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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]

The carbon budget concept states that the global mean temperature (GMT) increase is roughly linearly dependent on cumulative emissions of CO 2. The proportionality is measured as the transient climate response to cumulative emissions of carbon dioxide (TCRE). In this paper, the deviations of the carbon budget from the strict linear relationship implied by the TCRE are examined through the lens of a temperature response to an emission pulse (i.e., pulse response) and its relationship with a nonlinear TCRE. Hereby, two sources of deviation are distinguished: emission scenario and climate state dependence. The former stems from the scenario choice, i.e., the specific emission pathway for a given level of cumulative emissions and the latter from the change in TCRE with changing climatic conditions. Previous literature argues for scenario independence using a stylized set of emission scenarios, and offers a way to fit a nonlinear carbon budget equation. This paper shows how the pulse response, viewed as a Green's function, gives a unifying perspective on both scenario and state dependence. Moreover, it provides an optimization program that tests the scenario independence under the full range of emission pathways for a given set of constraints. In a setup chosen in this paper, the deviations stemming from emission pathway choices are less than 10 % of the overall temperature increase and gradually diminish. Moreover, using the pulse response as a Green's function, the scenario-dependent effects of a reduced-complexity climate model were replicated to a high degree, confirming that the behavior of scenario-dependent deviations can be explained and predicted by the shape of the pulse response. Additionally, it is shown that the pulse response changes with climatic conditions, through which the carbon budget state dependency is explained. Using a pulse response as an approximation for a state-dependent TCRE, an alternative method to derive a nonlinear carbon budget equation is provided. Finally, it is shown how different calibrations of a model can lead to different degrees of carbon budget nonlinearities. The analysis is done using FaIRv2.0.0, a simple climate emulator model that includes climate feedback modifying the carbon cycle, along with a one-box model used for comparison purposes. The Green's function approach can be used to diagnose both models' carbon budget scenario dependency, paving the way for future investigations and applications with other and more complex models. [ABSTRACT FROM AUTHOR]

Radical and quick transformations towards sustainability will be fundamental to achieving a more sustainable future. However, deliberate interventions to reconfigure systems will result in winners and losers, with the potential for greater or lesser equity and justice outcomes. Positive tipping points (PTPs) have been proposed as interventions in complex systems with the aim to (a) reduce the likelihood of negative Earth system tipping points and/or (b) increase the likelihood of achieving just social foundations. However, many narratives around PTPs often do not take into account the entire spectrum of impacts the proposed alternatives could have or still rely on narratives that maintain current unsustainable behaviours and marginalize many people (i.e. do not take "b" into account). One such example is the move from petrol-based to electric vehicles. An energy transition that remains based on natural resource inputs from the Global South must be unpacked with an equity and justice lens to understand the true cost of this transition. There are two arguments why a critical engagement with these and other similar proposals needs to be made. First, the idea of transitioning through a substitution (e.g. of fuel) while maintaining the system structure (e.g. of private vehicles) may not necessarily be conceived as the kind of radical transformation being called for by global scientific bodies like the Intergovernmental Panel on Climate Change (IPCC) and Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). Second, and probably more importantly, the question of positive for whom, positive where, and positive how must be considered. In this paper, we unpack these narratives using a critical decolonial view from the south and outline their implications for the concept of tipping points. [ABSTRACT FROM AUTHOR]

This paper develops a method for determining whether two vector time series originate from a common stochastic process. The stochastic process considered incorporates both serial correlations and multivariate annual cycles. Specifically, the process is modeled as a vector autoregressive model with periodic forcing, referred to as a VARX model (where X stands for exogenous variables). The hypothesis that two VARX models share the same parameters is tested using the likelihood ratio method. The resulting test can be further decomposed into a series of tests to assess whether disparities in the VARX models stem from differences in noise parameters, autoregressive parameters, or annual cycle parameters. A comprehensive procedure for compressing discrepancies between VARX models into a minimal number of components is developed based on discriminant analysis. Using this method, the realism of climate model simulations of monthly mean North Atlantic sea surface temperatures is assessed. As expected, different simulations from the same climate model cannot be distinguished stochastically. Similarly, observations from different periods cannot be distinguished. However, every climate model differs stochastically from observations. Furthermore, each climate model differs stochastically from every other model, except when they originate from the same center. In essence, each climate model possesses a distinct fingerprint that sets it apart stochastically from both observations and models developed by other research centers. The primary factor contributing to these differences is the difference in annual cycles. The difference in annual cycles is often dominated by a single component, which can be extracted and illustrated using discriminant analysis. [ABSTRACT FROM AUTHOR]

Based on a review of existing research on adaptive capacity, we identify a research gap in theorizing institutions, power structures, and agency in municipal climate change adaptation processes. Drawing on sociological institutionalism, governmentality, and communicative planning theory, we use post-structuralist concepts of power to to elucidate the collective (de-)mobilization of existing stocks of capacities within municipal institutions of adaptation with a focus on structural power and agency in participation processes. The concept of reflexive capacity is introduced as the ability of organizations such as municipal administrations to incorporate diverse stakeholders and knowledge into decision-making processes in a local context, which is derived from the relationship of power with with power over. The emergence and transformation of reflexive capacity are illustrated and discussed with one case study municipality in Germany, revealing the potential of this concept for the analysis of participation in adaptation processes and the power structures that are inherent to them. In the paper, we incorporate the concept of reflexive capacity with established concepts of adaptive capacity, creating an integrated framework termed institutional adaptive capacity. The analysis concludes that examining power structures and agency in the context of climate change adaptation explains how capacity stocks and individual psychosocial capacity mobilization are institutionally embedded and influenced by reflexive capacity. We argue that the consideration of power structures and agency can provide a complementary approach to explaining adaptive capacity and call for further transdisciplinary empirical research on this topic in different settings of state-initiated adaptation processes. [ABSTRACT FROM AUTHOR]

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO 2 emissions and non-CO 2 impacts, such as NO x emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NO x emitted, or per flown kilometre. In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NO x -induced ozone and H 2 O effects are well represented by the ACCF model output. The NO x -induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NO x -induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NO x -induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO 2 contribution observed in this study is low compared with the non-CO 2 effects, which requires further diagnosis. [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which (a) are of high spatial and temporal resolution, (b) explore a range of future climate change scenarios, (c) are consistent with historical observations in the historical period, and (d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing 11 near-surface meteorological variables that can be used to as input to many different impact models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980–2080. It was derived from the state-of-the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways 2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9–1.9 K for RCP2.6 up to 2.8–4.3 K for RCP8.5 between 1980–2000 and 2060–2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to - 39 %). All four RCP scenarios and ensemble members are also provided with bias correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, an extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: 10.5285/8194b416cbee482b89e0dfbe17c5786c. [ABSTRACT FROM AUTHOR]

Poor representations of aerosols, clouds, and aerosol–cloud interactions (ACIs) in Earth system models (ESMs) have long been the largest uncertainties in predicting global climate change. Huge efforts have been made to improve the representation of these processes in ESMs, and the key to these efforts is the evaluation of ESM simulations with observations. Most well-established ESM diagnostics packages focus on the climatological features; however, they lack process-level understanding and representations of aerosols, clouds, and ACIs. In this study, we developed the Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package to facilitate the routine evaluation of aerosols, clouds, and ACIs simulated the Energy Exascale Earth System Model (E3SM) from the US Department of Energy (DOE). This paper documents its version 2 functionality (ESMAC Diags v2), which has substantial updates compared with version 1 (Tang et al., 2022a). The simulated aerosol and cloud properties have been extensively compared with in situ and remote-sensing measurements from aircraft, ship, surface, and satellite platforms in ESMAC Diags v2. It currently includes six field campaigns and two permanent sites covering four geographical regions: the eastern North Atlantic, the central US, the northeastern Pacific, and the Southern Ocean. These regions produce frequent liquid- or mixed-phase clouds, with extensive measurements available from the DOE Atmospheric Radiation Measurement user facility and other agencies. ESMAC Diags v2 generates various types of single-variable and multivariable diagnostics, including percentiles, histograms, joint histograms, and heatmaps, to evaluate the model representation of aerosols, clouds, and ACIs. Select examples highlighting the capabilities of ESMAC Diags are shown using E3SM version 2 (E3SMv2). In general, E3SMv2 can reasonably reproduce many observed aerosol and cloud properties, with biases in some variables such as aerosol particle and cloud droplet sizes and number concentrations. The coupling of aerosol and cloud number concentrations may be too strong in E3SMv2, possibly indicating a bias in processes that control aerosol activation. Furthermore, the liquid water path response to a perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants further study to improve the cloud microphysics parameterizations in E3SMv2. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts to better understand past, present and future climate changes in a multi-model context. A total of 21 Model Intercomparison Projects (MIPs) were endorsed in its 6th phase (CMIP6), which included 190 different experiments that were used to simulate 40000 years and produced around 40 PB of data in total. This paper shows the main results obtained from the collection of performance metrics done for CMIP6 (CPMIP). The document provides the list of partners involved, the CPMIP metrics per institution/model, and the approach used for the collection and the coordination behind this process. Furthermore, a section has been included to analyze the results and prove the usefulness of the metrics for the community. Moreover, we describe the main difficulties faced during the collection and propose recommendations for future exercises. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted the increasingly dominant role of external forcing in driving Atlantic and Pacific Ocean variability during the second half of the 20th century. This paper provides insights into the underlying mechanisms driving interactions between modes of variability over the two basins. We define a set of possible drivers of these interactions and apply causal discovery to reanalysis data, an ensemble of pacemaker simulations where the observed El Niño Southern Oscillation (ENSO) is prescribed, and a pre-industrial control simulation. We also utilize large ensemble means of historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to quantify the effect of external forcing and improve the understanding of its impact. By conducting a causal analysis of the historical time series, a regime switch is identified in the interactions between major modes of Atlantic and Pacific climate variability. Causal networks derived from pacemaker simulations support this finding and further demonstrate that the effect of external forcing could favor an Atlantic-driven regime between 1985 and 2014 where warming tropical North Atlantic sea surface temperatures induce a La Niña-like cooling in the equatorial Pacific during the following season through a strengthening of the Pacific Walker Circulation. This negative sign effect was not detected when the historical external forcing signal is removed in the pacemaker ensemble. The analysis of the pre-industrial control run further supports the notion that the Atlantic and Pacific modes of natural climate variability exert contrasting impacts on each other even in the absence of external forcing. We show that causal discovery can quantify previously unknown connections and thus provides important potential to contribute to a deeper understanding of the mechanisms driving changes in regional and global climate variability. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root-zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exist for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method's model parameter T , which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T -parameter values were calculated for four soil depth layers (0–10, 10–40, 40–100, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers, with the median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties in the EF method from ISMN ground reference measurements taken at the surface and at varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes and temporal variations. The product described here is, to the best of our knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth. [ABSTRACT FROM AUTHOR]

Lakes experience anthropogenically-forced changes that may initiate ecosystem feedbacks, in some cases reaching tipping points beyond which impacts become hard to reverse. Lakes are also important players in the global climate by ventilating a large share of terrestrial carbon back to the atmosphere as greenhouse gases, and will likely provide substantial feedbacks to climate change. In this paper we address various major changes in lake ecosystems, and discuss if tipping points can be identified, predicted, or prevented in them, along with their associated feedbacks to climate change. Potential tipping dynamics assessed include eutrophication-driven anoxia and internal phosphorus-loading, increased loading of organic matter from terrestrial to lake ecosystems (lake "browning"), lake formation or disappearance in response to cryosphere shifts, switching from nitrogen to phosphorus limitation, salinization, and the spread of invasive species. We also address other types of abrupt, or threshold-type shifts in lakes and ponds, and conclude on which tipping points are locally or regionally relevant. We identify a key set of co-drivers that could lead to self-sustaining feedbacks, with warming, browning, and eutrophication leading to increased lake stratification, heterotrophy, and algal mass, which separately or collectively drive benthic oxygen depletion and in turn increased greenhouse gas emissions (helping to drive further warming and organic matter loading) and internal phosphorus-loading (driving further eutrophication). Several of these processes can feature tipping points, which further warming will likely make easier to reach. We argue that the full importance of the vulnerability of lakes to climate and other anthropogenic impacts, as well as their feedback to climate is not yet fully acknowledged, so there is a need both for science and communication in this regard. [ABSTRACT FROM AUTHOR]

This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of CESM-LE climate model simulations': the stationary case with pre-industrial radiative forcing and the climate change case with measured and RCP8.5 radiative forcing. Stationary control simulation 1800-year long time-series were used for stationary teleconnection and 40-member ensemble from the period 1920-2100 for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2-meter level, sea-level pressure, sea ice concentration, precipitations, geopotential height and 10-meter level wind speed. The Arctic was divided into seven areas. the Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local factors are stronger. The majority of teleconnections in winter and spring can be explained by climate indexes NAO and AO. During ongoing climate change, the teleconnection patterns did not show remarkable developments by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration. To estimate different Arctic regions' collective statistical connections with the Baltic Sea region, we calculated the correlation between the parameter and its Ridge regression estimation. Seasonal coefficient of determination, R2, were highest for winter: for temperature R2 = 0.64, for surface pressure R2 = 0.44 and for precipitation R2 = 0.35. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker with the highest R2 = 0.09 for temperature in the spring. Hence, the forecasting capacity of Arctic climate data for the Baltic Sea region is weak. Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnections pattern between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century. [ABSTRACT FROM AUTHOR]

Global lake/reservoir surface water extent is the basic input data for many studies. Although there are some datasets at present, there are problems such as incomplete or spatial inconsistency exist among them due to various reasons like different data sources and dynamic change characteristics of the surface water. In this paper, a new Global 10 Lake/Reservoir Surface Extent Dataset (GLRSED) that contains spatial extent and basic attributes (e.g., name, area, lake type and source) of 2.17 million lakes/reservoirs was produced based on HydroLAKES, GRanD and OpenStreetMap. In addition, by overlaying with mountain data, we identified the lakes/reservoirs located in mountain areas. By overlaying with the Global geReferenced Database of Dams (GOODD) and Georeferenced Global Dams and Reserves (GeoDAR) dataset, we partitioned human-managed reservoirs from natural lakes. Lakes/reservoirs on the rivers were identified by overlaying 15 with the SWOT Mission River Database (SWORD). Using the same method, we identified endorheic, glacier-fed and permafrost-fed lakes. Furthermore, the coverage of Surface Water and Ocean Topography (SWOT) ground track to each lake/reservoir in GLRSED was calculated to explore the potential of SWOT for monitoring lakes. The dataset could provide basic data for global lake/reservoir monitoring, enabling the study on the impact of human actions and climate changes on lake/reservoir freshwater availability. The GLRSED database is available at https://doi.org/10.5281/zenodo.8121174 (Bai et 20 al., under review, 2023). [ABSTRACT FROM AUTHOR]

A key goal of the Paris Agreement (PA) is to reach net-zero Greenhouse Gasses (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa's rapidly growing population and GDP makes this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) in Africa over three decades (1990-2018). We compare bottom-up approaches including UNFCCC national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO2 fluxes in the Land Use, Land Use Change and Forestry (LULUCF) sector, and global atmospheric inversions. Our database is available from Zenodo at: https://doi.org/10.5281/zenodo.7347077 (Mostefaoui et al., 2022). For inversions, we applied different methods to separate anthropogenic CH4 emissions. The bottom-up (BU) inventories show that over the decade 2010-2018, less than ten countries represented more than 75% of African fossil CO2 emissions. With a mean of 1373 MtCO2 yr-1, total African fossil CO2 emissions over 2010-2018 represent only 4% of global fossil emissions. Yet, these emissions grew by +34% from 1990-1999 to 2000-2009 and by +31% over 2000-2009 to 2010-2018, more than doubling in 30 years. This growth rate is more than twice faster than the global growth rate of fossil CO2 emissions. The anthropogenic emissions of CH4 grew by 5% from 1990-1999 to 2000-2009 and by 14.8% from 2000-2009 to 2010-2018. The N2O emissions grew by 19.5% from 1990-1999 to 2000-2009; and by 20.8% from 2000-2009 to 2010-2018. When using the mean of estimates from UNFCCC reports (including the land use sector), with corrections from outliers, Africa was a mean source of greenhouse gasses of +2622 $%&' ($() MtCO2e yr-1 from all bottom-up estimates (sub- and superscript indicating min-max range uncertainties), and of +2637%,'% -&,(MtCO2e yr-1 from top-down methods, during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of top-down estimates is larger than the one of bottom up, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty of bottom-up estimates. A main source of uncertainty comes from CO2 fluxes in the land-use sector (LULUCF) for which the spread across inversions is larger than 50%, especially in Central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022) The median of CH4 emissions from inversions based on satellite retrievals and in situ surface networks are consistent with each other within 2% at continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH4 emissions with bottom-up inventories such as PRIMAP-hist. For N2O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N2O sources cannot be separated accurately from anthropogenic ones in inversions, or because bottom-up estimates ignore indirect emissions and under-estimate emission factors. Future improvements can be expected thanks to a denser network for monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows to compute budgets thanks to recombinations those data products. Our results allow to understand uncertainty and trends of emissions and removals in a region of the world where few observations exist and most inventories are based on default IPCC guidelines values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to figures are available at: https://doi.org/10.5281/zenodo.7347077 (Mostefaoui et al., 2022). [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]

Research on agricultural development models shows that local applications of global models are adapted both to the globalisation of agricultural markets and to climate change. The circulation of such models is also linked to local political and historical contexts. However, few studies have focused on abrupt changes in economic policies, such as those following the Arab Spring. We propose to study the evolution of olive-growing development policies in post-revolutionary Tunisia. In order to mitigate both market- and climate-induced vulnerabilities and to make the sector more competitive with major olive producers, Food and Agriculture Organization (FAO) guidelines recommend intensification of Tunisian olive farming through irrigation. Our research makes two main claims: (1) the change in the production model towards irrigation aims to respond to globalisation, climate change and national policies. (2) Some exporters are involved at several levels of the value chain. This research conducted by geographers and economists analyses the mutations of the olive sector towards irrigation, using a double theoretical framework on the circulation of agricultural development models, with a political-ecology approach. This paper contributes to a growing body of research on the relationship between commodity production networks and water studies. [ABSTRACT FROM AUTHOR]

Appropriate adaptation planning is contingent upon information about the potential future impacts of climate change, and hydrological impact assessments are of particular importance. The UKSCAPE-G2G datasets were produced, as part of the Natural Environment Research Council (NERC) UK-SCAPE (UK Status, Change and Projections of the Environment) programme, to contribute to this information requirement. They use the Grid-to-Grid (G2G) national-scale hydrological model configured for both Great Britain and Northern Ireland (and the parts of the Republic of Ireland that drain to rivers in NI). Six separate datasets are provided, for two sets of driving data – one observation-based (1980–2011) and one climate-projection-based (1980–2080) – for both river flows and soil moisture on 1 km × 1 km grids across Great Britain and Northern Ireland. The river flow datasets include grids of monthly mean flow, annual maxima of daily mean flow, and annual minima of 7 d mean flow (m 3 s -1). The soil moisture datasets are grids of monthly mean soil moisture content (m water / m soil), which should be interpreted as depth-integrated values for the whole soil column. The climate-projection-based datasets are produced using data from the 12-member 12 km regional climate model ensemble of the latest UK climate projections (UKCP18), which uses RCP8.5 emissions. The production of the datasets is described, along with details of the file format and how the data should be used. Example maps are provided, as well as simple UK-wide analyses of the various outputs. These suggest potential future decreases in summer flows, annual minimum 7 d flows, and summer/autumn soil moisture, along with possible future increases in winter flows and annual maximum flows. References are given for published papers providing more detailed spatial analyses, and some further potential uses of the data are suggested. The datasets are listed in Table 1. [ABSTRACT FROM AUTHOR]

Global land cover (GLC) information with fine spatial resolution is a fundamental data input for studies on biogeochemical cycles of the Earth system and global climate change. Although there are several public GLC products with 30 m resolution, considerable inconsistencies were found among them, especially in fragmented regions and transition zones, which brings great uncertainties to various application tasks. In this paper, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing multiple existing land cover (LC) products based on the Dempster–Shafer theory of evidence (DSET). Firstly, we used more than 160 000 global point-based samples to locally evaluate the reliability of the input products for each land cover class within each 4 ∘ × 4 ∘ geographical grid for the establishment of the basic probability assignment (BPA) function. Then, Dempster's rule of combination was used for each 30 m pixel to derive the combined probability mass of each possible land cover class from all the candidate maps. Finally, each pixel was determined with a land cover class based on a decision rule. Through this fusing process, each pixel is expected to be assigned the land cover class that contributes to achieving a higher accuracy. We assessed our product separately with 34 711 global point-based samples and 201 global patch-based samples. Results show that the GLC-2015 map achieved the highest mapping performance globally, continentally, and ecoregionally compared with the existing 30 m GLC maps, with an overall accuracy of 79.5 % (83.6 %) and a kappa coefficient of 0.757 (0.566) against the point-based (patch-based) validation samples. Additionally, we found that the GLC-2015 map showed substantial outperformance in the areas of inconsistency, with an accuracy improvement of 19.3 %–28.0 % in areas of moderate inconsistency and 27.5 %–29.7 % in areas of high inconsistency. Hopefully, this improved GLC-2015 product can be applied to reduce uncertainties in the research on global environmental changes, ecosystem service assessments, and hazard damage evaluations. The GLC-2015 map developed in this study is available at 10.6084/m9.figshare.22358143.v2 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

Poor representations of aerosols, clouds and aerosol-cloud interactions (ACI) in Earth System Models (ESMs) have long been the largest uncertainties in predicting global climate change. Huge efforts have been made to improve the representation of these processes in ESMs, and key to these efforts is evaluation of ESM simulations with observations. Most well-established ESM diagnostics packages focus on the climatological features; however, they are lack of the process-level understanding and representations of aerosols, clouds, and ACI. In this study, we developed an ESM aerosol-cloud diagnostics package (ESMAC Diags) to facilitate routine evaluation of aerosols, clouds and aerosol-cloud interactions simulated by the Department of Energy's (DOE) Energy Exascale Earth System Model (E3SM). This paper documents its version 2 functionality (ESMAC Diags v2), which has substantial updates from its version 1 (Tang et al., 2022a). The simulated aerosol and cloud properties have been extensively compared with in-situ and remote-sensing measurements from aircraft, ship, surface and satellite platforms in ESMAC Diags v2. It currently includes six field campaigns and two permanent sites covering four geographical regions: Eastern North Atlantic, Central U.S., Northeastern Pacific and Southern Ocean, where frequent liquid or mixed-phase clouds are present and extensive measurements are available from the DOE Atmospheric Radiation Measurement user facility and other agencies. ESMAC Diags v2 generates various types of single-variable and multi-variable diagnostics, including percentiles, histograms, joint histograms and heatmaps, to evaluate model representation of aerosols, clouds, and aerosol-cloud interactions. Select examples highlighting ESMAC Diags capabilities are shown using E3SM version 2 (E3SMv2). E3SMv2 in general can reasonably reproduces many observed aerosol and cloud properties, with biases in some variables such as aerosol particle and cloud droplet sizes and number concentrations. The coupling of aerosol and cloud number concentrations may be too strong in E3SMv2, possibly indicating a bias in processes that control aerosol activation. Furthermore, the liquid water path adjustment to perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants a further study to improve the cloud microphysics parameterizations in E3SMv2. [ABSTRACT FROM AUTHOR]

Climate emulators are models calibrated on Earth System Models (ESMs) to replicate their behaviour. Thanks to their low computational cost, these tools are becoming increasingly important to accelerate the exploration of emission scenarios and the coupling of climate information to other models. However, the emulation of regional climate extremes and water cycle variables has remained challenging. The MESMER emulator was recently expanded to represent regional temperature extremes in the new "MESMER-X" version, which is targeted at impact-related variables, including extremes. This paper presents a further expansion of MESMER-X to represent indices related to fire weather and soil moisture. Given a trajectory of global mean temperature, the extended emulator generates spatially-resolved realisations for the seasonal average of the Canadian Fire Weather Index (FWI), the number of days with extreme fire weather, the annual average of the soil moisture and the annual minimum of the monthly average soil moisture. For each ESM, the emulations mimic the statistical distributions and the spatial patterns of these indicators. For each of the four variables considered, we calculate how much do the quantiles of the emulations deviate from those of the ESMs, resulting in good performances. Moreover, we argue that this framework can be expanded to further variables, given that it works over a large range of annual indicators. Overall, the now expanded MESMER-X emulator can emulate several climate variables, including climate extremes and soil moisture availability, and is a useful tool for the exploration of regional climate changes and their impacts. [ABSTRACT FROM AUTHOR]

Research on perceptions of climate change impacts contributes to understanding motivations for adaptation action and increases the legitimacy of climate adaptation research and policy. The body of literature on climate change perception (CCP) is extensive. Given that the perception of climate change is commonly presented as being dependent on an individual's sociocultural and spatial contexts and that climate change is an abstract concept with different dimensions of meaning, this paper systematizes the research in an actor-centered manner. Using a systematic literature review, the abstracts from 821 interdisciplinary studies on CCP were coded and statistically analyzed. The results show that predominantly knowledge about the CCP of vulnerable groups of actors and regions was generated using quantitative methods. Impacts at the collective and institutional levels of CCP were rarely explored. This indicates an individualizing perspective of research on climate adaptation of vulnerable actors. Conclusions for future research are drawn. [ABSTRACT FROM AUTHOR]

Defining experiences of climate change loss and damage (L&D) is the topic of contentious debate across the social sciences and humanities. In this paper, we contribute to this debate by making loss(es) from climate change better legible. After detailing the complexity of the L&D debate from both a political and scientific perspective, we turn to phenomenological theory (Martin Heidegger, Tetsuro Watsuji, Bernhard Waldenfels) in order to make sense of climate's presence and the absences generated from changing climates. The phenomenology of loss we develop promises to help account for experiences of climate change that escape more traditional (social) scientific approaches to both economic and non-economic losses. More broadly, we present an alternative approach to applying phenomenology to research in social science (on climate change). [ABSTRACT FROM AUTHOR]

The 2014-2018 drought over South Africa's winter rainfall zone (WRZ) created a critical water crisis which highlighted the region's drought and climate change vulnerability. Consequently, it is imperative to better understand the climatic characteristics of the drought in order to inform regional adaptation to projected climate change. In this paper we investigate the spatio-temporal patterns of drought intensity and the recent rainfall trends, focusing on assessing the consistency of the prevailing conceptual model of drought drivers with observed patterns. For this we use the new spatial subdivision for the region encompassing the WRZ introduced in our companion paper (Conradie et al., 2022). Compared to previous droughts since 1979, the 2014-2018 drought in the WRZ core was characterised by a markedly lower frequency of very wet days (exceeding the climatological 99.5th percentile daily rainfall -- including dry days) and of wet months (SPI1 > 0:5), a sub-seasonal attribute not previously reported. There was considerable variability in the spatial footprint of the drought. Short-term drought began in the south-western core WRZ in spring 2014. The peak intensity gradually spread north-eastward, although a spatially near-uniform peak is seen during mid-2017. The overall drought intensity for the 2015--2017 period transitions radially from most severe in the WRZ core to least severe in the surroundings. During 2014 and 2015, the drought was most severe at those stations receiving the largest proportion of their rainfall from westerly and north-westerly winds; by 2018, those stations receiving the most rain from the south and south-east were most severely impacted. This indicates an evolving set of dynamic drivers associated with distinct rain-bearing synoptic flows. No evidence is found to support the suggestion that the drought was more severe in the mountain catchments of Cape Town's major supply reservoirs than elsewhere in the core nor that rain day frequency trends since 1979 are more negative in this subdomain. Rainfall and rain day trend rates also exhibit some connections to the spatial seasonality structure of the WRZ, although this is weaker than for drought intensity. Caution should be applied in assessing South African rain day trends given their high sensitivity to observed data shortcomings. Our findings suggest an important role for zonally asymmetric dynamics in the region's drought evolution. This analysis demonstrates the utility of the spatial subdivisions proposed in the companion paper by highlighting spatial structure in drought intensity evolution linked to rainfall dynamics. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exists for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method’s model parameter T, which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers with median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally-dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties of the EF method from ISMN ground reference measurements taken at the surface and in varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes as well as temporal variations. The product described here is, to our best knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth. [ABSTRACT FROM AUTHOR]

The carbon budget concept is based on discoveries made at the end of the 2000s, claiming that global mean temperature (GMT) increase is roughly linearly dependent on cumulative emissions, with a proportionality metric named transient climate response to cumulative carbon emissions (TCRE). Since its emergence in natural science, the carbon budget concept has gained prominence as a tool for policymakers and climate economists alike. However, its usage in economic assessments has been critiqued due to its inability to capture time-delay effects and TCRE changing with climatic conditions. In this paper, we define the "perfect budget" equation as a purely linear relationship between GMT and cumulative emissions. Hereby, we distinguish two sources of deviations from the perfect carbon budget: emission scenario and climate state dependence. The former stems from the scenario choice under the fixed cumulative emissions, the latter from the change in TCRE with increased cumulative emissions. Introducing Green's function formalism in the context of the temperature response to an emission pulse (pulse response), we provide reasoning behind time delay and possible scenario-dependent deviations and connect Green's formalism with the carbon budget equation. Using an optimization program, we show that extreme scenario-dependent deviations are relatively small compared to the overall GMT increase and even smaller when there are no more emissions after the year of optimization. We explain the behaviour of scenario-dependent deviations by the shape of the pulse response. Additionally, we show that the pulse response changes with climatic conditions, through which we further explain carbon budget state dependency. Finally, we derive a generalized analytical carbon budget equation, which captures the state-dependent change of TCRE through a weak exponential GMT increase dependency on cumulative emissions. The analysis is done in FAIRv2.0.0, a simple climate model that includes climate feedback modifying the carbon cycle. [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which are a) high spatial and temporal resolution; b) explore a range of future climate change scenarios; c) are consistent with historical observations in the historical period; and d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing eleven near-surface meteorological variables that can be used to as input to many different impacts models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980-2080. It was derived from the state-of the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) Regional Climate Model (RCM) UKCP18 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways: RCP2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9-1.9 K for RCP2.6 up to 2.8-4.3 K for RCP8.5 between 1980-2000 and 2060-2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to -39 %). All four RCP scenarios and ensemble members are also provided with bias-correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: http://dx.doi.org/10.5285/8194b416cbee482b89e0dfbe17c5786c (Robinson et al., 2022). [ABSTRACT FROM AUTHOR]

The alpine grassland ecosystem accounts for 53 % of the Qinghai–Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015–2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m 2) was successfully upscaled to the MODIS pixel scale (62 500 m 2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m -2. Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (10.11888/Terre.tpdc.272587; H. Zhang et al., 2022). [ABSTRACT FROM AUTHOR]

Detailed and accurate geomorphological historical landslide inventory maps are an invaluable source of information for many research topics and applications. Their systematic preparation worldwide has been advised by many researchers as it may foster our knowledge on landslides, their spatial and temporal distribution, their potential interaction with the built environment, their contribution to landscape dynamics, and their response to climate change in the past. Due to the extreme variability of the morphological and radiometric elements that can reveal historical landslides, geomorphological historical landslide inventory maps are produced by expert interpretation, which makes it a time-consuming and expensive process, which often discourages wide-area mapping activities. In this paper we present a new geomorphological historical landslide inventory map for a 1460 km 2 area in the Daunia Apennines, the north-western sector of the Apulia (Puglia) region, in southern Italy. The inventory contains 17 437 landslides classified according to relative age, type of movement, and estimated depth. Landslides were mapped according to rigorous and reproducible criteria applied by two teams of expert photo interpreters to two sets of stereoscopic aerial photographs taken in 1954/55 and 2003. The dataset consists of a digital archive publicly available at 10.1594/PANGAEA.942427 (Cardinali et al., 2022). [ABSTRACT FROM AUTHOR]

Appropriate adaptation planning is contingent upon information about the potential future impacts of climate change, and hydrological impact assessments are of particular importance. The UKSCAPE-G2G datasets were produced, as part of the NERC UK-SCAPE programme, to contribute to this information requirement. They use the Grid-to-Grid (G2G) national-scale hydrological model configured for both Great Britain and Northern Ireland (and the parts of the Republic of Ireland that drain to rivers in NI). Six separate datasets are provided, for two sets of driving data -- one observation-based (1980-2011) and one climate projection-based (1980-2080) -- for both river flows and soil moisture on 1 km x 1 km grids across GB and NI. The river flow datasets include grids of monthly mean flow, annual maxima of daily mean flow, and annual minima of 7-day mean flow (m³s-1). The soil moisture datasets are grids of monthly mean soil moisture content (m water/m soil), which should be interpreted as depth-integrated values for the whole soil column. The climate projection-based datasets are produced using data from the 12-member 12km regional climate model ensemble of the latest UK climate projections (UKCP18), which uses RCP8.5 emissions. The production of the datasets is described, along with details of the file format, and how the data should be used. Example maps are provided, as well as simple UK-wide analyses of the various outputs. These suggest potential future decreases in summer flows, annual minimum 7-day flows, and summer/autumn soil moisture, along with possible future increases in winter flows and annual maximum flows. References are given for published papers providing more detailed spatial analyses, and some further potential uses of the data are suggested. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool needs to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as "CMORization". While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing of "native" climate model output, i.e., operational output produced by running the climate model through the standard workflow of the corresponding modeling institute. This is achieved by an extension of ESMValTool's preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitude–longitude grid. Extensions to ESMValTool's regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids. Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; at the moment, only surface variables are available), EC-Earth3, EMAC, ICON, and IPSL-CM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added. [ABSTRACT FROM AUTHOR]

Agriculture depends in large part on relations with weather phenomena, such as rain and temperature. Anticipatory knowledge about the atmosphere therefore is vital in agricultural livelihoods. Based on an ethnographic case study of weather forecasting for small-scale farming in western Kenya, in this paper I discuss different ways in which knowledge about the future weather is produced. While development organizations promote expert forecasts that draw on meteorological sensing technologies as a solution to dealing with climate change, I show how knowing the weather is an entangled affair in a sensory assemblage that simultaneously draws on scientific instruments and on other entities such as animals, plants, clouds and embodied sensoria associated with experiential knowledge. Building on concepts related to science and technology studies that address the relations between humans and nonhumans, I suggest to treat scientific and experiential devices symmetrically by looking at their more-than-human sensoria, proxies and imaginations to understand how farmers attune to the weather. In practice, then, navigating the uncertainties of the weather is not enabled by scientific meteorology alone, but by combining different sensory devices and practices of interpretation that together mediate the weather as something to be known and acted upon. [ABSTRACT FROM AUTHOR]

During the months of July and August 2021 and 2022, numerous climatic disturbances such as heat domes in Canada, floods and landslides in Belgium, Germany, Turkey, China as well as giant fires in Russia and Greece have marked the news and people's minds. Climate inertia and the complexity of changing the world's energy model make it necessary to adapt territories to limit the impacts of the changes that are underway. However, if the speeches on the urgency to act to cool the cities have become omnipresent, the implementations of solutions seem limited, and some territories even seem to be going in the opposite direction by massively artificializing the edges of urban areas. The objective of the FreshWay research project is to identify and analyse planning and implementation on the ground to combat summer heat waves and to represent the adaptation trajectories of cities. The first information that is questioned is the evolution of urban vegetation insofar as plants provide shade and allow cooling through the process of evapotranspiration. The paper presents the required information and the data model, cases study, the process to integrate data, the choice of indicators and the construction of trajectories from different perspectives for the municipality of Castelnau-le-lez, Sarcelles and Pontault-Combault, and at different level of details. [ABSTRACT FROM AUTHOR]

The extent of surface water has been changing significantly due to climatic change and human activities. However, it is challenging to capture the interannual changes of inland water bodies due to their high seasonal variation and abrupt change. In this paper, a global annual surface water cover frequency dataset (GLOBMAP SWF) was generated from the MODIS land surface reflectance products during 2000–2020 to describe the seasonal and interannual dynamics of surface water. Surface water cover frequency (SWF) was proposed as the percentage of the time period when a pixel is covered by water in a year. Instead of determination of the water directly, the SWF was estimated indirectly by identifying land observations among annual clear-sky observations to reduce the influence of clouds and variability of water bodies and surface background characteristics, which helps to improve the applicability of the algorithm for different regions across the globe. The generated dataset shows better performances for frozen water, saline lakes, bright surfaces and regions with frequent cloud cover compared with the two high-frequency surface water datasets derived from MODIS data, and it captures more intermittent surface water but may underestimate small water bodies when compared with two high-resolution datasets derived from Landsat data. Compared with the high-resolution SWF maps extracted from Sentinel-1 data in eight regions that cover lakes, rivers and wetlands, the R2 reaches 0.46 to 0.97, RMSE ranges from 7.24 % to 22.62 %, and MAE is between 2.07 % and 7.15 %. In 2020, the area of global maximum surface water extent is 3.38×106 km 2 , of which the permanent surface water accounts for approximately 54 % (1.83×106 km 2), and the other 46 % is intermittent surface water (1.55×106 km 2). The area of global maximum and permanent surface water has been shrinking since 2001, with a change rate of -7577 and -4315 km 2 yr -1 (p<0.05), respectively, while the intermittent surface water with the SWF above 50 % has been expanding (1368 km 2 yr -1 , p<0.01). This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland waters extent with consideration of its seasonal variation. The GLOBMAP SWF data are available at 10.5281/zenodo.6462883 (Liu and Liu, 2022). [ABSTRACT FROM AUTHOR]

This paper aims to study wet–cold compound events (WCCEs) in Greece for the wet and cold season November–April since these events may affect directly human activities for short or longer periods, as no similar research has been conducted for the country studying the past and future development of these compound events. WCCEs are divided into two different daily compound events, maximum temperature– (TX) accumulated precipitation (RR) and minimum temperature– (TN) accumulated precipitation (RR), using fixed thresholds (RR over 20 mm d -1 and temperature under 0 ∘ C). Observational data from the Hellenic National Meteorology Service (HNMS) and simulation data from reanalysis and EURO-CORDEX models were used in the study for the historical period 1980–2004. The ensemble mean of the simulation datasets from projection models was employed for the near future period (2025–2049) to study the impact of climate change on the occurrence of WCCEs under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Following data processing and validation of the models, the potential changes in the distribution of WCCEs in the future were investigated based on the projected and historical simulations. WCCEs determined by fixed thresholds were mostly found over high altitudes with TN–RR events exhibiting a future tendency to reduce particularly under the RCP 8.5 scenario and TX–RR exhibiting similar reduction of probabilities for both scenarios. [ABSTRACT FROM AUTHOR]

Emotions affect how humans relate to others and define their place in the world. They thus shape responses to socio-ecological problems like climate change. In spite of the overwhelming knowledge and concern about climate change, a lack of appropriate moral and political consequences prevails in most contemporary societies. Instead of trying to explain climate inaction as a result of (un)awareness, this paper introduces a new perspective by conceptualising climate inaction as an active social process animated by emotions. Drawing on an interdisciplinary and radically relational perspective, I grasp climate inaction as a product of more-than-human intra-action and explore the affective role of emotions within this production. To illustrate how emotions energise climate inaction, I sketch how fear, grief, and hope animate current climate responses. [ABSTRACT FROM AUTHOR]

Estimating the risk of forest collapse due to extreme climate events is one of the challenges of adapting to climate change. We adapt a concept from ruin theory, which is widely used in econometrics and the insurance industry, to design a growth–ruin model for trees which accounts for climate hazards that can jeopardize tree growth. This model is an elaboration of a classical Cramer–Lundberg ruin model that is used in the insurance industry. The model accounts for the interactions between physiological parameters of trees and the occurrence of climate hazards. The physiological parameters describe interannual growth rates and how trees react to hazards. The hazard parameters describe the probability distributions of the occurrence and intensity of climate events. We focus on a drought–heatwave hazard. The goal of the paper is to determine the dependence of the forest ruin and average growth probability distributions on physiological and hazard parameters. Using extensive Monte Carlo experiments, we show the existence of a threshold in the frequency of hazards beyond which forest ruin becomes certain to occur within a centennial horizon. We also detect a small effect of the strategies used to cope with hazards. This paper is a proof of concept for the quantification of forest collapse under climate change. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool need to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as CMORization. While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing native climate model output, i.e., raw output directly produced by the climate model. This is achieved by an extension of ESMValTool’s preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitudelongitude grid. Extensions to ESMValTool’s regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids.Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; will be fully available in ESMValTool v2.7.0), EC-Earth3, EMAC, ICON, and IPSLCM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added. [ABSTRACT FROM AUTHOR]

Detailed and accurate geomorphological historical landslide inventory maps are an invaluable source of information for many research topics and applications. Their systematic preparation worldwide has been advised by many researchers as it may foster our knowledge on landslides, their spatial and temporal distribution, their potential interaction with the built environment, their contribution to landscape dynamics, their response to climate change in the past. Due to the extreme variability of the morphological and radiometric elements that can reveal historical landslides, geomorphological historical landslide inventory maps are produced by expert interpretation, which makes it a time consuming and expensive process, which often discourages wide area mapping activities. In this paper we present a new geomorphological historical landslide inventory map for a 1,460 km² area in the Daunia Apennines, the northern-western sector of the Apulia (Puglia) region, in Southern Italy. The inventory contains 17,437 landslides classified according to relative age, type of movement and estimated depth. Landslides were mapped according to rigorous and reproducible criteria applied by two teams of expert photo interpreters to two sets of stereoscopic aerial photographs taken in 1954/55 and 2003. The dataset consists of a digital archive publicly available at https://doi.pangaea.de/10.1594/PANGAEA.942427 (Cardinali et al., 2022). [ABSTRACT FROM AUTHOR]

The Ross Ice Shelf (RIS) is currently stable but recent observations have indicated that basal melt rates beneath the ice shelf are expected to increase. It is important to know which areas of the RIS are more sensitive to enhanced basal melting as well as other external forcings or internal material properties of the ice to understand how climate change will influence RIS mass balance. In this paper, we use automatic differentiation and the Ice Sheet and Sea-level System Model to quantify the sensitivity of the RIS to changes in basal friction, ice rigidity, surface mass balance, and basal melting. Using volume above flotation (VAF) as our quantity of interest, we find that the RIS is most sensitive to changes in basal friction and ice rigidity close to grounding lines and along shear margins of the Siple Coast Ice Streams and Transantarctic Mountains Outlet Glaciers. The RIS sensitivity to surface mass balance is uniform over grounded ice, while the sensitivity to basal melting is more spatially variable. Changes in basal melting close to the grounding lines of the Siple Coast Ice Streams and Transantarctic Mountains outlet glaciers have a larger impact on the final VAF compared to elsewhere. Additionally, the pinning points and ice shelf shear margins are highly sensitive to changes in basal melt. Our sensitivity maps allow areas of greatest future vulnerability to be identified. [ABSTRACT FROM AUTHOR]

Global land cover (GLC) information with fine spatial resolution is a fundamental data input for studies on biogeochemical cycles of the Earth system and global climate change. Although there are several public GLC products with 30 m resolution, considerable inconsistencies were found among them especially in fragmented regions and transition zones, which brings great uncertainties to various application tasks. In this paper, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing multiple existing land cover products based on the Dempster-Shafer theory of evidence (DSET). Firstly, we used more than 160,000 global point-based samples to locally evaluated the reliability of the input GLC products for each LC class within each 4°×4° geographical grid for the establishment of the basic probability assignment (BPA) function. Then, the Dempster's rule of combination was used for each 30 m pixel to derive the combined probability mass of each possible land cover class from all the candidate maps. Finally, each pixel was determined with a land cover class based on a decision rule. Through this fusing process, each pixel is expected to be assigned with the land cover class that contributes to achieve a higher accuracy. We assessed our product separately with 34,987 global point-based samples and 144 global patch-based samples. Results show that, the GLC-2015 map achieved the highest mapping performance globally, continentally, and eco-regionally compared with the existing 30 m GLC maps, with an overall accuracy of 76.0% (83.8%) and a kappa coefficient of 0.715 (0.548) against the point-based (patch-based) validation samples. Additionally, we found that the GLC-2015 map showed substantial outperformance in the areas of inconsistency, with an accuracy improvement of 17.6%-23.2% in areas of moderate inconsistency, and 21.0%-27 25.2% in areas of high inconsistency. Hopefully, this improved GLC-2015 product can be applied to reduce uncertainties in the research on global environmental changes, ecosystem service assessments, and hazard damage evaluations, etc. [ABSTRACT FROM AUTHOR]

The alpine grassland ecosystem accounts for 53% of the Qinghai-Tibet Plateau (QTP) area, which is an important ecological protection barrier, but fragile and highly vulnerable to climate change. Therefore, continuous monitoring of the aboveground biomass (AGB) of grassland is necessary. Although many studies have mapped the spatial distribution of AGB over the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to re-estimate the grassland AGB over 20 the QTP from 2000 to 2019. The innovations were as follows: 1) In the aspect of ground data collection, the spatial scale matching among the traditional ground quadrat sampling, UAV photos, and MODIS pixels was fully considered. From 2015 to 2019, 906 pairs of ground-UAV sample data at the quadrat scale and 2,602 sets of UAV data matching the MODIS pixel scale were collected. A total of more than 37,000 UAV photos were captured at the height of 20 meters. Therefore, the ground validation samples was sufficient and scale matched. 2) In terms of model construction, the traditional quadrat scale 25 (0.25m2) was successfully upscaled to the MODIS pixel scale (6,2500 m2) based on the random forest method and stepwise upscaling scheme. Compared with previous studies, the scale matching of independent and dependent variables was realized, effectively reducing the impact of scale mismatch. At the pixel scale, the AGB value estimated by UAV had a more linear correlation with the MODIS vegetation indices than the traditional sampling method. The multi-year independent crossvalidation results showed that the constructed pixel scale AGB estimation had good robustness, with an average R2 of 0.83 30 and RMSE of 34.13 g/m2. Our dataset provides an important input parameter for a comprehensive understanding of the QTP in the process of global climate change. [ABSTRACT FROM AUTHOR]

Inland surface waters are abundant in the tundra and boreal forests of North America, essential to environments and human societies but vulnerable to climate changes. These high-latitude water bodies differ greatly in their morphological and topological characteristics related to the formation, type, and vulnerability. In this paper, we present a water body dataset for the North American high latitudes (WBD-NAHL). Nearly 6.5 million water bodies were identified, with approximately 6 million (∼90 %) of them smaller than 0.1 km2. The dataset provides area and morphological attributes for every water body. During this study, we developed an automated approach for detecting surface water extent and identifying water bodies in the 10 m resolution Sentinel-2 multispectral satellite data to enhance the capability of delineating small water bodies and their morphological attributes. The approach was applied to the Sentinel-2 data acquired in 2019 to produce the water body dataset for the entire tundra and boreal forests in North America. The dataset provided a more complete representation of the region than existing regional datasets for North America, e.g., Permafrost Region Pond and Lake (PeRL). The total accuracy of the detected water extent by the WBD-NAHL dataset was 96.36 % through comparison to interpreted data for locations randomly sampled across the region. Compared to the 30 m or coarser-resolution water datasets, e.g., JRC GSW yearly water history, HydroLakes, and Global Lakes and Wetlands Database (GLWD), the WBD-NAHL provided an improved ability on delineating water bodies and reported higher accuracies in the size, number, and perimeter attributes of water body by comparing to PeRL and interpreted regional dataset. This dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (TPDC; http://data.tpdc.ac.cn , last access: 6 June 2022): 10.11888/Hydro.tpdc.271021 (Feng and Sui, 2020). [ABSTRACT FROM AUTHOR]

Recent extreme weather events have had severe impacts on UK crop yields, and so there is concern that a greater frequency of extremes could affect crop production in a changing climate. Here we investigate the impacts of future climate change on wheat, the most widely grown cereal crop globally, in a temperate country with currently favourable wheat-growing conditions. Historically, following the plateau of UK wheat yields since the 1990s, we find there has been a recent significant increase in wheat yield volatility, which is only partially explained by seasonal metrics of temperature and precipitation across key wheat growth stages (foundation, construction and production). We find climate impacts on wheat yields are strongest in years with compound weather extremes across multiple growth stages (e.g. frost and heavy rainfall). To assess how these conditions might evolve in the future, we analyse the latest 2.2 km UK Climate Projections (UKCP Local): on average, the foundation growth stage (broadly 1 October to 9 April) is likely to become warmer and wetter, while the construction (10 April to 10 June) and production (11 June to 26 July) stages are likely to become warmer and slightly drier. Statistical wheat yield projections, obtained by driving the regression model with UKCP Local simulations of precipitation and temperature for the UK's three main wheat-growing regions, indicate continued growth of crop yields in the coming decades. Significantly warmer projected winter night temperatures offset the negative impacts of increasing rainfall during the foundation stage, while warmer day temperatures and drier conditions are generally beneficial to yields in the production stage. This work suggests that on average, at the regional scale, climate change is likely to have more positive impacts on UK wheat yields than previously considered. Against this background of positive change, however, our work illustrates that wheat farming in the UK is likely to move outside of the climatic envelope that it has previously experienced, increasing the risk of unseen weather conditions such as intense local thunderstorms or prolonged droughts, which are beyond the scope of this paper. [ABSTRACT FROM AUTHOR]

Upper-air data form the backbone of weather analysis and reanalysis products, particularly in the pre-satellite era. However, they are particularly prone to errors and uncertainties, especially data from the early days of aerology. Information that allows to better characterize the errors of radiosonde data is important. This paper reports on an attempt to collect data from historical upper-air intercomparisons and from historical error assessments reaching back to the 1930s. The digitised numerical data will be made available through Copernicus Climate Change Services; here we publish the full information that includes images, literature, and other metadata that may be relevant and can be used to inform homogenization approaches or reanalysis production. The data collection described in this paper is available on PANGAEA https://www.pangaea.de/tok/9e5b464ba828e931103d3794d9ae1d4576f7a03d (Imfeld et al, in review). [ABSTRACT FROM AUTHOR]

Time shapes every single human–environment relationship and is inherent in 21st-century global challenges such as climate change and the urgent move towards global sustainability. Nonetheless, the concept of time is still insufficiently addressed in climate justice debates. This paper aims to help fill this gap by presenting empirical results about experiences of climate change in farming communities in Tamil Nadu, South India, and fishing communities on the Lofoten Islands, Norway. With the help of the five dimensions of affectedness, rhythms and rituals, slow motion, care, and health and well-being, it exemplifies how time matters to issues of climate injustices faced by the communities. The paper promotes a qualitative understanding of time and climate change. Thereby, it may stimulate greater relatability to climate change, as well as discussion likely to lead to conceptual advances. [ABSTRACT FROM AUTHOR]

The increasing impacts of climate change require strategies for climate adaptation. Dynamic Global Vegetation Models (DGVMs) are one type of multi-sectorial impact models with which the effects of multiple interacting processes in the terrestrial biosphere under climate change can be studied. The complexity of DGVMs is increasing as more and more processes, especially for plant physiology, are implemented. Therefore, there is a growing demand for increasing the computational performance of the underlying algorithms as well as ensuring their numerical accuracy. One way to approach this issue is to analyse the routines which have the potential for improved computational efficiency and/or increased accuracy when applying sophisticated mathematical methods. In this paper, the Farquhar-Collatz photosynthesis model under water stress as implemented in the Lund-Potsdam-Jena managed Land DGVM (4.0.002) was examined.We found that the numerical solution of a nonlinear equation, so far solved with the Bisection method, could be significantly improved by using Newton's method instead. The latter requires the computation of the derivative of the underlying function which is presented. Model simulations show a significant lower number of iterations to solve the equation numerically and an overall run time reduction of the model of about 16 % depending on the chosen accuracy. The Farquhar-Collatz photosynthesis model forms the core component in many DGVMs and land-surface models. An update in the numerical solution of the nonlinear equation can therefore be applied to similar photosynthesis models. Furthermore, this exercise can serve as an example for improving computationally costly routines while improving their mathematical accuracy. [ABSTRACT FROM AUTHOR]

The extent of surface water has been changing significantly due to climatic change and human activities. However, it is challenging to capture the interannual changes of inland water bodies due to their high seasonal variation and abrupt change. In this paper, a global annual surface water cover frequency dataset (GLOBMAP SWF) was generated from the MODIS land surface reflectance products during 2000-2020 to describe the seasonal and interannual dynamics of surface water. Surface water cover frequency (SWF) was proposed as the percentage of the time period when a pixel is covered by water in a year. Instead of determination of the water directly, the SWF was estimated indirectly by identifying land observations among annual clear-sky observations to reduce the influence of clouds and variability of water bodies and surface background characteristics, which helps to improve the applicability of the algorithm for different regions across the globe. Regional analysis demonstrates that our estimation results show better performances on frozen water, saline lake, bright surface and cloud-frequent regions compared with the two high-frequent surface water datasets derived from MODIS data. Compared with the high-resolution SWF maps extracted from Sentinel-1 data in four regions that cover lake, river and wetland, the R2 reaches 0.83 to 0.97, RMSE is ranging from 7.24% to 11.28%, and MAE is between 2.07% and 7.15%. In 2020, the area of global maximum surface water extent is 3.38 million km², of which the permanent surface water accounts for approximately 54% (1.83 million km²) and the other 46% is intermittent surface water (1.55 million km²). The area of global maximum and permanent surface water has been shrinking since 2001, with a change rate of -7577 km²/yr and -4315 km2/yr (p < 0.05), respectively; while the intermittent surface water with the SWF above 50% has been expanding (1368 km²/yr, p < 0.01). This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland waters extent with consideration of its seasonal variation. The GLOBMAP SWF data are available at https://doi.org/10.5281/zenodo.6462883 (Liu and Liu, 2022). [ABSTRACT FROM AUTHOR]

This paper aims to study wet-cold compound events (WCCEs) over Greece for the wet and cold season November-April. WCCEs are divided in two different compound events (TX-RR) and (TN-RR) and two different approaches using fixed (RR over 20 mm/day and Temperature under 0 °C) and percentile (RR over 95th and Temperature under 5th) thresholds. Observational data from the Hellenic National Meteorology Service (HNMS) and simulation data from reanalysis and EUROCORDEX models were used in the study for the historical period 1980-2004. Simulation datasets from projection models were employed for the near future period (2025-2049) to study the impact of climate change on the occurrence of WCCEs under RCP 4.5 and 8.5 scenarios. Following data processing and validation of the models, the potential changes in the distribution of WCCEs in the future were investigated based the projected and historical simulations. WCCEs determined by fixed thresholds were mostly found over high altitudes with a future tendency to reduce particularly under RCP 8.5. On the other hand, WCCEs obtained with percentile thresholds, were distributed mostly in Eastern Greece and Crete while their changes differed significantly among models. [ABSTRACT FROM AUTHOR]

Integrated assessment models (IAMs) project future anthropogenic emissions which can be used as input for climate models. However, the full list of climate-relevant emissions is lengthy and most IAMs do not model all of them. Here we present Silicone, an open-source Python package which infers anthropogenic emissions of unmodelled species based on other reported emissions projections. For example, it can infer nitrous oxide emissions in one scenario based on carbon dioxide emissions from that scenario plus the relationship between nitrous oxide and carbon dioxide emissions found in other scenarios. Infilling broadens the range of IAMs available for exploring projections of future climate change, and hence Silicone forms part of the open-source pipeline for assessments of the climate implications of IAM scenarios, led by the Integrated Assessment Modelling Consortium (IAMC). This paper presents a variety of infilling options and outlines their suitability for different cases. We recommend certain infilling techniques as good defaults but emphasise that considering the specifics of the model being infilled will produce better results. We demonstrate the package's utility with three examples: infilling all required gases for a pathway with data for only one emission species, splitting up a Kyoto emissions total into separate gases, and complementing a set of idealised emissions curves to provide a complete, consistent emissions portfolio. The code and notebooks explaining details of the package and how to use it are available on GitHub (https://github.com/GranthamImperial/silicone , last access: 2 November 2020). The repository with this paper's examples and uses of the code to complement existing research is available at https://github.com/GranthamImperial/silicone%5fexamples (last access: 2 November 2020). [ABSTRACT FROM AUTHOR]