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Accelerometers are mainly used to measure the non-conservative forces at the center of mass of gravity satellites and are the core payloads of gravity satellites. All kinds of disturbances in the satellite platform and the environment will affect the quality of the accelerometer data. This paper focuses on the quality assessment of accelerometer data from the GRACE-FO satellites. Based on the ACC1A data, we focus on the analysis of accelerometer data anomalies caused by various types of disturbances in the satellite platform and environment, including thruster spikes, peaks, twangs, and magnetic torque disturbances. The data characteristics and data accuracy of the accelerometer in different operational states and satellite observation modes are analyzed using accelerometer observation data from different time periods. Finally, the data consistency of the accelerometer is analyzed using the accelerometer transplantation method. The results show that the amplitude spectral density of three-axis linear acceleration is better than the specified accuracy (above 10−1 Hz) in the accelerometer's nominal status. The results are helpful for understanding the characteristics and data accuracy of GRACE-FO accelerometer observations. [ABSTRACT FROM AUTHOR]

The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the "pattern effect." To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation. Plain Language Summary: Many properties of the atmosphere are affected by the temperature of the ocean surface. Knowing how strong these effects are would help us to better predict global warming. The response to a given surface warming depends on where the warming occurs. To account for this, researchers sometimes simulate the response to individual patches of warming and then assume the response to an arbitrary warming pattern can be summed together from these patch responses. This is sometimes called the atmospheric Green's function method, and it works well at recreating the atmospheric response to historical temperature changes. We are organizing a Green's Function Model Intercomparison Project (GFMIP), in which participants will apply the method consistently for many climate models. This paper presents the GFMIP protocol. In the course of developing this protocol, we found that the atmospheric response to warming is not proportional in all cases: the response to surface warming is not the opposite of the response to surface cooling; warming twice as much doesn't cause twice as much of a response; and making a patch of warming twice as large doesn't cause twice as large a response. GFMIP will help us figure out how to account for this nonlinearity. Key Points: The Green's Function Model Intercomparison Project (GFMIP) explores the atmospheric response to surface temperature patch perturbationsThis paper presents the GFMIP protocol, which was generated using insights from past studies and new sensitivity testsGreen's functions reconstruct the response to historical temperatures, but nonlinearities can affect responses to other warming patterns [ABSTRACT FROM AUTHOR]

Individuals' sensitivity to climate hazards is a central component of their vulnerability to climate change. In this paper, we introduce and outline the utility of a new intraindividual variability construct, affective sensitivity to air pollution (ASAP)–defined as the extent to which an individual's affective states fluctuate in accordance with daily changes in air quality. As such, ASAP pushes beyond examination of differences in individuals' exposures to air pollution to examination of differences in individuals' sensitivities to air pollution. Building on known associations between air pollution exposure and adverse mental health outcomes, we empirically illustrate how application of Bayesian multilevel models to intensive repeated measures data obtained in an experience sampling study (N = 150) over one year can be used to examine whether and how individuals' daily affective states fluctuate with the daily concentrations of outdoor air pollution in their county. Results indicate construct viability, as we found substantial interindividual differences in ASAP for both affect arousal and affect valence. This suggests that repeated measures of individuals' day-to-day affect provides a new way of measuring their sensitivity to climate change. In addition to contributing to discourse around climate vulnerability, the intraindividual variability construct and methodology proposed here can help better integrate affect and mental health in climate adaptation policies, plans, and programs. [ABSTRACT FROM AUTHOR]

Introduction: As an exceptional geographical entity, the vegetation of the Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in the southeastern sector of the QTP, serving as a transition area from sub-tropical evergreen broadleaf forest to high-mountain vegetation. However, there has been limited exploration into predicting the temporal and spatial variability of vegetation cover using anti-interference methods to address outliers in longterm historical data. Additionally, the correlation between these variables and environmental factors in natural forests with complex terrain has rarely been analyzed. Methods: This study has developed an advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional vegetation cover) to accurately evaluate and predict the time and spatial shifts in FVC within the BNNR, utilizing the GEE (Google Earth Engine). The satellite data utilized in this paper consisted of Landsat images spanning from 1986 to2020. By integrating TS and MK methodologies to monitor and assess the FVC trend, the Hurst index was employed to forecast FVC. Furthermore, the association between FVC and topographic factors was evaluated, the partial correlation between FVC and climatic influences was analyzed at the pixel level (30×30m). Results and discussion: Here are the results of this research: (1) Overall, the FVC of the BNNR exhibits a growth trend, with the mean FVC value increasing from 59.40% in 1986 to 68.67% in 2020. (2) The results based on the TS-MK algorithm showed that the percentage of the area of the study area with an increasing and decreasing trend was 59.03% (significant increase of 28.04%) and 22.13% (significant decrease of 6.42%), respectively. The coupling of the Hurst exponent with the Theil-Sen slope estimator suggests that the majority of regions within the BNNR are projected to sustain an upward trend in FVC in the future. (3) Overlaying the outcomes of TS-MK with the terrain factors revealed that the FVC changes were notably influenced by elevation. The partial correlation analysis between climate factors and vegetation changes indicated that temperature exerts a significant influence on vegetation cover, demonstrating a high spatial correlation. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization has caused obstructed urban natural ventilation and the contribution rate of urbanization is relatively high. Therefore, there is an urgent need for urban development planning that should respect natural ventilation and local climate to reduce negative impacts. By optimizing the urban construction layout to reduce obstruction and leave a passageway for wind to blow in, the natural ventilation environment could be improved. This paper presents a promising approach for natural ventilation planning at both the city and community scales. Based on the assessment of wind environment, heat island intensity, and ventilation potential, the results revealed that winds blowing from the western and northern mountainous area of Shijiazhuang play a natural ventilation inlet role which can provide clean air. The SSHI and SHI were mainly distributed within the Second Ring Road, which has a large proportion of the low ventilation potential level. Thus, six first-class ventilation corridors and thirteen secondary corridors were recommended, which were set to be adapted to the dominant wind direction. Subsequently, an urban climate analysis map (UCAnMap) was developed considering climate sensitivity, and planning recommendations were provided for different climate zones. The relationship between architectural spatial structure and ventilation efficiency was analyzed; the results revealed that increasing the height of the buildings will decrease the proportion of comfortable wind zones, and the overall ventilation efficiency will weaken, so the average building height of a typical block should be controlled within 45 m, which matches ventilation performance requirements. The ventilation efficiency of the block has a certain negative correlation with the building density, and as the building density decreased by more than 10%, the proportion of the comfortable wind zones could increase by 4–5%. [ABSTRACT FROM AUTHOR]

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [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]

Simulations of the global dust cycle and its interactions with a changing Earth system are hindered by the empirical nature of dust emission parameterizations in climate models. Here we take a step towards improving global dust cycle simulations by presenting a physically-based dust emission model. The resulting dust flux parameterization depends only on the wind friction speed and the soil's threshold friction speed, and can therefore be readily implemented into climate models. We show that our parameterization's functional form is supported by a compilation of quality-controlled vertical dust flux measurements, and that it better reproduces these measurements than existing parameterizations. Both our theory and measurements indicate that many climate models underestimate the dust flux's sensitivity to soil erodibility. This finding can explain why dust cycle simulations in many models are improved by using an empirical preferential sources function that shifts dust emissions towards the most erodible regions. In fact, implementing our parameterization in a climate model produces even better agreement against aerosol optical depth measurements than simulations that use such a source function. These results indicate that the need to use a source function is at least partially eliminated by the additional physics accounted for by our parameterization. Since soil erodibility is affected by climate changes, our results further suggest that many models have underestimated the climate sensitivity of the global dust cycle. [ABSTRACT FROM AUTHOR]

Climate hazards have only fairly recently been acknowledged as key risk factors for airports. While there is a growing body of research examining specific climate change impacts, there is only limited work that combines this literature with overall climate risk. This paper seeks to address this gap in the literature by investigating and synthesising findings from studies relating to historical airport sensitivity to climate hazards and offering insights on the overall climate risk for the global airport system. With airports increasingly needing to become more "climate-resilient" due to projected changes in global climate, airport planners and decision-makers face challenges in terms of identifying key priority areas for resilience planning and investment. The findings of the paper provide insights into these challenges by examining best-applied practices and current levels of vulnerability. The paper supports the wider inclusion of climate risks as a key factor in airports' planning and operational processes. This will require transforming current management cultures to enhance an airport's operational ability to respond to climate events efficiently and recover quickly in the event of a disruption. [ABSTRACT FROM AUTHOR]

A reference or "no‐feedback" radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan‐Boltzmann law as −4σTe‾3≈−3.8 ${-}4\sigma {\overline{{T}_{e}}}^{3}\approx -3.8$ W m−2 K−1 for Earth's present climate, where Te‾ $\overline{{T}_{e}}$ is a global effective emission temperature. The comparable radiative response in climate models, widely called the "Planck feedback," averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non‐negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan‐Boltzmann feedbacks arise from temperature‐dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long‐overlooked but notable gap in our understanding of Earth's reference radiative response to warming. Plain Language Summary: Earth's climate is stable because a warmer planet loses more energy to space, at infrared wavelengths invisible to the naked eye. The rate of change of this energy loss as the planet warms provides an estimate how Earth's energy balance responds to warming, which is simple enough to write on a small piece of paper. When scientists investigate the warming predicted by climate models, they often start from a similar but not identical calculation of how Earth's energy balance responds to warming. This calculation, based on model output, is about 15% less stabilizing than the simple pencil‐and‐paper estimate. In this paper, we explore the causes of this 15% difference between the pencil‐and‐paper estimate and the calculations using climate models. We show that the difference is primarily caused by the lack of assumed warming in climate models high in Earth's atmosphere, where temperatures are not closely linked to surface warming. This lack of warming acts as a hidden destabilizing feedback in current analysis of climate models. Key Points: Earth's reference radiative response, or "Planck feedback," is ∼0.5 W m−2 K−1 less stabilizing than a Stefan‐Boltzmann estimateWe find this deviation is mostly due to the assumed lack of stratospheric warming in calculations of the Planck feedbackThe lack of stratospheric warming serves as an implicit positive feedback in analysis of climate model warming [ABSTRACT FROM AUTHOR]

Digitization is a driving force for social development and corporate innovation. Digital projects have become an indispensable part of the sustainable development of enterprises. However, due to the imperfect decision-making system of digital projects and the lack of experience of traditional enterprises' digital projects, the decision-making of digital projects is an unavoidable challenge in the digital transformation of enterprises. For the digital project decision of the STATE GRID Corporation of China, this paper conducts a sensitivity analysis of digital project evaluation index weights based on cloud model theory, on top of historical successful project experience to support digital project decision-making. Firstly, this paper establishes a comprehensive evaluation index system for digitalization projects from five aspects: economic efficiency, interconnection, intelligent management, value release, and development innovation. The coefficient of variation method is used for index screening, and the weight intervals are formed by four subjective and objective assignment methods. Then, the LSOM model is established to generate the weight values in the interval, and, finally, the sensitivity of digital project comprehensive evaluation indexes is analyzed based on the cloud model to select the most robust index weights for project evaluation and choose the optimal project. The feasibility of the proposed method is verified by arithmetic examples. [ABSTRACT FROM AUTHOR]

The global hydrologic cycle is likely to increase its strength with global warming. Climate models generally predict an increase in strength of 2.2%K-1, which is much weaker than what would be expected from the increase in saturation vapor pressure of 6.5%K-1. Furthermore, it has been reported that the sensitivity of the hydrologic cycle to surface temperature differences caused by solar radiation is about 50% greater than by an equivalent difference induced by the greenhouse effect. Here we show that these sensitivities can be derived analytically from an extremely simple surface energy balance model that is constrained by the assumption that vertical convective trans port within the atmosphere operates at maximum power. Using current climatic mean conditions, this model predicts a sensitivity of the hydrologic cycle of 2.2%K-1 to surface temperature induced by differences in the greenhouse effect, and a sensitivity of 3.2%K-1 for differences caused by absorbed solar radiation. These sensitivities can be explained by considering the changes in the surface energy balance in which the heating by solar radiation is partitioned equally into radiative and turbulent cooling at a state of maximum power of convective exchange. This explanation emphasizes the different roles that solar and terrestrial radiation play in the surface energy balance and hydrologic cycling that cannot be lumped together into a radiative forcing concept. We illustrate one implication of this explanation for the case of geoengineering, which aims to undo surface temperature differences by solar radiation management, but will nevertheless result in substantial differences in hydrologic cycling due to the difference in sensitivities. We conclude that the overall sensitivity of the hydrologic cycle to surface temperature can be understood and predicted by very simple physical considerations. [ABSTRACT FROM AUTHOR]

The equilibrium climate sensitivity (ECS) is constrained based on observed nearsurface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data. For the net anthropogenic RF the posterior mean in 2010 is 2.1Wm-2 with a 90% credible interval (C.I.) of 1.3 to 2.8Wm-2, excluding present day total aerosol effects (direct+indirect) stronger than -1.7Wm-2. The posterior mean of the ECS is 1.8 °C with 90%C.I. ranging from 0.9 to 3.2°C which is tighter than most previously published estimates. We find that using 3 OHC data sets simultaneously substantially narrows the range in ECS, while using only one set and similar time periods can produce comparable results as previously published estimates including the heavy tail in the probability function. The use of additional 10 yr of data for global mean temperature change and ocean heat content data narrow the probability density function of the ECS. In addition when data only until year 2000 is used the estimated mean of ECS is 20% higher. Explicitly accounting for internal variability widens the 90%C.I. for the ECS by 60 %, while the mean ECS only becomes slightly higher. [ABSTRACT FROM AUTHOR]

Calcium oxalate (CaOx) crystals, ubiquitous in numerous plant families, have emerged as fascinating and complex structures with far-reaching implications in plant physiology, ecology, and human health. This paper encapsulates a comprehensive exploration of CaOx crystals, beginning with their formation and physiological role within plants, to their larger ecological implications, and finally, their impact on human society. Recent research has elucidated key genes involved in CaOx crystal formation, as well as the crystals' diverse physiological roles, including their function in calcium regulation, defense against herbivory, and potential contributions to carbon sequestration. We also highlight the ecological significance of CaOx crystals in nutrient cycling and plant-soil interactions, and the potential hazards posed by these crystals in human nutrition, particularly in the context of oxalate-rich diets and kidney stone formation. Despite significant advances, many aspects, particularly their ecological impacts and potential role in climate change mitigation, remain under-explored, necessitating future research. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the role of interest groups in Poland's energy policy in the context of the EU climate and energy policy. The main aim of the analysis is to try to answer the question of who shapes the Polish energy policy, which stakeholders have the greatest impact on, who are the key players, and what kind of interest intermediation system has developed in the energy sector. Poland's energy mix is dominated by coal, which results from the ownership structure in the energy sector, strong influence of conventional (coal) energy companies and centralization of energy policy. The interests of coal energy companies are protected by the government both in Poland and at the EU level. The research puts forward a thesis that Polish energy policy is co-shaped by conventional energy companies, which leads to their monopolistic position and the formation of a neocorporatist model with elements of statism, and the process of illiberal backlash and creeping authoritarianism further strengthens their role. Privileging the interests of the state-owned energy companies hampers the process of energy transition in Poland, which is contrary to the EU's climate and energy policy. [ABSTRACT FROM AUTHOR]

Crop quality is directly related to national food security and people's living standards, and it is also key to the improvement of agricultural quality and efficiency. According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), climate change has already exerted a negative impact on crop quality. To address climate change scientifically, this paper reviews the latest progress in studies on the impact of climate change on crop quality, and points out limitations of existing studies: (1) Climate factors affecting crop quality are not yet clearly identified; (2) The climate change influencing mechanism and disaster-inducing critical meteorological conditions for crop quality are not clearly established; and (3) No climatic suitability model for crop quality has been constructed to reflect the synergy of multiple climate factors. To ensure food quality and security, and to adjust and optimize the industrial planning of grain crops, promote a high crop quality and yield, and address climate change scientifically, this paper argues that subsequent studies should discuss the following topics, focusing on the climatic suitability of crop quality and resistance mechanisms: (1) changing laws involving the climate sensitivity of crop quality in the context of climate change; (2) response mechanisms of crop quality to climate change; (3) crop quality monitoring and assessment modelling; (4) climatic suitability zoning of crop quality; (5) spatiotemporal evolution trends of crop quality and its responses; and (6) crop quality and its legal measures in the world. [ABSTRACT FROM AUTHOR]

Stratospheric Aerosol Geoengineering (SAG) is one of the solar geoengineering approaches that have been proposed to offset some of the impacts of anthropogenic climate change. Past studies have shown that SAG may have adverse impacts on the global hydrological cycle. Using a climate model, we quantify the sensitivity of the tropical monsoon precipitation to the meridional distribution of volcanic sulfate aerosols prescribed in the stratosphere in terms of the changes in aerosol optical depth (AOD). In our experiments, large changes in summer monsoon precipitation in the tropical monsoon regions are simulated, especially over the Indian region, in association with meridional shifts in the location of the intertropical convergence zone (ITCZ) caused by changes in interhemispheric AOD differences. Based on our simulations, we estimate a sensitivity of − 1.8° ± 0.0° meridional shift in global mean ITCZ and a 6.9 ± 0.4% reduction in northern hemisphere (NH) monsoon index (NHMI; summer monsoon precipitation over NH monsoon regions) per 0.1 interhemispheric AOD difference (NH minus southern hemisphere). We also quantify this sensitivity in terms of interhemispheric differences in effective radiative forcing and interhemispheric temperature differences: 3.5 ± 0.3% change in NHMI per unit (Wm−2) interhemispheric radiative forcing difference and 5.9 ± 0.4% change per unit (°C) interhemispheric temperature difference. Similar sensitivity estimates are also made for the Indian monsoon precipitation. The establishment of the relationship between interhemispheric AOD (or radiative forcing) differences and ITCZ shift as discussed in this paper will further facilitate and simplify our understanding of the effects of SAG on tropical monsoon rainfall. [ABSTRACT FROM AUTHOR]

Models of the carbon cycle and climate on geologic (>104 -year) timescales have improved tremendously in the last 50 years due to parallel advances in our understanding of the Earth system and the increase in computing power to simulate its key processes. Still, balancing the Earth system's complexity with a model's computational expense is a primary challenge in model development. Simulations spanning hundreds of thousands of years or more generally require a reduction in the complexity of the climate system, omitting features such as radiative feedbacks, shifts in atmospheric circulation, and the expansion and decay of ice sheets, which can have profound effects on the long-term carbon cycle. Here, we present a model for climate and the long-term carbon cycle that captures many fundamental features of global climate while retaining the computational efficiency needed to simulate millions of years of time. The Carbon–H 2 O Coupled HydrOlOgical model with Terrestrial Runoff And INsolation, or CH2O-CHOO TRAIN, couples a one-dimensional (latitudinal) moist static energy balance model of climate with a model for rock weathering and the long-term carbon cycle. The CH2O-CHOO TRAIN is capable of running million-year-long simulations in about 30 min on a laptop PC. The key advantages of this framework are (1) it simulates fundamental climate forcings and feedbacks; (2) it accounts for geographic configuration; and (3) it is flexible, equipped to easily add features, change the strength of feedbacks, and prescribe conditions that are often hard-coded or emergent properties of more complex models, such as climate sensitivity and the strength of meridional heat transport. We show how climate variables governing temperature and the water cycle can impact long-term carbon cycling and climate, and we discuss how the magnitude and direction of this impact can depend on boundary conditions like continental geography. This paper outlines the model equations, presents a sensitivity analysis of the climate responses to varied climatic and carbon cycle perturbations, and discusses potential applications and next stops for the CH2O-CHOO TRAIN. [ABSTRACT FROM AUTHOR]

The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties. [ABSTRACT FROM AUTHOR]

Several studies have connected emissions of greenhouse gases to economic and trade data to quantify the causal chain from consumption to emissions and climate change. These studies usually combine data and models originating from different sources, making it difficult to estimate uncertainties in the end results. We estimate uncertainties in economic data, multi-pollutant emission statistics and metric parameters, and use Monte Carlo analysis to quantify contributions to uncertainty and to determine how uncertainty propagates to estimates of global temperature change from regional and sectoral territorial- and consumption-based emissions for the year 2007. We find that the uncertainties are sensitive to the emission allocations, mix of pollutants included, the metric and its time horizon, and the level of aggregation of the results. Uncertainties in the final results are largely dominated by the climate sensitivity and the parameters associated with the warming effects of CO2. The economic data have a relatively small impact on uncertainty at the global and national level, while much higher uncertainties are found at the sectoral level. Our results suggest that consumption-based national emissions are not significantly more uncertain than the corresponding production based emissions, since the largest uncertainties are due to metric and emissions which affect both perspectives equally. The two perspectives exhibit different sectoral uncertainties, due to changes of pollutant compositions. We find global sectoral consumption uncertainties in the range of ±9-±27% using the global temperature potential with a 50 year time horizon, with metric uncertainties dominating. National level uncertainties are similar in both perspectives due to the dominance of CO2 over other pollutants. The consumption emissions of the top 10 emitting regions have a broad uncertainty range of ±9-±25 %, with metric and emissions uncertainties contributing similarly. The Absolute global temperature potential with a 50 year time horizon has much higher uncertainties, with considerable uncertainty overlap for regions and sectors, indicating that the ranking of countries is uncertain. [ABSTRACT FROM AUTHOR]

• Machine learning (ML) techniques were pioneered to analyze non-linear relationships. • Algorithm's predictive power validated ML models, preventing spurious relations. • Recursive feature elimination explored predictor importance. • Presence of non-linear relationships is suggested, advising further investigation. • Acknowledgment of skepticism stresses rigorous analysis for long-term correlations. The Earth's climate is a complex system that can be influenced by various internal and external factors. Previous research has explored the relationship between climate and external forcings such as solar activity and GCR. However, the relations are intricate and intertwined into almost chaotic meteorological measurements. This paper delves deeper into understanding their interactions. GCR flux, Sunspot number (SSN), total solar irradiance (TSI), UV irradiance (UVI), and the Oceanic Niño Index (ONI) are the predictor variables, while total cloud amount (TCA) and low cloud amount (LCA) are the response variables. The analysis begins with standard statistical techniques and continues with multiple regression models including random forest, which is a machine learning (ML) method that can be used for non-linear regression. Subsequently, Recursive Feature Elimination (RFE) is employed to scrutinize the correlation among the predictor parameters. In the ML model, the final 25% of the dataset is held out and tested for validation, so, the predictive power of the algorithm is measured. Both geographical and temporal patterns have been investigated. This study suggests that a non-linear relationship might exist between the parameters, and should be investigated further, particularly in specific regions of the world. [ABSTRACT FROM AUTHOR]

Many of the implications of climate change for Aotearoa (New Zealand) remain unclear. To identify so-far unseen or understudied threats and opportunities related to climate change we applied a horizon-scanning process. First, we collated 171 threats and opportunities across our diverse fields of research. We then scored each item for novelty and potential impact and finally reduced the list to ten threats and ten opportunities through a prioritisation process. Within the 20 items presented in this paper, we uncover a range of climate-related costs and benefits. Unexpected opportunities evolve from economic reorganisation and changes to perspectives. The threats we highlight include the overall failure to interconnect siloed policy responses, as well as those relating to extreme events and feedbacks, as well as pressures that undermine the coherence of society. A major theme of our work is that climate change effects in Aotearoa are likely to transgress the boundaries of research disciplines, industry sectors and policy systems, emphasising the importance of developing transdisciplinary methods and approaches. We use this insight to connect potential responses to climate change with Aotearoa's culture and geography. [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

Infeasible solutions or negative expected values of future climate information are undesired problems if climate policies are adopted under Cost-Effectiveness Analysis (CEA) to reach uncertain temperature targets. Cost-Risk Analysis (CRA) was developed to resolve these issues. It allows for a trade-off between expected welfare losses of mitigation and avoided risk of transgressing a climate target with a certain probability of compliance (Safety). Some of the significant contributions of this paper are: (i) It updates the Probabilistic Integrated model of Climate and the Economy (PRICE) as a probabilistic version of the latest version of the Dynamic Integrated Climate-Economy model (DICE) 2016, and it extends the model to run welfare-maximizing decision analytic frameworks readily. (ii) It highlights that the standard method of applying CRA (Old CRA) leads to an extra welfare cost. (iii) It proposes revised instruction on how to use CRA. (iv) It simulates and compares welfare-maximizing decision analytic frameworks on the level of risk, damages, and carbon prices. (v) It measures the value of information using risk-based methods and compares them with the value of information calculated using the damage-based method. (vi) It measures the carbon prices for the CRA scenarios for the first time. The results show that the choice of the disutility function governs the magnitude of the value of information. Using a damage function or Old CRA, the value of information is significantly high for new information arriving between 2020 and 2060. If the New CRA is applied, however, such benefits are negligible. [ABSTRACT FROM AUTHOR]

Achieving universal access to clean cooking requires a significant mobilization of capital to close the current funding gap of around US$7 bn per year. The clean cooking landscape has changed considerably with substantial innovation in terms of technology, business models, and services. The transition towards higher-tier, modern energy cooking (MEC) solutions provides key opportunities for innovative financing models to scale MEC globally. Transitions from cooking with polluting fuels to MEC have significant positive impacts on the environment, gender equality, and health. Impact Finance to monetize these co-benefits for MEC solutions is widely seen as an outstanding opportunity to channel funding into MEC transitions. However, except for climate funding, opportunities to channel finance for wider impact SDG benefits arising from MEC have proved challenging to realize in practice. This article explores in detail two new approaches which are taking advantage of features of digital technology to overcome some of these obstacles. It adds to the recent debate around climate finance for clean cooking and presents key learning lessons from developing and piloting the 'Metered Methodology for Clean Cooking Devices' as the current most accurate approach to estimate carbon savings for MEC and the 'Clean Impact Bond (CIB)' which aims at monetizing health and gender-co-benefits. The paper demonstrates how robust methodologies can help to accelerate funding for MEC and calls for joint approaches to standardize and streamline climate and outcome finance approaches to enhance their impact by making them more accessible for a wider range of MEC technologies, geographies, and projects. [ABSTRACT FROM AUTHOR]

As an instrument to minimize carbon leakage, the effects and feasibility of Carbon Border Adjustments Mechanism (CBAM) will depend on multiple design options. While the EU has committed to introducing CBAM as part of its green climate deal, pursuing climate efforts to successfully limit global warming requires a collective implementation involving major emitters China and the US. This paper quantifies the distributional impacts of a joint CBAM implementation of in a climate alliance or a club of the EU, the US, and China. Differing from a myriad of studies that focus on unilateral CBAM, this analysis emphasizes collective implications on leakage, sectoral competitiveness, and welfare by projecting climate neutrality relative to current policies and climate targets. Our findings confirm that coalition reduces leakage, improves production on energy-intensive industries, and increases club's welfare relative to a non-CBAM and a unilateral implementation. These are in contrast with some unilateral analytical studies, especially for the US. It is further proof of the potential of CBAM as collective instruments to facilitate mitigation and trade competitiveness. [ABSTRACT FROM AUTHOR]

Rural commons face extraordinary challenges like fragility and sensitivity due to climate change. Retrofitting rural built environments affords benefits that could overcome these challenges and support sustainable development. However, notwithstanding the vast energy retrofitting interventions available, the associated aspects require investigation, particularly in distinct rural contexts with all their valuable, cultural, and historical inheritance. Hence, this study aimed to examine energy retrofitting practices in rural settlements worldwide over a decade to diagnose the goals that are being undertaken, stakeholder engagement, and finally, the bi-correlation between rural contexts and interventions, and retrofitting contributions to valorizing the place's identity. This study is a systematic literature review (SLR) considering the items of the PRISMA checklist (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). An SLR of published peer-reviewed studies between January 2012 and March 2023 in 16 electronic databases in all available languages, using a combination of seven keywords within three domains, was conducted. The initial search resulted in 397; after applying the inclusion/exclusion criteria, there were 60 eligible articles. The academic progress and tendencies in the energy retrofitting domain of rural built environments are discussed and summarized into four major thematic classifications (energy efficiency strategies, energy efficiency planning, policy evaluation, and occupant behavior). Briefly, rural buildings lack energy-saving designs. Simulation tools are essential; however, they should be calibrated with on-site conditions, showing the reasons for selecting the applied retrofitting measures and correlation with the surrounding context. Successful implementation requires cross-disciplinary collaboration, engaging decision makers, and providing energy education for the local community. Regulations should include micro-context-specific environmental performance indicators. These insights could help map out future academic pursuits and help the stakeholders better understand their nature. Simultaneously, this study assists early-stage researchers in conducting systematic literature reviews utilizing different tools. However, the SLR protocol may have limited findings due to the specific search terms used, so the authors believe the more the literature search scope is broadened, the more discoveries could be made. [ABSTRACT FROM AUTHOR]

Radiative sensitivity, i.e., the response of the radiative flux to climate perturbations, is essential to understanding climate change and variability. The sensitivity kernels computed by radiative transfer models have been broadly used for assessing the climate forcing and feedbacks for global warming. As these assessments are largely focused on the top of atmosphere (TOA) radiation budget, less attention has been paid to the surface radiation budget or the associated surface radiative sensitivity kernels. Based on the fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), we produce a new set of radiative kernels for both the TOA and surface radiative fluxes, which is made available at 10.17632/vmg3s67568 (Huang and Huang, 2023). By comparing these with other published radiative kernels, we find that the TOA kernels are generally in agreement in terms of global mean radiative sensitivity and analyzed overall feedback strength. The unexplained residual in the radiation closure tests is found to be generally within 10 % of the total feedback, no matter which kernel dataset is used. The uncertainty in the TOA feedbacks caused by inter-kernel differences, as measured by the standard deviation of the global mean feedback parameter value, is much smaller than the inter-climate model spread of the feedback values. However, we find relatively larger discrepancies in the surface kernels. The newly generated ERA5 kernel outperforms many other datasets in closing the surface energy budget, achieving a radiation closure comparable to the TOA feedback decomposition, which confirms the validity of the kernel method for the surface radiation budget analysis. In addition, by investigating the ERA5 kernel values computed from the atmospheric states of different years, we notice some apparent interannual differences, which demonstrates the dependence of radiative sensitivities on the mean climate state and partly explains the inter-dataset kernel value differences. In this paper, we provide a detailed description of how ERA5 kernels are generated and considerations to ensure proper use of them in feedback quantifications. [ABSTRACT FROM AUTHOR]

The emergence of 100‐Kyr glacial cycles (The Mid‐Pleistocene Transition [MPT]) is attributed in part to slower global overturning circulation and iron stimulation of biological carbon drawdown in the Southern Ocean. We present foraminifera‐bound nitrogen isotope values and polar planktic foraminifera abundances from the Agulhas Plateau that show that increases in biogenic sediment accumulation coincide with northward migrations of the Subtropical Frontal Zone (STFZ) and elevated foraminifera‐bound nitrogen isotope values during MPT glacial episodes. The nitrogen isotope values of two planktic foraminifera species, Globigerina bulloides and Globorotalia inflata show remarkable coherence amongst the sea surface temperature gradient between the STFZ and SAZ, and polar foraminifera abundances, indicating a strong relationship between nitrogen isotope dynamics above the Agulhas Plateau and migrations of the STFZ. Northward migration of the STFZ may have been essential to prolonging glacial intervals by increasing deep ocean carbon storage via a northward shift of the South Westerly Winds and a reduction in upwelling, delivery of fresher surface waters into the upper limb of global overturning circulation, or inhibiting heat and salt delivery to the Atlantic as Agulhas Leakage. Plain Language Summary: The Mid‐Pleistocene Transition (MPT) is a unique period of time, during which the natural rhythm of Earth's climate shifted, the pace of glacial and interglacial cycles changed from 40,000 years to 100,000 years as ice sheets expanded, carbon accumulated in the deep ocean, and the planet cooled. The exact cause of these changes is unclear, but internal system feedbacks were likely at play. As climate changes today and the oceans increasingly take up carbon, the MPT provides a unique case study to investigate Earth's natural climate system and important carbon cycle feedbacks. In this paper we use the nitrogen isotopic composition of plankton shells and other biogeochemical records to show that leading up to the MPT the Southern Ocean underwent major physical changes and expanded as 100,000 years glacial cycles emerged. Our data indicates that the expansion of the Southern Ocean delivered cold, nutrient rich waters northward where they could mix with warm, salty Indian and Atlantic Ocean waters as hypothesized by other studies. Key Points: Foraminifera‐bound nitrogen isotope values from the Agulhas Plateau record latitudinal migrations of the Subtropical FrontElevated foraminifera‐bound nitrogen isotope values after 900 ka are consistent with Southern sourced nitrateSouthern Ocean contraction during a "super interglacial" brings more subtropical nitrate to the Agulhas Plateau [ABSTRACT FROM AUTHOR]

Climate change impacts on fishery resources have been widely reported worldwide. Nevertheless, a knowledge gap remains for the warm-temperate Southwest Atlantic Ocean—a global warming hotspot that sustains important industrial and small-scale fisheries. By combining a trait-based framework and long-term landing records, we assessed species' sensitivity to climate change and potential changes in the distribution of important fishery resources (n = 28; i.e., bony fishes, chondrichthyans, crustaceans, and mollusks) in Southern Brazil, Uruguay, and the northern shelf of Argentina. Most species showed moderate or high sensitivity, with mollusks (e.g., sedentary bivalves and snails) being the group with the highest sensitivity, followed by chondrichthyans. Bony fishes showed low and moderate sensitivities, while crustacean sensitivities were species-specific. The stock and/or conservation status overall contributed the most to higher sensitivity. Between 1989 and 2019, species with low and moderate sensitivity dominated regional landings, regardless of the jurisdiction analyzed. A considerable fraction of these landings consisted of species scoring high or very high on an indicator for potential to change their current distribution. These results suggest that although the bulk of past landings were from relatively climate-resilient species, future catches and even entire benthic fisheries may be jeopardized because (1) some exploited species showed high or very high sensitivities and (2) the increase in the relative representation of landings in species whose distribution may change. This paper provides novel results and insights relevant for fisheries management from a region where the effects of climate change have been overlooked, and which lacks a coordinated governance system for climate-resilient fisheries. [ABSTRACT FROM AUTHOR]

We describe the approach taken to develop the United Kingdom's first community Earth system model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the U.K. academic community. We document our model development procedure and the subsequent U.K. submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well‐established, world‐leading HadGEM models of the physical climate system and incorporates cutting‐edge new representations of aerosols, atmospheric chemistry, terrestrial carbon, and nitrogen cycles and an advanced model of ocean biogeochemistry. A high‐level metric of overall performance shows that both models, HadGEM3‐GC3.1 and UKESM1, perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3‐GC3.1 and UKESM1 include the emergent climate sensitivity (5.5 and 5.4 K, respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud‐aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role, is highlighted in other papers in this special issue. We place our findings within the broader modeling landscape indicating how our understanding of key processes driving higher sensitivity in the two U.K. models seems to align with results from a number of other CMIP6 models. Plain Language Summary: The United Kingdom has taken a community approach to model development and delivery of simulations to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The papers in this special issue characterize and evaluate the U.K. models and highlight emerging properties of the models, such as the climate sensitivity. Understanding of the reasons for an increase in sensitivity in these new models is highlighted here, and similarities to our findings from other modeling centers are discussed. Key Points: The United Kingdom has developed its first community Earth system model and delivered a traceable hierarchy of models to CMIP6We applied a process‐based evaluation strategy in model development but chose not to use historic trends or measures of climate responseThe U.K. models exhibit higher climate sensitivity than seen in CMIP5 arising in part from more positive cloud feedbacks [ABSTRACT FROM AUTHOR]

This paper evaluates the relation between rockfall events and weather conditions for two regions in Norway – Romsdalen and Gudbrandsdalen and explores how rockfall frequency might change with future climate conditions. Our analysis focuses on understanding the relationship between rockfall occurrence and effective water inputs, including rainfall and snow melt, and temperature oscillations both in cold conditions (freeze-thaw cycles) and in warm conditions (hot-cold cycles). To accomplish this, regional weather data and rockfall information in the Norwegian Mass Movement Database have been employed. Our results indicate that temperature oscillations might be better suited than effective water input to depict the occurrence of rockfalls in the two study areas in Norway. Freeze-thaw cycles are most frequent during winter and spring, and hot-cold cycles are most frequent during summer. Climate change will affect rockfall seasonality and the frequency in which freeze-thaw cycles and hot-cold cycles are observed. Thus, altering the exposure of population and infrastructures to rockfalls. [ABSTRACT FROM AUTHOR]

Old plant communities are of utmost importance for nature conservation, carbon sequestration, as well as gene pool maintenance. Shrub populations occurring in extreme environments beyond abiotic tree-lines provide diverse ecosystem services and have potential as proxy archives because they often inhabit areas with scarce and short instrumental records. We provide dendrochronological insight into one such population made up of prostrate Juniperus communis shrubs in the northern Scandinavian tundra (Norway), growing on a competition-free boulder field. We dated and provided a growth chronology of the oldest living shrub population (190 ± 148 years) from Scandinavia with the oldest individual being 683 years old. This is a conservative estimate, as shrubs in extreme environments do not form even a single row of cells in some years. The cross-dating issues of poorly growing shrubs do not fully allow to rely on climate sensitivity of the juniper population studied, although the species' potential for dendroclimatological reconstructions is generally considered high, also because the common juniper is an abundant woody species distributed globally. Old populations present an important gene source in plant recruitment, particularly in the context of the present environmental change. [ABSTRACT FROM AUTHOR]

Designing effective spatial management strategies is challenging because marine ecosystems are highly dynamic and opaque, and extractive entities such as fishing fleets respond endogenously to ecosystem changes in ways that depend on ecological and policy context. We present a modelling framework, marlin, that can be used to efficiently simulate the bio‐economic dynamics of marine systems in support of both management and research. We demonstrate marlin's capabilities by focusing on two case studies on the conservation and food production impacts of marine protected areas (MPAs): a coastal coral reef and a pelagic tuna fishery. In the coastal coral reef example, we explore how heterogeneity in species distributions and fleet preferences can affect distributional outcomes of MPAs. In the pelagic case study, we show how our model can be used to assess the climate resilience of different MPA design strategies, as well as the climate sensitivity of different fishing fleets. This paper demonstrates how intermediate complexity simulation of coupled bio‐economic dynamics can help communities predict and potentially manage trade‐offs among conservation, fisheries yields and distributional outcomes of management policies affected by spatial bio‐economic dynamics. [ABSTRACT FROM AUTHOR]

Non-equilibrium is dominant in geophysical and climate phenomena. However the study of non-equilibrium is much more difficult than equilibrium, and the relevance of probabilistic simplified models has been emphasized. Large deviation rates have been used recently in climate science. In this paper, after recalling progress during the last decades in understanding the role of large deviations in a class of non-equilibrium systems, we point out differences between equilibrium and non-equilibrium. For example, in non-equilibrium (a) large deviation rates may be extensive but not simply additive. (b) In non-equilibrium there are generically long-range space correlations, so large deviation rates are non-local. (c) Singularities in large deviation rates denote the existence of phase transitions often not possible in equilibrium. To exemplify, we shall refer to lattice gas models like the symmetric simple exclusion process and other models which are playing an important role in the understanding of non-equilibrium physics. The reasons why all this may be of interest in climate physics will be briefly indicated. [ABSTRACT FROM AUTHOR]

Warming climate and precipitation changes induce notable shifts in climate zones. In this study, the latest generation of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the previous generation CMIP5 under high‐emission scenarios are used together with observations and applied to the Köppen‐Geiger climate classification. The aim is to shed light on how projected warming and changes in precipitation will influence future climate zones and their associated ecosystems while revealing differences between the two model generations. Compared to CMIP5 models, CMIP6 models exhibit slightly improved performance in replicating the observed Köppen‐Geiger map for the historical (1976–2005) period and similar inter‐model agreement for the future. The models show major changes in climate zones with a range of projections depending on which ensemble subset is used: 37.9%–48.1% of the global land area is projected to change climate zone by the end of the century, with the most pronounced changes expected over Europe (71.4%–88.6%) and North America (51.2%–65.8%). CMIP6 models project a higher rate of areal climate zone change (km2/year) throughout the 21st century, which is mainly driven by their greater global land warming rates. Using a likely equilibrium climate sensitivity subset of CMIP6 models that is consistent with the latest evidence constrains the climate zone shifts, and their projections better match the results of CMIP5 simulations. Although the high warming rates of some CMIP6 models are less credible, the risks associated with them are greater, and they heighten the need for urgent action to preserve terrestrial ecosystems. Plain Language Summary: Köppen‐Geiger climate classification is a tool to map regional climate zones based on a region's temperature and precipitation. In recent decades, increasing temperatures and changing precipitation trends have started to affect the distribution of those climate zones, and more extensive changes are expected throughout the 21st century. In this paper, we assess the observed and expected changes in the global distribution of Köppen‐Geiger climate zones using observations along with the latest and the previous generation of global climate model projections. We find that shifts in climate zones are more pronounced in the latest generation of models due to their questionable greater global warming rate projections. Depending on which subset of model projections is used, up to half of the Earth's land area faces the risk of shifting to a different climate zone by the end of the century, with the greatest changes expected in Europe and North America. The rate of change (affected area per year) is also projected to accelerate through the 21st century, suggesting that vulnerable species and agricultural practices might have less time to adapt to changes in climate zones than previously projected. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibits slightly improved performance in simulating the globally observed historical Köppen‐Geiger climate zones compared to CMIP5By the end of the century, 38%–48% of the global land area is projected to be in a different climate zone than todayThe rate of climate zone change is projected to accelerate, with more pronounced rates in CMIP6 due to considerably higher warming rates [ABSTRACT FROM AUTHOR]

Dayaratna et al. (Environ Econ Policy Stud 22:433–448, 2020) proposes several improvements to economic- and climate-simulating IAMs and to their input variables. They show that an empirically based ECS estimate, agricultural damage estimates from recent literature, and discount rates as low as 2.5%, when used within the FUND model, yield a negative social cost of carbon through mid-twenty-first century. Five of their propositions, that "fat-tails" of the ECS distribution are improperly simulated by IAMS, FUND does not simulate enough benefits for agriculture, the ECS is near 1.5 °C, DICE and PAGE overestimate the SCC, and the marginal cost of public funds for carbon taxes has a value of at least a few dollars, are more closely analyzed. Estimates of ECS from a recent paper and a 5% discount rate applied to FUND result in an SCC estimated to lie within the range of about −$1 to $2.50 per metric ton CO2 for the year 2020. [ABSTRACT FROM AUTHOR]

Agriculture plays an important role in Senegalese economy and annual early warning predictions of crop yields are highly relevant in the context of climate change. In this study, we used three main machine learning methods (support vector machine, random forest, neural network) and one multiple linear regression method, namely Least Absolute Shrinkage and Selection Operator (LASSO), to predict yields of the main food staple crops (peanut, maize, millet and sorghum) in 24 departments of Senegal. Three combination of predictors (climate data, vegetation data or a combination of both) are used to compare the respective contribution of statistical methods and inputs in the predictive skill. Our results showed that the combination of climate and vegetation with the machine learning methods gives the best performance. The best prediction skill is obtained for peanut yield likely due to its high sensitivity to interannual climate variability. Although more research is needed to integrate the results of this study into an operational framework, this paper provides evidence of the promising performance machine learning methods. The development and operationalization of such prediction and their integration into operational early warning systems could increase resilience of Senegal to climate change and contribute to food security. [ABSTRACT FROM AUTHOR]

The eight papers in this issue of I Photochemical & Photobiological Sciences i are authored by members and co-authors of the Environmental Effects Assessment Panel (EEAP) of the Montreal Protocol under the United Nations Environment Programme. In that first Assessment, Martyn emphasised the importance of evaluating the ways in which biological systems and the environment were being affected by depletion of stratospheric ozone and associated enhanced UV-B (280-315 nm) radiation at the Earth's surface. These papers assess the linkages between stratospheric ozone depletion and UV radiation, and climate change, with respect to the effects on the environment and human health. [Extracted from the article]

This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average - 0.09 W m -2 with a standard deviation due to interannual variability of 0.25 W m -2 , showing no significant drift. The global surface air temperature in the simulation is on average 14.08 ∘ C with an interannual standard deviation of 0.17 ∘ C, exhibiting a small drift of 0.015 ± 0.005 ∘ C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 ∘ C, and its transient climate response is estimated at 2.1 ∘ C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995–2014 has an average bias of - 0.86 ± 0.05 ∘ C with a standard deviation across ensemble members of 0.35 ∘ C in the Northern Hemisphere and 1.29 ± 0.02 ∘ C with a corresponding standard deviation of 0.05 ∘ C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091–2100) of 4.9 ∘ C above the preindustrial mean. A 0.5 ∘ C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 ∘ C. [ABSTRACT FROM AUTHOR]

Climate warming at high latitudes has contributed to the growing interest in shrub tree-ring analysis. Shrub architecture presents new challenges for dendrochronology, such as the seemingly lower and inconsistent climatic sensitivity of stems vs. root collars. Shrub stems may thus be considered as sub-optimal to study climate–growth relationships. In this paper, we propose that the lower climatic sensitivity of stems could be caused by the use of unsuitable detrending methods for chronologies spanning decades rather than centuries. We hypothesize that the conversion of the ring width (RW) to basal area increment (BAI) is better suited than traditional detrending methods to removing age/size-related trends without removing multi-decadal climate signals. Using stem and root collar samples collected from three sites in the forest–tundra ecotone of eastern Canada, we compared the climate–growth relationships of these two approaches for stems and root collars using mixed-effects models. The climate sensitivity was, on average, 4.9 and 2.7 times higher with BAI than with detrended (mean-centered) RW chronologies for stems and root collars, respectively. The climatic drivers of radial growth were identical for stems and root collars when using BAI (July temperature and March precipitation), but were inconsistent when using detrended RW series (root collars: July temperature and March precipitation at all sites; stems: April and June temperature, depending on the site). Although the use of BAI showed promising results for studying long-term climate signals in shrub growth chronologies, further studies focusing on different species and locations are needed before the use of BAI can become broadly used in shrub dendrochronology. [ABSTRACT FROM AUTHOR]

Quantification of rainfall intermittency via. interevent time distribution, series of continuous wet spells (burst size) and variability in interevent times between rainfall events is essential for planning and management of water resources and hydrologic extremes. However, their structure, quantification and association with long-term climatology are less explored. In this paper, a complex system-based measure – burstiness – is used to quantify the variability of interevent times across six meteorologically homogenous zones of India. It is observed that burstiness is related to the burst size as well as long-term rainfall climatology. The existence of unimodal and bimodal structure in burstiness distribution reveals the uniqueness and differences in the rainfall patterns. The differences in sensitivity of rainfall to burstiness highlight the role of interplay between climate-landscape and reveals the importance of the intermittent structure of rainfall. The study provides an approach to model intermittency by preserving the temporal structure of the interevent time distribution. [ABSTRACT FROM AUTHOR]

Intermittent and ephemeral streams in dryland environments support diverse assemblages of aquatic and terrestrial life. Understanding when and where water flows provide insights into the availability of water, its response to external controlling factors, and potential sensitivity to climate change and a host of human activities. Knowledge regarding the timing of drying/wetting cycles can also be useful to map critical habitats for species and ecosystems that rely on these temporary water sources. However, identifying the locations and monitoring the timing of streamflow and channel sediment moisture remains a challenging endeavor. In this paper, we analyzed daily conductivity from 37 sensors distributed along 10 streams across an arid mountain front in Arizona (United States) to assess spatiotemporal patterns in flow permanence, defined as the timing and extent of water in streams. Conductivity sensors provide information on surface flow and sediment moisture, supporting a stream classification based on seasonal flow dynamics. Our results provide insight into flow responses to seasonal rainfall, highlighting stream reaches very reactive to rainfall versus those demonstrating more stable streamflow. The strength of stream responses to precipitation are explored in the context of surficial geology. In summary, conductivity data can be used to map potential stream habitat for water‐dependent species in both space and time, while also providing the basis upon which sensitivity to ongoing climate change can be evaluated. [ABSTRACT FROM AUTHOR]

Energy policy investments are usually evaluated using a cost-benefit analysis (CBA), which requires an estimation of the social discount rate (SDR). The choice of SDR can be crucial for the outcome of the appraisal, as energy-related investments generate long-term impacts affecting climate change. Once discounted, these impacts are highly sensitive to slight changes in the value of the SDR. Some countries (the UK and France) switched from a constant SDR to the declining rate scheme—a solution that limits the impact sensitivity. To our knowledge, none of the CEE countries apply DDR in CBA. While a constant SDR is a relatively well-established approach, declining SDRs are estimated to be used much less frequently, particularly for CEE EU member countries and energy policies. The rationale for the decline can rest on uncertainty over future discount rates, as shown by the approach developed by Weitzman and Gollier, which extends the classical Ramsey model. We applied this approach in our paper, as the Ramsey formula is the prevailing formula for EU countries' SDR estimates. We estimated a flat SDR via the Ramsey formula with Gollier's "precautionary term", and next, we calculated Weitzman's certainty equivalent rates for the 500-year horizon. Ramsey's SDRs, obtained using consumption growth rates dating back to 1996, varied between 6.77% for Lithuania and 2.95% for Czechia and declined by 0.15% on average (Gollier's term). Declining SDRs for the longest horizon dropped to approx. 0.5% (from 0.35% for Bulgaria to 0.67% for Poland), and the descent is deeper and faster when forward SDRs (following the UK Green Book approach) were considered (0.01% to 0.04%). The results are important for long-term policies regarding energy and climate change in CEE EU member countries, but they are still dependent on fossil fuels and experience an investment gap to fulfil EU climate goals. [ABSTRACT FROM AUTHOR]

Climate response metrics are used to quantify the Earth's climate response to anthropogenic changes of atmospheric CO2. Equilibrium climate sensitivity (ECS) is one suchmetric thatmeasures the equilibrium response to CO2 doubling. However, both in their estimation and their usage, such metrics make assumptions on the linearity of climate response, although it is known that, especially for larger forcing levels, response can be nonlinear. Such nonlinear responses may become visible immediately in response to a larger perturbation, or may only become apparent after a long transient period. In this paper, we illustrate some potential problems and caveats when estimating ECS from transient simulations. We highlight ways that very slow time scales may lead to poor estimation of ECS even if there is seemingly good fit to linear response over moderate time scales. Moreover, such slow processes might lead to late abrupt responses (late tipping points) associated with a system's nonlinearities.We illustrate these ideas using simulations on a global energy balance model with dynamic albedo. We also discuss the implications for estimating ECS for global climate models, highlighting that it is likely to remain difficult to make definitive statements about the simulation times needed to reach an equilibrium. [ABSTRACT FROM AUTHOR]

How Earth's climate reacts to anthropogenic forcing is one of the most burning questions faced by today's scientific community. A leading source of uncertainty in estimating this sensitivity is related to the response of clouds. Under the canonical climate-change perspective of forcings and feedbacks, the effect of anthropogenic aerosols on clouds is categorized under the forcing component, while the modifications of the radiative properties of clouds due to climate change are considered in the feedback component. Each of these components contributes the largest portion of uncertainty to its relevant category and is largely studied separately from the other. In this paper, using idealized cloud-resolving radiative–convective-equilibrium simulations, with a slab ocean model, we show that aerosol–cloud interactions could affect cloud feedback. Specifically, we show that equilibrium climate sensitivity increases under high aerosol concentration due to an increase in the short-wave cloud feedback. The short-wave cloud feedback is enhanced under high-aerosol conditions due to a stronger increase in the precipitation efficiency with warming, which can be explained by higher sensitivity of the droplet size and the cloud water content to the CO 2 concentration rise. These results indicate a possible connection between cloud feedback and aerosol–cloud interactions. [ABSTRACT FROM AUTHOR]

By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol. Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the Δh parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries. [ABSTRACT FROM AUTHOR]