Abstract. Stratospheric Aerosol Injection (SAI) is a form of proposed climate intervention to reflect incoming solar radiation, offsetting some of the impacts of greenhouse gas warming. Due to the characteristics of stratospheric circulation, the lifetime of such aerosols, and the differential impacts that different aerosol patterns can produce on surface climate, many possible scenarios of SAI implementations might exist, ranging from steady, cooperative deployments across one or more injection latitudes to highly dynamic, uncoordinated deployments involving multiple independent actors with different aims. However, a full exploration of this scenario space is constrained by the computational cost of fully coupled climate model simulations that are usually used to evaluate the impacts of potential scenarios. Here, we describe the development and evaluation of the Climate Intervention Dynamical EmulatoR (CIDER), a climate emulator that can be used to quickly simulate the response to a SAI deployment on both a regional and a global scale for a set of variables (temperature, precipitation, evaporation, and sea ice fraction) as the injection rates vary in magnitude, latitude, and time. CIDER is trained on a large but finite set of pre-existing Earth System Model (ESM) simulations, but it can emulate novel, out-of-sample scenarios at a small fraction of a cost of one ESM simulation. Because CIDER does not include a representation of how SAI affects the diurnal and seasonal cycles, nor how it affects internal variability, it is not meant to substitute for ESMs, nor to directly inform more detailed impact analyses of SAI. Nevertheless, it can be used to quickly understand the broad impacts of different SAI strategies and produce large sets of different SAI implementations, making it a valuable tool for educational and communication purposes, for rapid identification of scenario parameters prior to simulation in a full ESM, and for coupling with Integrated Assessment Models (IAMs). In this paper, we describe CIDER and its workflow, as well as the process we used to train on existing simulations. We then evaluate the emulator's performance on a novel scenario, simulated using the same climate model used for the training set, but not included in the set, showing that CIDER is capable of emulating outside-the-box scenarios with a high degree of fidelity. The novel scenario we use is an example of a multi-actor, uncoordinated SAI deployment, and thus rather different from the balanced, coordinated scenarios used in the training set and typically simulated for SAI. The code and underlying training set are open source and available for the community to reproduce our results and improve upon them.

The physicochemical properties and atmospheric aging behavior of black carbon (BC) are critical for assessing its climatic impact, yet how these vary across emission sources remains poorly understood. Here, the relationship between the microphysical properties and aging processes of BC subgroups (Char and Soot) from major emission sources, including biomass burning (BB), diesel vehicle exhaust (DV), and industrial coal combustion (ICC) were investigated at single particle level. Our results revealed that BB emissions contained 84 ± 5% Char, significantly higher than DV/ICC sources (17-30%). The monomer diameter (dm) and graphitic interplanar spacing of BB-derived BC were twice of those DV- and ICC-derived BC, and the aggregate diameter (da) and O/C ratio were approximately 10-fold higher, along with a weaker degree of necking. Based on the high-time-resolution sampling analysis of BB processes, we found that lower combustion efficiency favored Char formation, resulting in the increasing of dm, da, and O/C ratio of BC particle. Moreover, under equivalent aging, Soot-dominated DV-BC aggregates grew 50% in da versus only 2% for Char-dominated BB-BC. This work demonstrates that the proportion of Char and Soot determine BC's physicochemical properties and aging behavior in different sources, emphasizing the need for subgroup-specific parametrizations in climate models.

ABSTRACT Gross primary productivity (GPP) has been proven to respond positively but asymmetrically to increases in precipitation, such that the productivity gains in wet periods may be unequal to the productivity declines in dry periods. Climate model projections indicate significant precipitation changes in terms of mean and variability over the coming decades; however, how the changes in precipitation regimes affect the asymmetric GPP responses remains an open question. In this study, the responses of GPP to precipitation changes were assessed by using model simulations under a range of experimental scenarios from 1995 to 2100. In particular, the asymmetry of GPP in response to precipitation was quantified by the asymmetry index (AI), where positive AI suggests larger increases in GPP during wet years and negative AI indicates greater decreases in GPP during dry years. The results showed that: (1) In the historical period of 1995–2014, positive GPP asymmetry occurred in 47.50% of the global vegetated area including central Australia (AI = 0.24), northern Russia (0.04), and southern western Africa (0.03), while negative asymmetry was observed in the Amazon (−0.14), central North America (−0.05), and eastern Europe (−0.03). (2) During the period of 2015–2100, the positive AI in central Australia (northern Russia) is projected to increase (decrease) at a rate of 0.007 decade −1 (−0.013 decade −1 ) under the high emission scenario. (3) In the perspective of different land cover types, clear positive GPP asymmetry is found in shrublands and grasslands during the historical period, and the positive sign is expected to be enhanced in the former but diminished in the latter by the end of the century. The changes in the asymmetric GPP responses to precipitation are suggestive of variations in terrestrial ecosystem carbon uptake and the need for effective ecosystem management strategies.

Abstract Plant wax hydrogen isotopes (δ 2 H) preserved in lake sediments provide valuable insights into past climatic changes. However, lake catchments often experience local shifts in vegetation type that can yield distinct isotopic signatures in the sediments, potentially obscuring hydroclimatic signals. Here, we compile regional plant wax data and test different approaches to correct for the impact of changes in vegetation type over time to isolate precipitation δ 2 H values (δ 2 H prc ) since the Younger Dryas (12.9–11.7 ka) from a sediment record from Rotsee (central Switzerland). Our method intercomparison shows that n ‐alkane relative abundances produced the most accurate δ 2 H prc estimates, agreeing with an independent speleothem fluid‐inclusion δ 2 H record from Milandre Cave. This indicates that sedimentary plant wax δ 2 H values represent a vegetation‐weighted community signal. Precipitation was 2 H‐depleted during the Younger Dryas (∼−75‰), and then δ 2 H prc values increased sharply into the Holocene. During the early Holocene (∼10 ka), δ 2 H prc values of ∼−55‰ were reached, consistent with maximum summer insolation, followed by a gradual long‐term decline toward the present, reflecting Neoglacial cooling and consistent with modern δ 2 H prc values (∼−67‰). Importantly, while plant wax δ 2 H values declined sharply due to deforestation beginning in the Roman period, this impact is effectively corrected for by using the relative abundance of n ‐alkanes. These findings underscore the need for site‐specific vegetation corrections to produce robust hydroclimate reconstructions from plant‐wax isotopes, especially in lakes with small catchments, thereby enhancing comparability of sedimentary records with climate models and deepening our understanding of past climate–vegetation–human interactions.

Climate-connectivity-geography interactions govern the hydrologic vulnerability of geographically isolated wetlands.

Abstract Decision-analytic frameworks under climate uncertainty include Cost-Benefit Analysis (CBA), which maximizes welfare by trading mitigation costs against quantified damages; Cost-Effectiveness Analysis (CEA), used here in its probabilistic form, which minimizes the cost of meeting a predefined temperature target via a chance constraint that accounts for uncertainty when damages cannot be reliably estimated; and Cost-Risk Analysis (CRA), which reinterprets adherence to the temperature target within an unconstrained utility-maximization framework by penalizing the probability of target exceedance via a risk function. This study operationalizes Cost-Benefit-Risk Analysis (CBRA), a novel framework that extends CRA by retaining its risk function while adding an explicit, partial damage function, thereby internalizing quantified impacts and leaving residual, unquantified impacts to be represented by the reduced risk term. In our application, the partial global damage function is derived from a forward-looking, regionally and sectorally disaggregated Computable General Equilibrium (CGE) model. This allows us to assess how much of the precautionary risk embedded in climate targets is captured by explicit economic losses. We implement CBRA in the integrated assessment model MIND, coupling a modified version of the FaIR climate model that accounts for climate sensitivity uncertainty. Our findings reveal that explicit damages from agriculture, labor productivity, and human health explain 58% of the risk captured by a 2 $$^{\circ}$$ C target under a 65% safety level. We demonstrate that when MIND is updated with FaIR, CRA and CEA deliver near-equivalent outcomes (differences of 1.3% in peak emissions, 0.40% in peak temperature, and 0.36% in cumulative emissions), confirming the theoretical equivalence suggested in previous studies. These results suggest that as damage estimates improve, a greater share of precautionary risk is accounted for within cost-benefit models, reducing the need for rigid precautionary targets and narrowing the gap between CBA and CEA. However, uncertainty in climate sensitivity remains a dominant factor, highlighting the need for a more precise understanding of the climate system response to guide policy.

Seed germination is highly temperature sensitive, and increasing global temperatures due to climate change are likely to have a strong effect on agriculture. Improved utilisation of indigenous, arid-resilient crops like fonio (Digitaria exilis) are a commonly proposed solution to improving food security in West Africa. This study develops knowledge of fonio germination requirements and relates them to future predicted climate conditions. We use an interdisciplinary methodology, integrating extensive laboratory-based seed germination experiments under a range of temperatures, with niche suitability and future climate modelling, to investigate trends for how cultivation of fonio may be impacted by climate change. By analysing 37 seed accessions from Guinea, Togo, Mali, and Burkina Faso, we estimated the ceiling temperature for germination of this species to be 43°C (SD=±1.6), with an optimum temperature of 36°C (SD=±2.2) - as also noted from phenotypic observations on seedlings. There is no obvious difference in response by accessions originating from either hotter or cooler climates. By comparing these temperature thresholds to future climate predictions, tested alongside suitability modelling, we see a decline of 7.9-10.45% in the future suitable area for fonio cultivation, depending on the prediction method, especially affecting Senegal, Mali, and Burkina Faso. Newly suitable area is predicted to increase in Guinea, Ghana, Cote d'Ivoire, and Nigeria by 5.5%. Our findings provide valuable insight into the physiology and thermal tolerance of fonio seeds, and identify particularly vulnerable agricultural regions in West Africa which will require additional support. This should include developing future dryland agriculture policies, livelihood projects, and resilient crop varieties.

Impact of climate change on freshwater macronutrients and agricultural yields across Britain.

The interhemispheric thermal contrast, defined as the mean sea surface temperature difference between the northern and southern hemispheres, crucially influences tropical climate. Climate models show a positive interhemispheric thermal contrast trend since 1950, with more warming in the northern hemisphere compared to the southern hemisphere, contradicting the observed negative trend. Here we show this discrepancy stems from models overestimating greenhouse gas responses via wind-evaporation-sea surface temperature feedback, while anthropogenic and natural aerosols combine to produce the negative trend in observations. Consequently, models with high equilibrium climate sensitivity exhibit larger discrepancies with observations. Despite model failure to reproduce the trend, the modeled multidecadal interhemispheric thermal contrast variability aligns with observations, enabling a constrained estimate of effective radiative forcing due to aerosol-cloud interactions of , with a "likely" range 57% narrower than the latest IPCC report. Our study further suggests that future northward shifts of the tropical rain belt are likely to be less pronounced than predicted by climate models with high equilibrium climate sensitivity.

Climate change disrupts the natural water cycle and agriculture, hindering the progress toward achieving sustainable development goals. Employing bias-corrected climate model simulations is crucial for future climate change patterns prediction and informing policy decisions. This research employs a multi-model ensemble from the Coupled Model Intercomparison Project Phase 6 to assess how climate change affects precipitation patterns in the Abaya-Chamo Sub-basin located in southern Ethiopia. Future predicted precipitation datasets were evaluated under Shared Socioeconomic Pathway scenarios. The Climate Data Operators (CDOs) tool was used to interpolate global climate model results. A power transformation method was utilized to address systematic biases in the outputs of the multi-model ensemble. Spatial patterns of precipitation maps in ArcMap were generated using the inverse distance weighting method. The findings revealed that the bias-corrected mean monthly and annual precipitations were lower than the observed precipitations. The SSP2-4.5 scenario forecasted a decrease in mean annual precipitation of 6.6% to 25.85% over the near periods (2021-2064) and a decrease of 2.25% to 20.24% in the long term future (2065-2100). The spring (MAM) season experienced the largest percentage reduction of all seasons. The spatial distribution of mean annual precipitation varied widely across watersheds, ranging from 450 to 1,140 millimeters. The multi-model ensemble projection for precipitation indicates a more significant decrease in the Gidabo watersheds during the summer (JJA) and spring (MAM) seasons, highlighting spatial variability. Projected future precipitation declines are expected to reduce the amount of water available to ecosystems. Therefore, developing comprehensive, effective water resource management strategies is extremely important to adapt to these changes. Keywords: Abaya-Chamo, Bias Correction, CMIP6, Climate Change, Multi-Model Ensemble, Precipitation.

Abstract. Aerosols are a key climate forcer and harmful to human health at the surface. Accurately modeling aerosol optical properties, mass loading and their relationship is important for constraining aerosol-climate forcing and characterizing particulate matter pollution exposure. We investigate the drivers of uncertainties in the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) in simulating aerosols by focusing on the link between aerosol optical properties and mass. We compare a GEOSCCM hindcast with long-term coincident observations including satellite AOD measurements, speciated PM2.5 datasets from observations-model data fusion, and ground-based measurements of aerosol mass and optical properties. We analyze regional trends and seasonal variations of AOD and PM2.5, and surface aerosol properties, including relative humidity's role in hygroscopic enhancement. This work also presents the first extensive assessment of GEOSCCM's aerosol component with observational data. Our findings show that biases in PM2.5 components and relative humidity significantly impact simulated aerosol scattering at the surface, while scattering efficiency assumptions align with observations. This indicates that errors in simulated scattering relate more to simulated aerosol speciated mass and relative humidity than optical properties and size distribution assumptions in GEOSCCM. Our work highlights the importance of relative humidity biases on aerosol scattering enhancement for climate models where meteorology is not prescribed. Findings suggest improvements in GEOSCCM aerosols mass and optical properties could be achieved through updating emission inventories, especially over biomass burning regions, reducing nitrate biases, and improving relative humidity simulation.

Development of co-integrated standardized procedure for the joint monitoring, forecasting and probabilistic characterization of climate extremes under global climate models

Abstract Ensuring food security is a vital challenge. To meet food and, especially, protein demand in the next few decades, the aquaculture industry needs to expand. This could be achieved by expanding marine aquaculture at sea. Moving aquaculture plots further offshore has gained interest due to its increased space availability and more stable conditions compared to coastal areas, while also mitigating the effects of climate change extremes inshore. Spatial multi-criteria evaluation allowed for the identification of regions in offshore European waters that, under present-day conditions, were both feasible and suitable for mussel cultivation ( Mytilus edulis L.). Future climate models were also used and showed a latitudinal trend, making Northern European waters more suitable in the future, while the Southern part of Europe became too warm. However, the future impact of extreme events, such as marine heatwaves, is difficult to predict. In addition, the study identified offshore wind farms with potential for co-location with mussel cultivation, which could help concentrate human uses at sea and reduce the extent of marine areas subject to anthropogenic pressure. With the offshore wind industry expanding rapidly in the future, even more co-location options will become possible.

Abstract Climate models consistently project a weakening Indonesian Throughflow (ITF) under future warming, but its driving mechanisms remain incompletely explained. Prior studies primarily attribute the ITF decline to changes in the Atlantic Meridional Overturning Circulation (AMOC) and Indo‐Pacific winds, but these two processes account for only a fraction of the simulated ITF weakening (50%–100% across models). Here we show that Southern Ocean surface warming and freshening explain this missing fraction. Enhanced dense‐to‐light water mass transformation in the Southern Ocean produces upper‐ocean volume convergence that propagates globally via wave dynamics, slowing the ITF in a manner analogous to the AMOC‐driven impacts. We further quantified the combined AMOC and Southern Ocean contributions and find that these bipolar processes explain, on average, 89% of the projected ITF decline in climate models. Our results reveal critical linkages between high‐latitude climate change and low‐latitude circulation, positioning the ITF as an indicator of global overturning adjustments.

Abstract. Drought risk is changing as the hydrological cycle responds to anthropogenic climate change. Projections of future drought risk used to inform water management would ideally be conducted at local scale, but local-scale projections demand local data and computational resources that are often not available. As an alternative, global-scale projections of glacier runoff and the hydrological cycle can provide important insights for the local scale, particularly when interpreted carefully. Here, we use an ensemble of 11 latest-generation (CMIP6) climate models to force three different global glacier models, and we examine changes in glacial drought buffering for 75 major river basins in the early, mid-, and late 21st century. Glacial drought buffering results are broadly consistent across glacier models. By contrast, we find that the spread in glacial drought buffering among different climate models is large and likely under-sampled compared with the full archive of suitable CMIP6 simulations (123 simulations from 28 models for the SSP2-4.5 scenario). This work highlights that, for downstream hydrological studies: (1) no one global glacier model is more suitable than another, and (2) analysing a representative ensemble of climate models is imperative. Our findings illustrate that differences in glacier model outputs that appear consequential to glaciologists may be less consequential for downstream impact metrics.

Abstract Interactions between tropical Pacific and tropical Atlantic (TP-TA) play a crucial role in tropical Pacific climate variability and predictability on a broad range of timescales. However, the ability of state-of-the-art climate models to simulate these cross-basin interactions remains uncertain. This study systematically evaluates the representation of TP-TA interactions in 34 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). While models generally reproduce the spatial patterns of key tropical climate modes, significant biases are found in their amplitudes, seasonality (particularly for the Equatorial Atlantic mode), and spectral characteristics. Notably, most models substantially underestimate Atlantic impacts on spatiotemporal aspects of El Niño-Southern Oscillation (ENSO). To disentangle the bidirectional coupling mechanisms, we employ a Linear Inverse Model (LIM) that allows to selectively isolate Atlantic-to-Pacific versus Pacific-to-Atlantic coupling. Our analysis reveals two key aspects of TP-TA interactions: (1) internal Atlantic variability enhances Pacific climate variance across interannual and decadal timescales, and (2) Pacific-driven Atlantic variability reduces tropical Pacific low-frequency variability. Although these influences qualitatively agree with observations, their simulated intensities are markedly weaker. Furthermore, we identify considerable inter-model spread in representing TA impacts on TP variability, highlighting persistent challenges in achieving robust model consensus. Our findings underscore the need to improve the representation of TP-TA interactions in climate models, particularly through more realistic simulations of tropical Atlantic dynamics and their seasonal evolution, to make progress in seasonal-to-decadal climate predictions.

Abstract. Photoacoustic spectroscopy (PAS) has become a common method for measuring aerosol absorption and is one of the few techniques capable of directly measuring absorption by suspended aerosol particles at ambient concentrations. When multiple wavelengths are used, PAS provides a way of measuring the absorption Ångström exponent (AAE) and, when combined with a scattering or extinction method, provides a measure of the aerosol single-scattering albedo (SSA), and both AAE and SSA are important parameters in climate models. Despite this utility, few commercial PAS instruments are available, and no multi-wavelength commercial instruments are currently available. Thus, most extant PAS instruments are custom-built and therefore come with considerable cost and development time and require access to machine shops capable of fabricating the needed components. The goal of this work was to provide a blueprint for a low-cost, multi-wavelength PAS for measurement of the aerosol AAE both in the laboratory and in the field. In an effort to create an instrument with a low barrier to entry, we aim to use low-cost, readily available components and use open-source options wherever possible. In this paper, we present the SiMPLE-PAS, a single-pass, multi-wavelength, portable, and low-expense photoacoustic spectrometer that uses low-cost electronics and a 3D-printed cell to meet these design goals. The instrument has a total bill-of-materials cost on the order of USD 500. The instrument is, to the best of our knowledge, the first 3D-printed PAS for aerosols and likely the lowest-cost PAS to date. The instrument performed well in laboratory validation experiments and showed good agreement with measurements of aerosol absorption by the previously developed MultiPAS-IV instrument when co-located at the second Georgia Wildland Fire Simulation Experiment (G-WISE 2) during April 2025. The instrument shows competitive detection limits of 0.63, 1.99, and 0.55 Mm−1 for the blue, green, and red channels (10 min, 2σ), respectively, which will allow it to measure both ambient and laboratory-generated aerosols. The SiMPLE-PAS therefore provides a low-cost, accessible photoacoustic spectrometer that offers to lower the barrier to entry for groups wishing to measure aerosol absorption, whether in the laboratory or in the field.

This article investigates the fluctuations in groundwater level (GWL) and groundwater recharge in response to rainfall variability using 2001–2021 rainfall data from four districts in West Bengal, India: Purulia, Bankura, West Midnapore, and East Midnapore. Purulia and Bankura have a tropical semi-arid to sub-humid type of climate, while West Midnapore and East Midnapore have a tropical wet-dry monsoon climate. The GWL analysis was performed using the Mann-Kendall Test (M-K Test) and Sen’s Slope Esti-mator, while groundwater recharge was estimated using empirical expressions, including the Chaturvedi and Modified Chaturvedi formulas, and the Maxey-Eakin and Krishna Rao methods. We used pre-monsoon and post-monsoon GWL data for the GWL trend analysis and monthly rainfall data for groundwater recharge estimation. To assess the impact of climate change on GWL variation for the 2022–2050 period, the global climatic model (GCM) CanESM5-SSP126 rainfall data was used. The trend analysis of ground¬water level at East Midnapore and West Midnapore shows a higher rate of decline (GWL beyond 15 m bgl) compared to Purulia and Bankura during both the pre- and post-monsoon periods. Groundwater recharge estimates increased linearly with rainfall and the results show the Modified Chaturvedi formula as the most suitable in the selected districts. Additionally, new empirical expressions developed between rainfall and groundwater levels showed a better fit for Bankura and Purulia stations. These expressions suggest that rainfall will increase in Purulia toward 2050, leading to a subsequent rise in groundwater levels.

In an era of climate instability, biodiversity loss and rapid industrial expansion, artificial intelligence (AI) has emerged as a revolutionary technological force capable of addressing complex environmental challenges. This research investigates how AI can be used to support sustainable growth and environmental preservation. Ecological governance, resource efficiency and environmental data accuracy are all improved by Ai-driven innovation like machine learning, remote sensing, autonomous monitoring system, and predictive analytics. The study looks into the use of AI in several significant sectors, such as pollution control, biodiversity monitoring, sustainable agriculture, climate modelling and renewable energy optimization. The advantages and drawbacks of artificial intelligence systems include assessed using a mixed-method strategy that includes case analysis, expert insights, conceptual modelling and secondary data. The findings that AI greatly improves environmental monitoring, lowers carbon emissions and maximizes. The application of natural resources, and supports evidence-based policy choices. However, long-term sustainability requires addressing problems like data bias, energy-intensive computing, unequal access to technology and moral dilemmas. According to the study, AI can be an effective driver of sustainable growth and environmental preservation when used responsibly and inclusively. It also presents a viable route to ecological balance and global resilience.

ABSTRACT Ongoing climate change is leading to considerable alterations of the mean climate, the day‐to‐day weather and their variability. This poses substantial challenges for stakeholders and increases the urgency to develop adequate adaptation and mitigation strategies. In order to represent the local changes in climate across different regions as accurately as possible, and provide useful and usable information for stakeholders, we use an ensemble of convection‐permitting climate simulations to quantify the projected changes for user‐relevant climate indices for Southern and Central Germany. A wide range of temperature, precipitation, and user‐oriented climate indices relevant to various stakeholder applications to address their information requirements are considered. After bias adjustment, the indices are in very good agreement with results using the HYRAS dataset, though small shortcomings remain. Regarding the climate change signal, several different patterns can be identified. For high temperature indices, a significant increase is observed, particularly for very hot days and tropical nights. On the other hand, the number of frost and ice days significantly decreases with global warming. Other indices like hiking days and summer dry days show comparatively small changes. While relative changes are largest for high altitude areas for high temperature indices, their absolute changes are largest for low areas like the Rhine Valley. For high temperature indices, an increase both in mean values and variability is found with global warming. The opposite is true for snow days, ice days and winter service days. We conclude that convection‐permitting simulations can be key to provide usable user‐relevant climate indices for recent climate conditions and projections for future decades, both considering high spatial resolution and uncertainty estimations. Such a database can thus be extremely useful for the development and implementation of informed adaptation measures to climate change, as discussed in detail for specific examples in Germany.