applications to climate science, technologies, and policy. Yet despite the vast demonstrated potential for AI to change the way in which climate research is conducted, no study has presented a systematic and comprehensive understanding of the way in which AI is intersecting with climate research around the world. Using a novel merged corpus of scholarly literature which contains millions of unique scholarly documents in multiple languages, we review the community of knowledge at the intersection of climate change and AI to understand how AI methods are being applied to climate-related research and which countries are leading in this area. We find that Chinese research institutions lead the world in publishing and funding research at the intersection of climate and AI, followed by the United States. In mapping the specific AI tasks or methods being applied to specific climate research fields, we highlight gaps and identify opportunities to expand the use of AI in climate research. This paper can therefore greatly improve our understanding of both the current use and the potential use of AI for climate research. [ABSTRACT FROM AUTHOR]

Aerosol–cloud interactions play a crucial role in shaping Earth's climate and hydrological cycle. Observing these interactions with high precision and accuracy is of the utmost importance for improving climate models and predicting Earth's climate. Over the past few decades, lidar techniques have emerged as powerful tools for investigating aerosol–cloud interactions due to their ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and temporal resolutions. This review paper provides an overview of recent advancements in the study of ACI using lidar techniques. The paper begins with a description of the different cloud microphysical processes that are affected by the presence of aerosol, and with an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol–cloud interactions. This includes investigations into the role of aerosol particles in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research. By reporting the latest findings and methodologies, this review aims to provide valuable insights for researchers engaged in climate science and atmospheric research. [ABSTRACT FROM AUTHOR]

Regional decadal predictions have emerged in the past few years as a research field with high application potential, especially for extremes like heat and drought periods. However, up to now the prediction skill of decadal hindcasts, as evaluated with standard methods is moderate, and for extreme values even rarely investigated. In this study, we use hindcast data from a regional climate model (CCLM) for 8 regions in Europe to construct time evolving climate networks and use the network correlation threshold (link strength) as a predictor for heat periods. We show that the skill of the network measure to predict the low frequency dynamics of heat periods is similar to the one of the standard approach, with the potential of being even better in some regions. [ABSTRACT FROM AUTHOR]

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. This paper presents a new technique of combining the driving force of a time series obtained using the Slow Feature Analysis (SFA) approach, then introducing the driving force into a predictive model to predict non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill. [ABSTRACT FROM AUTHOR]

In this paper, a new issue that very low relative humidity observations exist in a deeper atmosphere layer in the low- and mid-troposphere is studied on the basis of the global radiosonde observations from December 2008 to November 2009, and the humidity retrieval productions from Formosa Satellite mission-3/Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC, referred to as COSMIC hereafter) in the same period. Results show that these extremely dry relative humidity observations are considerable universal in the worldwide operational radiosonde data. Globally, the annual average occurrence probability of the extremely dry relative humidity is of 4.2 %. These measurements usually occur between 20° and 40° latitudes in both Northern and Southern Hemispheres, and in the height from 700 to 450 hPa in the low- and mid-troposphere. Winter and spring are the favoured seasons for these extremely dry humidity observations, with the maximum ratio of 9.53% in the Northern Hemisphere and 16.82% in the Southern Hemisphere. The phenomenon is mainly related to the performance of the radiosonde humidity sensor and the cloud types traversed by the radiosonde balloon. These extremely low relative humidity observations are erroneous, which cannot represent the real atmospheric status, and are likely caused by the failure of humidity sensor. However, these observations have been archived as the formal data. It will affect the reliability of numerical weather prediction, the analysis of weather and climate, if the quality control procedure is not applied. [ABSTRACT FROM AUTHOR]

Ships' logbooks are now an accepted part of the repertoire of data sources in climate change studies. This article examines some of the particular issues surrounding logbooks from the Arctic region in the so-called pre-instrumental period. Attention is given to the means by which narrative descriptions of wind, weather and sea ice cover can be reliably expressed in index form. Consideration is also given to the various means by which these data can be most effectively managed for scientific analysis as in most cases they were not recorded for such purposes. Many such logbooks remain yet to be digitized and the methods described here can be applied with equal confidence in future through such undertakings using English language documents. [ABSTRACT FROM AUTHOR]

Data assimilation systems and retrieval systems that are based upon a maximum likelihood estimation, many of which are in operational use, rely on the assumption that all of the errors and variables involved follow a normal distribution. This work develops a series of statistical tests to show that mixing ratio, temperature, wind and surface pressure follow non-normal, or in fact, lognormal distributions thus impacting the design-basis of many operational data assimilation and retrieval systems. For this study one year of Global Forecast System 00:00 UTC 6 h forecast were analyzed using statistical hypothesis tests. The motivation of this work is to identify the need to resolve whether or not the assumption of normality is valid and to give guidance for where and when a data assimilation system or a retrieval system needs to adapt its cost function to the mixed normal-lognormal distribution-based Bayesian model. The statistical methods of detection are based upon Shapiro-Wilk, Jarque-Bera and a χ² test, and a new composite indicator using all three measures. Another method of detection fits distributions to the temporal-based histograms of temperature, mixing ratio, and wind. The conclusion of this work is that there are persistent areas, times, and vertical levels where the normal assumption is not valid, and that the lognormal distribution-based Bayesian model is observationally justified to minimize the error for these conditions. The results herein suggest that comprehensive statistical climatologies may need to be developed to capture the non-normal traits of the 6 h forecast. [ABSTRACT FROM AUTHOR]

Abstract: The ecosystem services (ES) approach is a framework for describing the benefits of nature to human well‐being, and this has become a popular instrument for assessment and evaluation of ecosystems and their functions. Grazing lands can provide a wide array of ES that depend on their management practices and intensity. This article reviews the trends and approaches used in the analysis of some relevant ES provided by grazing systems, in line with the framework principles of the Millennium Ecosystem Assessment (MA). The scientific literature provides reports of many studies on ES in general, but the search here focused on grazing systems, which returned only sixty‐two papers. This review of published papers highlights that: (i) in some papers, the concept of ES as defined by the MA is misunderstood (e.g., lack of anthropocentric vision); (ii) 34% of the papers dealt only with one ES, which neglects the need for the multisectoral approach suggested by the MA; (iii) few papers included stakeholder involvement to improve local decision‐making processes; (iv) cultural ES have been poorly studied despite being considered the most relevant for local and general stakeholders; and (v) stakeholder awareness of well‐being as provided by ES in grazing systems can foster both agri‐environmental schemes and the willingness to pay for these services. [ABSTRACT FROM AUTHOR]

A 30-year (1980–2010) climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations is constructed by using four global atmospheric reanalyses: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); the Japanese 55-year Reanalysis (JRA-55); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Climate Forecast System Reanalysis (CFSR). Both the reanalysis ensemble mean (REM) and the differences in each reanalysis from the REM are investigated in the latitude–pressure domain for December–January–February and for June–July–August. For the REM investigation, two residual vertical velocities (the original one and one evaluated from residual meridional velocity) and two mass streamfunctions (from meridional and vertical velocities) are compared. Longwave (LW) radiative heating and shortwave (SW) radiative heating are also shown and discussed. For the TEM equations, the residual terms are also calculated and investigated for their potential usefulness, as the residual term for the momentum equation should include the effects of parameterized processes such as gravity waves, while that for the thermodynamic equation should indicate the analysis increment. Inter-reanalysis differences are investigated for the mass streamfunction, LW and SW heating, the two major terms of the TEM momentum equation (the Coriolis term and the Eliassen–Palm flux divergence term), and the two major terms of the TEM thermodynamic equation (the vertical temperature advection term and the total diabatic heating term). The spread among reanalysis TEM momentum balance terms is around 10 % in Northern Hemisphere winter and up to 50 % in Southern Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are found in the vertical advection, for which the structure is inconsistent with the differences in heating. The results shown in this paper provide basic information on the degree of agreement among recent reanalyses in the stratosphere and upper troposphere in the TEM framework. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) can cause severe wind and rain hazards. Unusual TC tracks and their extreme precipitation forecasts have become two difficult problems faced by conventional models of primitive equations. The case study in this paper finds that the numerical computation of the climatological component in conventional models restricts the prediction of unusual TC tracks. The climatological component should be a forcing quantity, not a predictor in the numerical integration of all models. Anomaly-based variable models can overcome the bottleneck of forecast time length or the one-week forecasting barrier, which is limited to less than one week for conventional models. The challenge in extreme precipitation forecasting is how to physically get the vertical velocity. The anomalous moisture stress modulus (AMSM), as an indicator of heavy rainfall presented in this paper, considers the two conditions associated with vertical velocity and anomalous specific humidity in the lower troposphere. Vertical velocity is produced by the orthogonal collision of horizontal anomalous airflows. [ABSTRACT FROM AUTHOR]

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]