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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]

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]

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]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

In the online seminar series "Perspectives on climate sciences: from historical developments to future frontiers", which took place during 2020–2021, well-known and established scientists from several fields – including mathematics, physics, climate science and ecology – presented their perspectives on the evolution of climate science and on relevant scientific concepts. This special issue aims to create a platform for a more detailed elaboration of the topics discussed in the seminars but also to publish new scientific findings. In this paper, we first give an overview of the content of the seminar series, and then we introduce the written contributions to this special issue. In line with the spirit of the seminar series, this paper is structured along thematic areas of the broad field of climate science, conveying different perspectives on the climate system: geophysical fluid dynamics, dynamical systems theory, multiscale processes, statistical physics, paleoclimate and the human dimension. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a spectral surface reflectivity climatology based on observations made by TROPOMI on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface for 21 wavelength bands ranging from 328 to 2314 nm and for each calendar month. The spatial resolution of the database grid is 0.125° × 0.125°. A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database. Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important. Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1 , OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0.002–0.02 in the UV-VIS (below 500 nm), up to 0.003 in the NIR (670–772 nm), and below 0.001 in the short-wave infrared (SWIR) (2314 nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER, we made use of comparison with the MODIS surface bi-directional reflectance distribution function (BRDF) for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER, and we found good agreement with the MODIS surface BRDF. The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polar-orbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including Ocean and Land Colour Instrument (OLCI) on the Sentinel-3 satellites, Sentinel-5, and Multi-viewing Multi-channel Multi-polarisation imager (3MI) on the MetOp-SG-A1 satellite to be launched in 2025, as well as the future CO2M mission. [ABSTRACT FROM AUTHOR]

The concept of a digital twin is intriguing as it presents an innovative approach to solving numerous real-world challenges. Initially emerging from the domains of manufacturing and engineering, digital twin research has transcended its origins and now finds applications across a wide range of disciplines. This multidisciplinary expansion has impressively demonstrated the potential of digital twin research. While the simulation aspect of a digital twin is often emphasized, the role of artificial intelligence (AI) and machine learning (ML) is severely understudied. For this reason, in this paper, we highlight the pivotal role of AI and ML for digital twin research. By recognizing that a digital twin is a component of a broader Digital Twin System (DTS), we can fully grasp the diverse applications of AI and ML. In this paper, we explore six AI techniques—(1) optimization (model creation), (2) optimization (model updating), (3) generative modeling, (4) data analytics, (5) predictive analytics and (6) decision making—and their potential to advance applications in health, climate science, and sustainability. [ABSTRACT FROM AUTHOR]

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Heritage science is an inherently practice-oriented field that aims to support our understanding, and conservation, of heritage. Research is commonly undertaken using laboratory or field-based methodologies, but given the ethical and scale constraints, over time and space, of these approaches, process-based models should provide a tool for exploring practical solutions. Unlike other fields, such as climate science and ecology, there appears limited engagement with modelling within heritage science. The characteristics and use of processed-based models published in the field is examined to explore tensions in using models to transfer understanding between research and practice. By examining models that investigate interactions between heritage materials and environment, we find that, at best, model outputs may be used by other researchers or occasionally by heritage institutions; or more commonly, the model's existence is used as a justification of research, yet without meaningful engagement within either the academic and heritage practitioner communities. Some models are unlikely to be used in practice as they have been developed at spatial or temporal scales incompatible with being truly applicable to objects or sites, or can seem to advance theory without engaging with practice. The uptake of models by researchers who rerun or change the code is rare. Models that seem to gain substantial use appear to benefit from graphical user interfaces that make them easy to run. Evidence of models in solving real-world conservation problems is hard to find. This may arise because practical applications are rarely reported in academic journal literature and open access publications. There is some evidence they are revealed in conferences and possibly internal heritage organisation reports, but this gray literature doesn't readily feedback into the development and refinement of existing models. It is likely the use of models would increase if mechanisms were available to support the development of user interfaces, training workshops and the ability of practical use cases to be fed back to the modelling community. [ABSTRACT FROM AUTHOR]

In the North West Shelf region of Australia is a surface current (Holloway Current), which flows southwestward along the shelf break. This paper describes a seasonal undercurrent below the Holloway Current. A 5-day climatology is constructed from the output of an eddy-resolving oceanic general circulation model (OGCM). A seasonal northeastward-flowing undercurrent is found on the upper continental slope during the climatological April-May. This undercurrent reverses during February-March. During its annual cycle, the phase of the undercurrent tends to propagate southwestward and upward. The annual frequency dominates, but the positive and negative phases of the undercurrent are not symmetric in the yearly cycle because of the contributions from the semi-annual and 1/3-annual components. We propose a hypothesis that this undercurrent is a beam of coastal trapped wave (CTW). As an initial attempt to assess the plausibility of this hypothesis, we construct an idealized linear coastal-trapped wave (CTW) solution driven by an idealized harmonic meridional winds at the annual frequency. The solution takes the form of a beam originating from the forcing region on the continental shelf and propagating offshore and southward. When it emerges on to the continental slope, it takes the form of an undercurrent. This idealized solution shares several properties with the undercurrent in the OGCM despite several discrepancies. [ABSTRACT FROM AUTHOR]

The optimal control problems of degenerate parabolic equations have many applications in economics, physics, climatology, and so on. Motivated by the applications, we consider the optimal control problems of a class of nonlinear degenerate parabolic equations in this paper. The main result is that we deduce the first order necessary condition for the optimal control problem of nonlinear degenerate parabolic equations by variation method. Moreover, we investigate the uniqueness of the solutions to the optimal control problems. For the linear equations, we obtain the global uniqueness, while for the nonlinear equations, we obtain only the local uniqueness. Finally, we give a numerical example to validate the theoretical results. [ABSTRACT FROM AUTHOR]

An important component of the risks from climate change arises from outcomes that are very unlikely, but whose impacts if they were to occur would be extremely severe. Examples include levels of surface warming, or changes in the water cycle, that are at the extreme of plausible ranges, or crossing of a climate system "tipping point" such as ice sheet or ocean circulation instability. If such changes were to occur their impacts on infrastructure or ecosystems may exceed existing plans for adaptation. The traditional approach of ensemble climate change projections is not well suited to managing these High Impact‐Low Likelihood (HILL) risks, where the objective is to "prepare for the worst" rather than to "plan for what's likely." In this paper we draw together a number of ideas from recent literature, to classify four types of HILL climate outcome and to propose the development of a practical "toolkit" of physical climate information that can be used in future to inform HILL risk management. The toolkit consists of several elements that would need to be developed for each plausible HILL climate outcome, then deployed individually to develop targeted HILL risk management approaches for individual sectors. We argue that development of the HILL toolkit should be an important focus for physical climate research over the coming decade, and that the time is right for a focused assessment of HILL risks by the Intergovernmental Panel on Climate Change in its 7th Assessment Cycle. Plain Language Summary: To prepare for the risks that arise from climate change (and avoid them where possible), it is important to understand how climate is likely to change in future, and what the impacts are likely to be. Over many years, climate science has developed sophisticated climate projections to estimate these likely impacts, and these are widely used to plan how people and societies will need to adapt to climate change. However it is also important to understand possibilities that are unlikely, but would have even more severe impacts if they did occur—for example, global warming levels at the high end of plausible estimates, or crossing a "tipping point" for major changes in ice sheets or ocean currents. A different type of information is needed to plan for these risks. In this paper we propose a new set of climate information "tools" to respond to these high‐impact risks. The tools include plausible scenarios of extreme outcomes, and early warning systems to detect if they are on the horizon. Combining these new tools with existing climate projections will allow society to understand more fully the risks of climate change, and to plan for the likely effects while preparing for the worst. Key Points: Climate outcomes or events that have a high impact are a key component of climate risk, even if their likelihood is lowTraditional climate projections are of limited use to inform management of high impact‐low likelihood risksPhysical climate science needs an increased focus on storylines, early warning and monitoring to inform management of high impact risks [ABSTRACT FROM AUTHOR]

As the Arctic and its rivers continue to warm, a better understanding of the possible future impacts on people would benefit from close partnership with Indigenous communities and scientists from diverse fields of study. We present efforts by the Arctic Rivers Project to conduct community‐engaged research to increase collective understanding of the historical and potential future impacts of climate change on rivers, fish, and Indigenous communities. Working in central to northern Alaska and the Yukon Territory in Canada, the project seeks to engage with Indigenous communities in ethical and equitable ways to produces science that is useful, useable, and used that may serve as an example for future research efforts. Toward this goal, we formed an Indigenous Advisory Council and together developed project‐specific knowledge co‐production protocols. This paper provides a novel model of design and implementation to co‐produce knowledge with communities across a large study domain. Plain Language Summary: The Arctic and rivers located in the Arctic and subarctic are warming due to climate change. To understand the impacts this warming will have on people, partnering with impacted Indigenous communities in the region is important. It is also important that these partnerships are ethical and equitable and produce science that is actionable. This paper discusses efforts undertaken by a specific project, the Arctic Rivers Project, to conduct ethical and equitable research with Indigenous communities and generate science that is useful to those communities. Through this research our goal is to better understand potential future impacts of climate change on rivers, fish, and Indigenous communities in central northern Alaska and the Yukon Territory in Canada. To achieve this goal, the project formed an Indigenous Advisory Council (IAC) and together developed guidelines for how we can work collaboratively with Indigenous communities. Our specific process of forming an IAC and guidelines is, to our knowledge, a new way to approach collaborative research when working across a large geographic area. We present our process here so that it may provide an example for other research efforts. Key Points: Arctic climate information can be made useful, useable, and used by equitably accounting for diverse community adaptation needs through knowledge co‐productionInstitutional and community capacity, including means and ability, is necessary for equitable knowledge co‐production to occurWe present an approach for co‐producing knowledge with Indigenous communities that can serve as an example for other scientific efforts [ABSTRACT FROM AUTHOR]

The Global Historical Climatology Network (GHCN) monthly precipitation dataset contains historical time series for thousands of land surface stations worldwide. Initially released in 1992 and revised in 1998, the dataset has been employed in a variety of applications over the past three decades, including operational monitoring, applied research, and international assessments. This paper describes the data and methods used to compile the latest edition (version 4), which has three major enhancements. The first enhancement is to the station network, which increased in size by a factor of five due to the inclusion of dozens of new source datasets, most notably GHCN Daily (GHCNd). The second improvement is the application of a rule-based algorithm to compare and merge records representing the same location. The third enhancement is to the quality assurance approach, now consisting of 18 new checks based on GHCNd and other operational systems. Updated monthly, the resulting dataset consists of time series of monthly precipitation totals at more than 120,000 worldwide stations, including more than 33,000 active observing sites. [ABSTRACT FROM AUTHOR]

Based on the best-track dataset from the Shanghai Typhoon Institute/China Meteorological Administration, the paper provides a comprehensive summary and analysis of tropical cyclone (TC) activities in the Western North Pacific (WNP) and the South China Sea (SCS) for 2022. Using the historical climatology from 1951 to 2020, the anomalous conditions during 2022 in TC frequency, origin locations, tracks, intensity, and duration for the entire ocean basin as well as landfall events in China are examined. Results show that the overall TC frequency is slightly lower than normal, but the multiple TC events have a very high frequency of occurrence. Origin locations of TCs, which mark the starting points of their paths, show a large westward and northward deviation from climatology. Around 40% of the named TCs exhibit a shift in their direction of movement from westerly to easterly. Additionally, comparisons of the means, medians, upper and lower quartiles all indicate that the intensity of TCs in 2022 is generally lower than the climatology, with the duration of TCs at tropical storm intensity or above being shorter than usual. A notable observation is the fewer incidence of TC landfalls in China, but with a geographical concentration in Guangdong Province. These anomalous annual TC activities are influenced by related atmospheric and oceanic environmental conditions modulated by multi-scale climate variability. The findings provide useful information for enhancing disaster mitigation strategies in the Asia-Pacific region. [ABSTRACT FROM AUTHOR]

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales. [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

This paper presents climatological features of the longitudinal structures WN4, WN3, and WN2 and their drivers observed in the lower thermospheric temperatures and in the ionospheric TEC. For this purpose, two long-term data sets are utilized: the satellite SABER/TIMED temperature measurements, and the global TEC maps generated with the NASA JPL for the interval of 2002–2022. As the main drivers of the longitudinal structures are mainly nonmigrating tides, this study first investigates the climatology of those nonmigrating tides, which are the main contributors of the considered longitudinal structures; these are nonmigrating diurnal DE3, DE2, and DW2, and semidiurnal SW4 and SE2 tides. The climatology of WN4, WN3, and WN2 structures in the lower thermosphere reveals that WN4 is the strongest one with a magnitude of ~20 K observed at 10° S in August, followed by WN2 with ~13.9 K at 10° S in February, and the weakest is WN3 with ~12.4 K observed over the equator in July. In the ionosphere, WN3 is the strongest structure with a magnitude of 5.9 TECU located at −30° modip latitude in October, followed by WN2 with 5.4 TECU at 30 modip in March, and the last is WN4 with 3.7 TECU at −30 modip in August. Both the climatology of the WSA and the features of its drivers are investigated as well. [ABSTRACT FROM AUTHOR]

The article discusses citizen science and climate services in cities, highlighting current practices, new approaches, and future opportunities for enhancing urban climate resilience. Topics include the role of citizen engagement and co-design in transforming urban climate resilience, the use of advanced interactive technologies and crowdsourcing for community-driven climate monitoring, and the development of co-produced climate services for improved urban decision-making and adaptation.

Adaptation decision‐scientists increasingly use real‐option analysis to consider the value of learning about future climate variable development in adaptation decisions. Toward this end learning scenarios are needed, which are scenarios that provide information on future variable values seen not only from today (as static scenarios), but also seen from future moments in time. Decision‐scientists generally develop learning scenarios themselves, mostly through time‐independent (stationary) or highly simplified methods. The climate learning scenarios thus attained generally only poorly represent the uncertainties of state‐of‐the‐art climate science and thus may lead to biased decisions. This paper first motivates the need for learning scenarios by providing a simple example to illustrate characteristics and benefits of learning scenarios. Next, we analyze how well learning scenarios represent climate uncertainties in the context of sea level rise and present a novel method called direct fit to generate climate learning scenarios that outperforms existing methods. This is illustrated by quantifying the difference of the sea level rise learning scenarios created with both methods to the original underlying scenario. The direct fit method is based on pointwise probability distributions, for example, boxplots, and hence can be applied to static scenarios as well as ensemble trajectories. Furthermore, the direct fit method offers a much simpler process for generating learning scenarios from static or "ordinary" climate scenarios. Plain Language Summary: Many climate change adaptation decisions require large investments in infrastructure (e.g., dikes), while at the same time future projections about critical variables (e.g., sea level rise) are highly uncertain. Decision‐scientists address these challenges with methods based on flexibility and staged decision‐making. For example, a coastal decision‐maker could implement a dike with a wider foundation, and, if necessary, upgrade the dike height in the future. The decision‐maker will learn by observing future sea level rise if higher dike protection levels are actually necessary in the future. In order to assess whether it is economically beneficial to wait for future learning through observations, and thus to justify additional expenses for flexible infrastructure investments, learning scenarios are required. Learning scenarios provide projections of critical variables seen from today and from future moments in time. For example, learning scenarios of sea level rise contain sea level rise projections seen from 2050 onward, depending on a certain amount of sea level rise observed until 2050. In this paper, we provide a simple example to illustrate coastal decision‐making with a learning scenario, propose a new method to generate learning scenarios, and apply this method to generate sea level rise learning scenarios. Key Points: We show how climate learning scenarios can be applied for improving and justifying investments in flexible long‐lasting infrastructureWe develop sea level rise learning scenarios based on Intergovernmental Panel on Climate Change sixth Assessment Report using a novel method termed direct fitOur new method reduces the average deviation of learning scenarios from the original data by 83% compared to standard methods [ABSTRACT FROM AUTHOR]

This paper studies early arguments in Sweden for combating climate change. We show how scientific results in relation to climate change entered the political sphere as part of the debate on energy in the 1970s, a process we propose to name energysation. We argue that the use of climate science by pro-nuclear political actors served as a way of maintaining a course set by a high-energy society while simultaneously trying to outmanoeuvre the growing environmental anti-nuclear and low-energy movement. When the pro-nuclear power side met with resistance, this led to a displacement of climate change knowledge away from the realm of the national political sphere and specific energy forms, a process we conceptualise as de-energysation. By highlighting conflicts and the political framings of climate change in the early years 1974-1983, we suggest that the history of these frames influences current delay in climate change mitigation and limits the range of actions and ways of addressing the ongoing climate emergency. [ABSTRACT FROM AUTHOR]

Research Question: The aim of this paper is to determine the factors influencing safety climate in different industrial sectors in project-based organizations. Motivation: Systematic research on occupational safety is not so frequent in the Republic of Serbia. The lack of research that deals with safety climate in project-based organizations exploring the attitudes of employees in different industrial sectors is evident. In the research conducted by Milijic et al. (2013) a model for measuring safety climate factors was proposed and the following step is to identify the most important safety climate issues for each industrial sector as a key to fostering better safety performance. Chen et al. (2018a) concluded that engagement at all levels of the company is essential to fostering better safety performances as well as that safety awareness is the most important factor influencing workers' safety efficiency. The authors of the present paper seek those particular factors that adequately describe safety climate in project-based organizations by examining the attitudes of workers in different industrial sectors. Idea: The main idea of this paper is to evaluate and rank safety climate in different industrial sectors and different workplaces considering the following five factors of safety climate: Safety and competence awareness, Occupational safety management commitment, Safety training and tools, Safety practices and procedures and Organizational environment. Data: Safety climate analysis was conducted on a set of data collected as a part of a survey carried out in eleven project-oriented organizations operating in different industrial sectors on the territory of Serbia. Tools: This paper proposes a multi-criteria analysis of the data collected from a national survey using the Entropy-PROMETHEE-GAIA method. Findings: A complete ranking of safety climate in project-oriented organizations based on the opinions of employees in different industrial sectors resulted in Energy as the bestranked sector. On the other hand, the worst-ranked sector is High-rise construction. The results of the complete ranking of safety climate in project-oriented organizations based on the opinions of employees at different workplaces in different industrial sectors showed that the best-ranked alternatives were manager working places. When it comes to production workers on projects, the most favorable safety climate is in energy workplaces and for non-productive workers, while the most favorable safety climate is on projects in the field of mechanical engineering. Contribution: Adopting the conclusions from the paper can, in practice, improve safety climate along with safety at work in project-based organizations. [ABSTRACT FROM AUTHOR]

Causal discovery from river runoff data aids flood prevention and mitigation strategies, garnering attention in climate and earth science. However, most climate causal discovery methods rely on conditional independence approaches, overlooking the non-stationary characteristics of river runoff data and leading to poor performance. In this paper, we propose a river runoff causal discovery method based on deep reinforcement learning, called RCD-DRL, to effectively learn causal relationships from non-stationary river runoff time series data. The proposed method utilizes an actor-critic framework, which consists of three main modules: an actor module, a critic module, and a reward module. In detail, RCD-DRL first employs the actor module within the encoder-decoder architecture to learn latent features from raw river runoff data, enabling the model to quickly adapt to non-stationary data distributions and generating a causality matrix at different stations. Subsequently, the critic network with two fully connected layers is designed to estimate the value of the current encoded features. Finally, the reward module, based on the Bayesian information criterion (BIC), is used to calculate the reward corresponding to the currently generated causal matrix. Experimental results obtained on both synthetic and real datasets demonstrate the superior performance of the proposed method over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Intergovernmental Panel on Climate Change (IPCC), which is the United Nations body for assessing the science related to climate change, has recently recognized the natural carbonation process as a way of carbon offsetting with mortar and concrete. Accordingly, this activity could be recognized as a carbon removal process for which certification should be granted. The aim of the certification of carbon removal is to promote the development of adequate and efficient new carbon removal processes. Therefore, the main objective of this study is to provide reliable results on carbon dioxide uptake by cement-based materials in Spain. Yearly, greenhouse gas emissions are reported to the United Nations Framework Convention on Climate Change (UNFCCC) by each country, and the natural carbonation should be added up to the carbon accounting. Therefore, natural carbonation should be included in the IPCC Guidelines for National Greenhouse Gas Inventories, and such accounting information should be made available promptly to the national regulatory authorities. This paper provides the results of carbon dioxide uptake by Spanish cement-based materials from 1990 to 2020 by using an easy method of estimating the net carbon dioxide emissions (simplified method) considering the carbon dioxide released by the calcination during clinker production (process emissions). The outcome of this study reveals that there was 93,556,000 tons of carbon dioxide uptake by the mortar and concrete manufactured in Spain from 1990 to 2020. [ABSTRACT FROM AUTHOR]

We present an application of quantile generalized additive models (QGAMs) to study spatially compounding climate extremes, namely extremes that occur (near‐) simultaneously in geographically remote regions. We take as an example wintertime cold spells in North America and co‐occurring wet or windy extremes in Western Europe, which we collectively term Pan‐Atlantic compound extremes. QGAMS are largely novel in climate science applications and present a number of key advantages over conventional statistical models of weather extremes. Specifically, they remove the need for a direct identification and parametrization of the extremes themselves, since they model all quantiles of the distributions of interest. They thus make use of all information available, and not only of a small number of extreme values. Moreover, they do not require any a priori knowledge of the functional relationship between the predictors and the dependent variable. Here, we use QGAMs to both characterize the co‐occurrence statistics and investigate the role of possible dynamical drivers of the Pan‐Atlantic compound extremes. We find that cold spells in North America are a useful predictor of subsequent wet or windy extremes in Western Europe, and that QGAMs can predict those extremes more accurately than conventional peak‐over‐threshold models. Plain Language Summary: In this paper we propose a new data‐driven method to study climate extremes occurring simultaneously in multiple, possibly remote, locations. Such extremes can pose a greater threat to human societies than single, isolated extremes, as their effects may exacerbate each other and lead to correlated losses. The method we suggest requires fewer assumptions than conventional extreme value statistical techniques, and can help us to identify previously unknown relationships between the extremes themselves and their possible drivers. We exemplify its use by studying the co‐occurrence of periods of unusually cold weather in North America and subsequent uncommonly strong wind and abundant precipitation in Western Europe. We find that the new method has better predictive power for the European extremes than conventional statistical approaches. Furthermore, we confirm the results of previous studies suggesting an association between the wintertime extremes in North America and Western Europe. Key Points: Quantile general additive models (QGAMs) can model the relationship between compound climate extremes flexibly and robustlyNorth American cold spells show some predictive skill for wet or windy extremes in Western Europe, even when accounting for confoundersGiven relevant atmospheric predictors, QGAMs can predict these extremes more accurately than peak‐over‐threshold models in most regions [ABSTRACT FROM AUTHOR]

The combination of precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics. The model has several idealizations, including that all condensates immediately fall out of the atmosphere, and is run across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a 10-fold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of "tropical cyclone worlds" to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly "turning on" mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill. [ABSTRACT FROM AUTHOR]

Global response to climate‐sensitive infectious diseases has been uncertain and slow. The understanding of the underlying vulnerabilities which forms part of changes created by forces within the Earth system has never before been critical until the coronavirus disease 2019, "COVID‐19" pandemic with the initial developmental phase linked to weather elements and climate change. Hence, the heightened interest in climate‐sensitive infectious diseases and GeoHealth, evident in the renewed calls for "One Health" approach to disease management. "One Health" explains the commonality of human and animal medicine, and links to the bio‐geophysical environment, yet are at crossroads with how forces within the Earth system shape etiologies, incidences, and transmission dynamics of infectious diseases. Hence, the paper explores how these forces, which are multistage and driven by climate change impacts on ecosystems affect emerging infectious diseases, leading to the question "what drive the drivers of diseases?" Three questions that challenge broad theories of Earth system science on boundaries and connectivity emerged to guide study designs to further interrogating disease surveillance and health early warning systems. This is because, climate change (a) drives prevailing biological health hazards as part of forces within the Earth system, (b) shifts disease control services of ecosystems and functioning to effectively regulate disease incidence, and (c) modifies pathogen—species hosts relationships. Hence, the need to rethink pluralistic concepts of climate‐sensitive diseases in their infection and management from a GeoHealth perspective, which "One Health" potentially conveys, and to also maintain ecosystem health. Plain Language Summary: Climate change, an Earth system process (forces deep inside the earth that bring about adjustments) is considered the greatest threat to human health in the twenty‐first century. Climate change has been linked to shifts in ecosystem processes, services and interactions that affect how many organisms thrive including those causing diseases. Yet, how climate change influences specific processes between air, land, life, and water on earth to ultimately affect diseases is still emerging. Collectively partly defined as GeoHealth, it has become topical amidst the coronavirus disease 2019 (COVID‐19) pandemic. Thus, its origin partly links to changes in land‐atmosphere interactions. Hence, the paper poses questions to better appreciate how climate change influence forces within the Earth system to subsequently impact infectious diseases and management in a holistic approach called "One Health," which GeoHealth is positioned to contribute to. Thus, "One Health" provides pathways to environmentally safeguard and sustain ecosystem health and human well‐being. Key Points: Earth system science of ecosystems and climate change hardly tells mechanisms of disease‐causing organisms, detection, and managementRevisiting "One Health" amidst "coronavirus disease 2019" is opportune pathway for GeoHealth's influence of climate‐sensitive infectious diseasesModern Earth system science of climate context of "One Health" is key to maintaining human well‐being and ecosystem integrity [ABSTRACT FROM AUTHOR]

Soil moisture operational product system (SMOPS) is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real-time blended soil moisture (SM) for numeric weather prediction and national water model applications. However, all individual satellite SM data ingested into the current operational SMOPS are scaled to global land data assimilation system (GLDAS) 0–10 cm SM climatology before the combination. As a result, the useful information from the original microwave SM retrievals could be lost, and the GLDAS model errors could be brought into the final SMOPS blended product. In this paper, we propose to scale the individual SM retrievals to the soil moisture active passive (SMAP) data through building regression models. The rescaled individual SM data and the SMAP observations then have similar climatology and dynamics, which allows producing the SMOPScdr (distinguishing with the current operational SMOPSopr) data using an equal-weight averaging approach. With respect to the in situ SM measurements, the developed SMOPScdr is more successful tracking the surface SM status than the individual satellite SM products with significantly decreased errors. The proposed method also preserves the climatology of the reference SMAP data for the period when SMAP is not available, allowing us to produce a long-term SMOPScdr data product. [ABSTRACT FROM AUTHOR]

The paper deals with long-term means of 557.7 nm atomic oxygen airglow intensity (OI) and air temperature within the mesopause over the southern regions of East Siberia. Data on temperature and emission parameters were obtained with a SABER radiometer, KEO Scientific "Arinae" Fabry–Pérot interferometer, SATI spectrometer and NRLMSIS model over the Tory Geophysical Observatory (52° N, 103° E). Annual variations of 557.7 nm emission intensity and temperature obtained in observations differ from model approximations. Potential reasons for the discrepancies revealed are discussed. [ABSTRACT FROM AUTHOR]

This essay addresses the long-term effectiveness of urban climate change adaptation approaches, based, inter alia, on work in the C40 city network. We argue that in most cities, the dominant framing of climate risk management almost exclusively focuses on short-term incrementalities and preventive solutions directly tackling hazards, vulnerability, and exposure. This approach has serious flaws, leading to missed opportunities for longer-term sustainable urban development. Until very recently, climate science usually provided only a marginal input to long-term urban planning and design. We argue that any analysis of urban climate risk management and the associated climate services should be broadened beyond solely climate focusing on impacts. In this context, the development of positive urban visions is a key gap for both research and practice. A change is required from negatively addressing risks to positively pursuing a positive vision of attractive, resilient, and sustainable cities. The emphasis on short-term incremental solutions should shift towards long-term transformation. This embodies a paradigm shift from "function follows system" to "system follows function". For many cities, this also means a change in procedural practice from siloed top-down to integrated, participatory urban transformation. Our main argument in this paper is that simple, longer-term sustainable urban transformation would not only reduce climate risks but also enhance overall environmental quality, economic opportunities, and social wellbeing. [ABSTRACT FROM AUTHOR]

This research paper explores S M Nazmuz Sakib's hypothesis of aerosol-sea ice feedback and its implications for climate system dynamics. The hypothesis suggests that changes in aerosol emissions significantly impact sea ice concentration and thickness in the Arctic, which, in turn, affect aerosol transport and deposition over the Tibetan Plateau. The paper presents a comprehensive analysis of the hypothesis, including the underlying facts, a proposed formula for the aerosol-sea ice feedback, and the potential variations of this feedback based on regional and temporal patterns of aerosol emission changes. Furthermore, the paper introduces the Sea Ice-Aerosol-Cloud Feedback (SIACF) Index and its application to historical incidents related to aerosol emission changes. The SIACF Index provides a quantitative measure to evaluate the influence of changes in aerosol emissions on sea ice concentration and thickness. The paper concludes by emphasizing the importance of testing this hypothesis through global aerosol models, observations, and historical incidents to gain a deeper understanding of aerosol-climate interactions and develop effective mitigation strategies. [ABSTRACT FROM AUTHOR]

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ > 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences. [ABSTRACT FROM AUTHOR]

How do firms address complex collective action problems effectively? Institutional and stakeholder research suggests that firms may avoid the tragedy of the commons by aligning the interests of critical proximate stakeholders in ways that governments cannot accomplish. This phenomenological paper investigates this possibility by analyzing Amazon rainforest preservation by Natura, a Brazilian cosmetics company. The results indicate that Natura internalized environmental externalities by linking ecologically conscious consumers with rural Amazonian communities. A differences-in-differences analysis compares forest preservation and fire activity in the municipalities that Natura entered with those in which it did not enter. Natura's impact is identified through an instrumental variable analysis using missing satellite images, which Natura relied upon to decide which municipalities to enter. Quantitative results tie Natura's entry into municipalities with forest preservation. Analysis of three mechanisms associates Natura's involvement with stakeholder decisions to cultivate diverse forest-generated crops instead of clearing the land for conventional agriculture. This study contributes to the management literature by suggesting how firms can address important global challenges, such as rainforest preservation, by investing in stakeholder capability development and by creating institutional arrangements in line with those envisioned elsewhere. This paper was accepted by George Serafeim, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the Clarkson Centre for Business Ethics [CAD 7,500.00] and Canada's Social Sciences and Humanities Research Council. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4884. [ABSTRACT FROM AUTHOR]

Private sector innovation is critical to mitigating and adapting to climate change. This paper studies innovation in solar energy technology, a key source of clean energy that has experienced rapid price declines over the past decade. To understand the causes and effects of innovation, I estimate a dynamic structural model of competition among solar panel manufacturers. The model captures important features of the industry, including the role of government subsidies for solar adoption, and I employ a unique measure of technological progress that is observable and verifiable. The results produce two main insights. First, ignoring innovation by firms can generate biased estimates of the effects of government policy. Second, decentralized government intervention in a global market generates spillovers; a subsidy in one country causes international firms to innovate more, leading to lower prices and increased adoption elsewhere. This spillover underscores the need for international coordination by governments and the private sector to address climate change. This paper was accepted by Erica Plambeck, Special Section of Management Science on Business and Climate Change. Funding: The author acknowledges support from the U.S. Environmental Protection Agency [Science to Achieve Results (STAR) Fellowship Assistance Agreement FP-91769401-0] and Harvard [Joseph Crump Fellowship]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4662. [ABSTRACT FROM AUTHOR]

Many countries have adopted payment for ecosystem services (PES) programs to reduce deforestation. Empirical evaluations find such programs, which pay forest owners to conserve forest, can lead to anywhere from no impact to a 50% reduction in deforestation level. To better understand the potential effectiveness of PES contracts, we use a principal–agent model, in which the agent has an observable amount of initial forest land and a privately known baseline conservation level. Commonly used conditional contracts perform well when the environmental value of forest is sufficiently high or sufficiently low, but can do arbitrarily poorly compared with the optimal contract for intermediate values. We identify a linear contract with a distribution-free per-unit price that guarantees at least half of the optimal contract payoff. A numerical study using U.S. land use data supports our findings and illustrates when linear or conditional contracts are likely to be more effective. This paper was This paper was accepted by Beril Toktay, Special Section of Management Science on Business and Climate Change. Funding: W. Dai Li was supported by a Stanford Interdisciplinary Graduate Fellowship. I. Lo was supported by an Environmental Venture Project Grant from the Stanford Woods Institute for the Environment. Supplemental Material: Data and the e-companion are available at https://doi.org/10.1287/mnsc.2021.4273. [ABSTRACT FROM AUTHOR]

This paper shows how green investing spurs companies to mitigate their carbon emissions by raising the cost of capital of the most carbon-intensive companies. Companies' emissions decrease when the wealth share of green investors and their sensitivity to climate externalities increase. We show that the impact of green investors primarily governs companies' long-run emissions. Companies are further incentivized to reduce their emissions when green investors anticipate tighter climate regulations and climate-related technological innovations. However, heightened uncertainty regarding future climate risks alleviates green investors' pressure on the cost of capital of companies and pushes them to increase their emissions. Calibrated on U.S. data, our model suggests that, albeit effective, the impact of green investors remains limited given their current wealth share and practices. This paper was accepted by George Serafeim, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the Europlace Institute of Finance. T. De Angelis received funding from the Engineering and Physical Sciences Research Council [Grant EP/R021201/1], and P. Tankov received funding from the Finance for Energy Markets research initiative of the Institut Europlace de Finance. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4472. [ABSTRACT FROM AUTHOR]

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While the majority of adults across the United States report that they believe that global warming is happening, far fewer report discussing global warming (Howe et al., 2015, https://doi.org/10.1038/nclimate2583; Marlon et al., 2022, https://climatecommunication.yale.edu/visualizations‐data/ycom‐us/). One way to inspire further climate action and engagement is to increase individuals' capacity to confidently and effectively discuss climate change. Climate communication science highlights that such communication is most effective when it is anecdotal, narrative, tailored to the audience, and place‐based. To generate climate conversations and inspire action in a variety of communities, partners at the University of Minnesota Extension piloted a program to train community members from across the state of Minnesota in effective climate communication through a series of instructional workshops, coaching, and participant‐led communication activities. Following the training portion of the program, participants identified and hosted their own climate‐related communication activities in their communities. These "climate conversations" took place across Minnesota and included community events, dialogue with elected leaders, and conversations stimulated through literature, among other activities. In their communities, program participants sparked conversations, initiated long‐term climate action efforts, and improved their sense of efficacy in response to climate change. Participants also reported that they improved their climate conversation skills, increased their local climate knowledge, established a support network with fellow participants, had reduced anxiety around communicating, and increased their confidence in being able to communicate about climate change in their communities. This pilot program provides a framework for future cohort‐ and community‐based climate communication programs in the state and beyond. Plain Language Summary: More frequent, effective climate conversations initiated by a diversity of trusted voices can help to increase climate concern and desire for action at the community level. However, in the United States, there is a disconnect between the level of concern individuals have about climate change and the extent to which individuals talk about the issue. To help bridge this gap, the University of Minnesota Climate Adaptation Partnership developed a training program aimed at inspiring and equipping local community members across Greater Minnesota with the skills and confidence to have effective conversations about climate change in their communities. This paper summarizes the programmatic activities we used to support our goals, and some reflections on the program's results. This pilot program provides a framework for future efforts that can be facilitated by Extension programs, community‐based organizations, universities, and others to inspire and accelerate similar community‐centered climate conversations. Key Points: We piloted a training program to teach Minnesotans to effectively communicate about climate change and its impacts on their communitiesParticipants improved behavioral and conversation skills to communicate about climate change more effectivelyThis program provides a framework for future programs aimed at increasing the diversity of people engaging in local climate conversations [ABSTRACT FROM AUTHOR]

In this paper we introduce a spectral surface reflectivity climatology based on observations made by the TROPOMI instrument on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface for 21 wavelength bands ranging from 328nm to 2314nm and for each calendar month. The spatial resolution of the database grid is 0.125° × 0.125°. A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database. Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important. Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1, OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0.002-0.02 in the UV-VIS (below 500nm), up to 0.003 in the NIR (670-772nm), and below 0.001 in the SWIR (2314nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER we made use of comparison with MODIS surface BRDF for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER and we found good agreement with MODIS surface BRDF. The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polarorbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including OLCI on the Sentinel-3 satellites, Sentinel-5 and 3MI on the MetOp-SG-A1 satellite to be launched in 2025, and the future CO2M mission. [ABSTRACT FROM AUTHOR]

Aerosols, both natural and anthropogenic, are an important but complex component of the Earth's climate system. Their net impact on climate is about equal in magnitude to that of greenhouse gases but can vary significantly by region and type. Understanding and quantifying these aerosol effects is critical for accurate climate modeling and for developing strategies to mitigate climate change. In this paper, we utilize AERONET (Aerosol Robotic NETwork) data from 10 stations situated in the dust belt region to characterize aerosol properties essential for climate change assessment. Aerosol optical depth (AOD) data at 500 nm and Ångström exponent (α) data at the pair of wavelengths of 440 and 870 nm (α440-870) in the study region are analyzed to discriminate among different types of aerosols. The annual and monthly variabilities in AODs are analyzed to see the aerosols trend in the study region. In addition, the AOD and α440-870 data are utilized in order to determine different aerosol types during the period of study. Furthermore, the correlation coefficient between AODs and various meteorological parameters (temperature, wind speed, wind direction, relative humidity, and visibility) is analyzed. The results of the study indicate that Tamanrasset (2.49%), KAUST (1.29%), Solar Village (1.67%), and Dalanzadgad (0.64%) indicate an increasing trend, while Cairo (−0.38%), Masdar (−2.31%), Dushanbe (−1.18%), and Lahore (−0.10%) indicate a decreasing trend in AODs during the study period. Similarly, the results of characterizing aerosol types show that the highest percentage of desert dust aerosols (68%), mixed aerosols (86%), and biomass burning aerosols (15%) are found over Tamanrasset, Lahore, and Dalanzadgad AERONET stations. The study revealed a strong correlation between AODs and visibility, a moderate correlation with temperature, and a low correlation with other meteorological parameters (wind speed, wind direction, and relative humidity) in the study region. The results of the study are very encouraging and enhance our confidence in using historical AERONET data to improve our understanding of atmospheric aerosols' characteristics. [ABSTRACT FROM AUTHOR]

Bangladesh has been ranked as one of the world's top countries affected by climate change, particularly in terms of agricultural crop sector. The purpose of this study is to identify spatial and temporal changes and trends in long-term climate at local and national scales, as well as their implications for rice yield. In this study, Modified Mann-Kendall and Sen's slope tests were used to detect significant trends and the magnitude of changes in temperature and rainfall. The temperature and rainfall data observed and recorded at 35 meteorological stations in Bangladesh over 65-years in the time span between the years 1949 and 2013 have been used to detect these changes and trends of variation. The results show that mean annual Tmean, Tmin, and Tmax have increased significantly by 0.13°C, 0.13°C, and 0.13°C/decade, respectively. The most significant increasing trend in seasonal temperatures for the respective Tmean, Tmin, and Tmax was 0.18°C per decade (post-monsoon), 0.18°C/decade (winter), and 0.23°C/decade (post-monsoon), respectively. Furthermore, the mean annual and pre-monsoon rainfall showed a significant increasing trend at a rate of 4.20 mm and 1.35 mm/year, respectively. This paper also evaluates climate variability impacts on three major rice crops, Aus, Aman, and Boro during 1970–2013. The results suggest that crop yield variability can be explained by climate variability during Aus, Aman, and Boro seasons by 33, 25, and 16%, respectively. Maximum temperature significantly affected the Aus and Aman crop yield, whereas rainfall significantly affected all rice crops' yield. This study sheds light on sustainable agriculture in the context of climate change, which all relevant authorities should investigate in order to examine climate-resilient, high-yield crop cultivation. [ABSTRACT FROM AUTHOR]

Broadband (spectrally‐integrated) radiation calculations are dominated by the expense of spectral integration, and many applications require fast parameterizations for computing radiative flux. Here we describe a novel approach using a linear weighted sum of monochromatic calculations at a small set of optimally‐chosen frequencies. The empirically‐optimized quadrature method is used to compute atmospheric boundary fluxes, net flux profiles throughout the atmosphere, heating rate profiles, and top‐of‐the‐atmosphere forcing by CO2, in the longwave for clear skies. We evaluate the method against two modern correlated k‐distribution models and find that we can achieve comparable errors with 32 spectral points. We also examine the effect of minimizing different cost functions, and find that in order to accurately represent heating rates and CO2 forcing, these quantities must be included in the cost function. Plain Language Summary: Quantifying the way radiation flows through the atmosphere is computationally expensive, and most applications require fast approximation. In this paper, we develop an empirically‐optimized quadrature method that can compute quantities of interest in climate science and weather prediction such as radiative flux throughout the atmosphere, heating rates, and forcing by carbon dioxide. The method uses a simple optimization algorithm, combined with a linear model, to achieve accuracy and computational cost of similar order of magnitude to the modern correlated k‐distribution models. Depending on the focus of the application, the algorithm can easily be adapted to prioritize desired quantities. Key Points: Spectrally integrated radiative quantities can be estimated from a small weighted set of monochromatic calculationsTradeoffs between computational cost and accuracy are commensurate with correlated k‐distribution modelsOptimization priorities can be tailored to a specific applications [ABSTRACT FROM AUTHOR]