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Atmospheric inverse modeling is the process of estimating emissions from atmospheric observations by minimizing a cost function, which includes a term describing the difference between simulated and observed concentrations. The minimization of this difference is typically limited by uncertainties in the atmospheric transport model rather than by uncertainties in the observations. In this study, we showcase how a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiments using an ensemble Kalman smoother system. We use the estimation of European CH4 emissions from the in situ measurement network as an example, but we also demonstrate the additional benefits for trace gases with more localized sources, such as SF6. The uncertainty in flow-dependent transport is determined using meteorological ensemble simulations that are perturbed by physics and driven at the boundaries by an analysis ensemble from a global meteorology and a CH4 simulation. The impact of direct representation of temporally varying transport uncertainties in atmospheric inversions is then investigated in an observation system simulation experiment framework in various setups and for different flux signals. We show that the uncertainty in the transport model varies significantly in space and time and that it is generally highest during nighttime. We apply inversions using only afternoon observations, as is common practice, but also explore the option of assimilating hourly data irrespective of the hour of day using a filter based on transport uncertainty and taking into account the temporal covariances. Our findings indicate that incorporating flow-dependent uncertainties in inversion techniques leads to more accurate estimates of GHG emissions. Differences between estimated and true emissions could be reduced more effectively by 9 % to 82 %, with generally larger improvements for the SF6 inversion problem and for the more challenging setup with small flux signals. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are linked to severe economic losses and casualties in China every year; hence, exploring the potential mitigation strategies to minimize these events and their changes in frequency and intensity under global warming is of importance, particularly for the populous subregions. In addition to global warming scenarios, this study examines the effects of the potential deployment of stratospheric aerosol injection (SAI) on hydrological extremes in China based on the SAI simulations (G6sulfur) of the Geoengineering Model Intercomparison Project (GeoMIP) by the UK Earth System Model (UKESM1) simulations. G6sulfur is compared with simulations of the future climate under two different emission scenarios (SSP5-8.5 and SSP2-4.5) and a reduction in the solar constant (G6solar) to understand the effect of SAI on extreme precipitation patterns. The results show that under global warming scenarios, precipitation and extreme wet climate events during 2071–2100 are projected to increase relative to the control period (1981–2010) across all the subregions in China. Extreme drought events show a projected increase in southern China. The G6sulfur and G6solar experiments show statistically similar results to those under SSP2-4.5 in extreme precipitation intensities of China in UKESM1. These results are encouraging. The efficacy of SAI in decreasing extreme precipitation events and consecutive wet days is more pronounced than that of G6solar when compared to SSP2-4.5. While both G6sulfur and G6solar show drying at high-latitude regions, which is consistent with our understanding of the spin-down of the hydrological cycle under SRM. Given the limitations of the current model and the small ensemble size, and considering that the hydrological effects are less beneficial than those indicated for temperature, it is recommended that further, more comprehensive research be performed, including using multiple models, to better understand these impacts. [ABSTRACT FROM AUTHOR]

The influence of nonmigrating diurnal tides on Na layer variability in the mesosphere and lower thermosphere regions is investigated for the first time using data from the Optical Spectrograph and InfraRed Imaging System (OSIRIS) on the Odin satellite and Specified Dynamics Whole Atmosphere Community Climate Model (SD-WACCM) with metal chemistry. The Na density from OSIRIS exhibits a clear longitudinal variation indicative of the presence of tidal components. Similar variability is seen in the SD-WACCM result. Analysis shows a significant relationship between the nonmigrating diurnal tides in Na density and the corresponding temperature tidal signal. Below 90 km , the three nonmigrating diurnal tidal components in Na density show a significant positive correlation with the temperature tides. Conversely, the phase mainly indicates a negative correlation above 95 km. Around the metal layer peak, the response of the Na density to a 1 K change in tidal temperature is estimated to be 120 cm-3. [ABSTRACT FROM AUTHOR]

Elongated open-water areas in sea ice (leads) release sea spray particles to the atmosphere. However, there is limited knowledge on the amount, properties and drivers of sea spray emitted from leads, and no existing parameterization of this process is available for use in models. In this work, we use measurements of aerosol fluxes from to produce an estimate of the location, timing and amount of sea spray emissions from leads at the scale of the Arctic Ocean for 1 year. Lead fractions are derived using sea ice data sets from numerical models and satellite detection. The proposed parameterization estimates that leads account for 0.3 %–9.8 % of the annual sea salt aerosol number emissions in the Arctic Ocean regions where sea ice concentration is greater than 80 %. Assuming similar size distributions to those from emissions from the open ocean, leads account for 30 %–85 % of mass emissions in sea ice regions. The total annual mass of sea salt emitted from leads, 0.1–2.1 Tgyr-1 , is comparable to the mass of sea salt aerosol transported above sea ice from the open ocean, according to the MERRA-2 reanalysis. In addition to providing the first estimates of possible upper and lower bounds of sea spray emissions from leads, the conceptual model developed in this work is implemented and tested in the regional atmospheric chemistry model WRF-Chem. Given the estimates obtained in this work, the impact of sea spray from leads on Arctic clouds and radiative budget needs to be further explored. [ABSTRACT FROM AUTHOR]

East Asia has been identified as a key area at risk of precipitation increases resulting from global warming. East Asian summer monsoon has distinct spatiotemporal characteristics and the simulation characteristics of global climate models therefore needs to be evaluated closely to obtain a reliable projection of future precipitation patterns and associated extreme events. Using metrics for the spatiotemporal variability of monsoon precipitation over East Asia, this study evaluated the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) models and analyzed future projections and uncertainty factors. Spatiotemporal precipitation patterns were simulated reasonably well by CMIP6 models but with weaker rainfall amplitudes. CMIP6 models simulated more intense precipitation than the predecessor CMIP5 ones and captured observations better. In future projections, an overall precipitation increase occurs during both the northward migration of the rain band and during peak monsoon time over East Asia and the three subregions, with stronger changes under higher emission scenarios. This increase was mainly ascribed to a thermodynamic response due to increased moisture availability. Internal climate variability and model uncertainty dominated future precipitation uncertainties. Dynamic terms explained a large portion of model uncertainty due to circulation changes, and thermodynamic terms were significantly related to scenario uncertainty. [ABSTRACT FROM AUTHOR]

Earth System Models (ESMs) are intricate models employed for simulating the Earth's climate, typically constructed from distinct independent components dedicated to simulate specific natural phenomena (such as atmosphere and ocean dynamics, atmospheric chemistry, land and ocean biosphere, etc.). In order to capture the interactions between these processes, ESMs utilize coupling libraries, which oversee the synchronization and field exchanges among independent developed codes typically operating in parallel as a Multi-Program, Multi-Data (MPMD) application. The performance achieved depends on the coupling approach, as well as on the number of parallel resources and scalability properties of each component. Determining the appropriate number of resources to use for each component in coupled ESMs is crucial for efficient utilization of the High Performance Computing (HPC) infrastructures used in climate modelling. However, this task traditionally involves manual testing of multiple process allocations by trial and error, requiring significant time investment from researchers. Thus, making the process more error-prone, and often resulting in a loss in application performance due to the complexity of the task. This paper introduces the automatic load-balance tool (auto-lb), a methodology and tool for determining the resource allocation to each component within coupled ESMs, aimed at improving the application's performance. Notably, this methodology is automatic and does not require expertise in HPC to improve the performance achieved by coupled ESMs. This is accomplished by minimizing the load-imbalance: reducing each constituent's execution cost (core-hours), as well as minimizing the core-hours wasted resulting from the synchronizations between them, without penalizing the execution speed of the entire model. This optimization is achieved regardless of the scalability properties of each constituent and the complexity of their dependencies during the coupling. To achieve this, we designed a new performance metric called 'Fittingness' to assess the performance of coupled execution evaluating the trade-off between the parallel efficiency and application throughput. This metric is intended for scenarios where optimality can depend on various criteria and constraints. Aiming for maximum speed might not be desirable if it leads to a decrease in parallel efficiency and, therefore, increasing the computational costs of simulation. The methodology was tested across multiple experiments using the widely recognized European ESM, EC-Earth3. The results were compared with real operational configurations, such as those used for the Coupled Model Intercomparison Project Phase 6 (CMIP6) and for the European Climate Prediction Project (EUCP), and validated on different HPC platforms. All of them suggest that the current approaches lead to performance loss, and that auto-lb can achieve better results in both, execution speed and reduction of the core-hours needed. When comparing to the EC-Earth standard-resolution CPMIP6 runs, we achieved a configuration 4.7 % faster while also reducing the core-hours required by 1.3 %. Likewise, when compared to the EC-Earth high-resolution EUCP runs, the method presented showed an improvement of 34 % in the speed, with a 6.7 % reduction in the core-hours consumed. [ABSTRACT FROM AUTHOR]

Accurate and detailed retrieval of global horizontal irradiance (GHI) has many benefits, for instance, in support of the energy transition towards an energy supply with a high share of renewable energy sources and for validating high-resolution weather and climate models. In this study, we apply a downscaling algorithm that combines the high-resolution visible and standard-resolution channels on board the Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) to obtain cloud physical properties and GHI at an increased nadir spatial resolution of 1 km × 1 km instead of 3 km × 3 km. We validate the change in accuracy of the high-resolution GHI in comparison to the standard-resolution product against ground-based observations from a unique network of 99 pyranometers deployed during the HOPE field campaign in Jülich, Germany, from 18 April to 22 July 2013. Over the entire duration of the field campaign, a small but statistically significant reduction in root mean square error (RMSE) of 2.8 Wm-2 is found for the high-resolution GHI at a 5 min scale. The added value of the increased spatial resolution is largest on days when GHI fluctuates strongly: for the 10 most variable days a significant reduction in the RMSE of 7.9 Wm-2 is obtained with high- versus standard-resolution retrievals. In contrast, we do not find significant differences between both resolutions for clear-sky and fully overcast days. The sensitivity of these results to temporal- and spatial-averaging scales is studied in detail. Our findings highlight the benefits of spatially dense network observations as well as a cloud-regime-resolved approach for the validation of GHI retrievals. We also conclude that more research is needed to optimally exploit the instrumental capabilities of current advanced geostationary satellites in terms of spatial resolution for GHI retrieval. [ABSTRACT FROM AUTHOR]

The aerosol particles serving as cloud condensation and ice nuclei contribute to key cloud processes associated with cold-air outbreak (CAO) events but are poorly constrained in climate models due to sparse observations. Here we retrieve aerosol number size distribution modes from measurements at Andenes, Norway, during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) and at Zeppelin Observatory, approximately 1000 km upwind from Andenes at Svalbard. During CAO events at Andenes, the sea-spray-mode number concentration is correlated with strong over-ocean winds with a mean of 8±4 cm−3 that is 71 % higher than during non-CAO conditions. Additionally, during CAO events at Andenes, the mean Hoppel minimum diameter is 6 nm smaller than during non-CAO conditions, though the estimated supersaturation is lower, and the mean number concentration of particles that likely activated in-cloud is 109±61 cm−3 with no statistically significant difference from the non-CAO mean of 99±66 cm−3. For CAO trajectories between Zeppelin Observatory and Andenes, the upwind-to-downwind change in number concentration is the largest for the accumulation mode with a mean decrease of 93±95 cm−3, likely attributable primarily to precipitation scavenging. These characteristic properties of aerosol number size distributions during CAO events provide guidance for evaluating CAO aerosol–cloud interaction processes in models. [ABSTRACT FROM AUTHOR]

The low-level jet (LLJ) and the urban heat island (UHI) are common nocturnal phenomena. While the UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) initiative in the Paris region, continuous profiles of horizontal wind speed and vertical velocity were recorded with two Doppler wind lidars (DWLs) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site, highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼ 10 h, the mean wind speed is 9 m s -1 , and the average core height is 400 m above the city. The temporal evolution of many events shows signatures that indicate that the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The horizontal wind shear below the LLJ core induces variance in the vertical velocity (σw2) above the urban canopy layer. It is shown that σw2 is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing σw2 and strong UHI values only occur when σw2 is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health. [ABSTRACT FROM AUTHOR]

Given the long response time of ice sheets, simulations of the Greenland ice sheet typically exceed the availability of input climate data to reliably simulate the fast processes underlying surface mass balance. Strong feedback processes are known to make the mass balance sensitive to inter- and intra-annual variability. Even simulations with climate models do not always cover the full period of interest, motivating bridging these gaps using relatively coarsely resolved climate reconstructions or temporal interpolation methods. However, both of these approaches usually only provide information about the climatological average but not variability. We investigate how this simplification impacts the surface mass balance using the BErgen Snow SImulator. The model was run for up to 500 years using the same atmospheric climatology but different synthetic variabilities. While changing inter-annual variations has an impact of less than 5 % on the surface mass balance of the Greenland ice sheet, neglecting intra-annual variability by using a daily climatology causes a 40 % change in mass balance. Decomposing the total effect into contributions from different input variables, the biggest contributor is precipitation followed by temperature. Using a daily climatology, a small amount of snowfall every day overestimates the albedo and thus surface mass balance (SMB). We propose a correction that re-captures the effect of intermittent precipitation, reducing the SMB overestimation to 15 %–25 %. We conclude that simulations of the Greenland surface mass and energy balance should be forced with a transient climate, in particular for models that are calibrated with transient data. [ABSTRACT FROM AUTHOR]

The Laurentian Great Lakes significantly influence the climate of the Midwest and Northeast United States, due to their vast thermal inertia, moisture source potential, and unique heat and moisture flux dynamics. This study presents a newly developed coupled lake-ice-atmosphere (CLIAv1) modeling system for the Great Lakes by coupling the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) regional climate model (RCM) with the three-dimensional (3D) Finite Volume Community Ocean Model (FVCOM) and investigates the impact of coupled dynamics on simulating the Great Lakes' winter climate. By integrating 3D lake hydrodynamics, CLIAv1 addresses the limitations of traditional one-dimensional (1D) lake and demonstrates superior performance in reproducing observed LSTs, ice cover distribution, and the vertical thermal structure of the Great Lakes compared to the NU-WRF model coupled with the default 1D Lake Ice Snow and Sediment Simulator (LISSS). CLIAv1 also enhances simulation of over-lake atmospheric conditions, including air temperature, wind speed, and sensible and latent heat fluxes, underscoring the importance of resolving complex lake dynamics for reliable climate projections. More importantly, this study addresses the crucial question about what are the key processes influencing lake thermal structure and ice cover that are missed by 1D lake models but effectively captured by 3D lake models. Through process-oriented numerical experiments, we identify key 3D hydrodynamic processes – ice transport, heat advection, and shear production in turbulence – that explain the superiority of 3D lake models over 1D lake models, particularly in cold season performance and lake-atmosphere interactions. Properly resolving these processes using 3D hydrodynamic model is crucial for successfully simulating the lake-ice-atmosphere coupled Great Lakes winter system. This research underscores the necessity of incorporating 3D hydrodynamic models in RCMs to improve our predictive understanding of the Great Lakes' response to climate change. The findings advocate for a shift towards high-resolution, physics-based modeling approaches to ensure accurate future climate and limnological projections for large freshwater systems. [ABSTRACT FROM AUTHOR]

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247599 (Cheng et al., 2024). [ABSTRACT FROM AUTHOR]

The mid-Holocene climate of Australia and the equatorial tropics of the Indonesian–Australian monsoon region is investigated using the Community Earth System Model (CESM) and the Weather Research and Forecasting (WRF) model. Each model is used to simulate the pre-industrial (1850) and the mid-Holocene (6000 years before 1950) climate. The results of these four simulations are compared to existing bioclimatic modelling of temperature and precipitation. The finer-resolution WRF simulations reduce the bias between the model and bioclimatic data results for three of the four variables available in the proxy data set. The model results show that temperatures over southern Australia at the mid-Holocene and in the pre-industrial period were similar, and temperatures were slightly warmer during the mid-Holocene over northern Australia and into the tropics compared to the pre-industrial. During the mid-Holocene, precipitation was generally reduced over northern Australia and in the Indonesian–Australian monsoon region, particularly during summertime. The results highlight the improved value of using finer-resolution models such as WRF to simulate the palaeoclimate. [ABSTRACT FROM AUTHOR]

Ice pellets can form when supercooled raindrops collide with small ice particles that can be generated through secondary ice production processes. The use of atmospheric models that neglect these collisions can lead to an overestimation of freezing rain. The objective of this study is therefore to understand the impacts of collisional freezing and secondary ice production on simulations of ice pellets and freezing rain. We studied the properties of precipitation simulated with the Predicted Particle Properties (P3) microphysics scheme for two distinct secondary ice production processes. Improvements to the representation of ice pellets and ice crystals in P3 were analyzed by simulating an ice pellet storm that occurred over eastern Canada in January 2020. Those simulations showed that adding secondary ice production processes increased the accumulation of ice pellets but led to unrealistic size distributions of precipitation particles. Realistic size distributions of ice pellets were obtained by modifying the collection of raindrops by small ice particles and the merging criteria of ice categories in P3. [ABSTRACT FROM AUTHOR]

Global-mean surface temperature rapidly increased 0.29 ± 0.04 K from 2022 to 2023. Such a large interannual global warming spike is not unprecedented in the observational record, with a previous instance occurring in 1976–1977. However, why such large global warming spikes occur is unknown, and the rapid global warming of 2023 has led to concerns that it could have been externally driven. Here we show that climate models that are subject only to internal variability can generate such spikes, but they are an uncommon occurrence (p = 1.6 % ± 0.1 %). However, when a prolonged La Niña immediately precedes an El Niño in the simulations, as occurred in nature in 1976–1977 and 2022–2023, such spikes become much more common (p = 10.3 % ± 0.4 %). Furthermore, we find that nearly all simulated spikes (p = 88.5 % ± 0.3 %) are associated with El Niño occurring that year. Thus, our results underscore the importance of the El Niño–Southern Oscillation in driving the occurrence of global warming spikes such as the one in 2023, without needing to invoke anthropogenic forcing, such as changes in atmospheric concentrations of greenhouse gases or aerosols, as an explanation. [ABSTRACT FROM AUTHOR]

Recent analyses show the importance of methane shortwave absorption, which many climate models lack. In particular, Allen et al. (2023) used idealized climate model simulations to show that methane shortwave absorption mutes up to 30 % of the surface warming and 60 % of the precipitation increase associated with its longwave radiative effects. Here, we explicitly quantify the radiative and climate impacts due to shortwave absorption of the present-day methane perturbation. Our results corroborate the hypothesis that present-day methane shortwave absorption mutes the warming effects of longwave absorption. For example, the global mean cooling in response to the present-day methane shortwave absorption is -0.10±0.07 K, which offsets 28 % (7 %–55 %) of the surface warming associated with present-day methane longwave radiative effects. The precipitation increase associated with the longwave radiative effects of the present-day methane perturbation (0.012±0.006 mm d -1) is also muted by shortwave absorption but not significantly so (-0.008±0.009 mm d -1). The unique responses to methane shortwave absorption are related to its negative top-of-the-atmosphere effective radiative forcing but positive atmospheric heating and in part to methane's distinctive vertical atmospheric solar heating profile. We also find that the present-day methane shortwave radiative effects, relative to its longwave radiative effects, are about 5 times larger than those under idealized carbon dioxide perturbations. Additional analyses show consistent but non-significant differences between the longwave versus shortwave radiative effects for both methane and carbon dioxide, including a stronger (negative) climate feedback when shortwave radiative effects are included (particularly for methane). We conclude by reiterating that methane remains a potent greenhouse gas. [ABSTRACT FROM AUTHOR]

Windstorms affecting Europe are among the natural hazards with the largest socio-economic impacts. Therefore, many sectors like society, the economy, or the insurance industry are highly interested in reliable information on associated impacts and losses. In this study, we compare – for the first time – estimated windstorm losses using a simplified meteorological loss index (LI) with losses obtained from a complex insurance loss (catastrophe) model, namely the European Windstorm Model of Aon Impact Forecasting. To test the sensitivity of LI to different meteorological input data, we furthermore contrast LI based on the reanalysis dataset ERA5 and its predecessor ERA-Interim. We focus on similarities and differences between the datasets in terms of loss values and storm rank for specific historical storm events in the common reanalysis period across 11 European countries. Our results reveal higher LI values for ERA5 than for ERA-Interim for all of Europe (by roughly a factor of 10), coming mostly from the higher spatial resolution in ERA5. The storm ranking is comparable for western and central European countries for both reanalyses, confirmed by high correlation values between 0.6 and 0.89. Compared to the Aon Impact Forecasting model, LI ERA5 shows comparable storm ranks, with correlation values ranging between 0.45 and 0.8. In terms of normalized loss, LI exhibits overall lower values and smaller regional differences. Compared to the market perspective represented by the insurance loss model, LI seems to have particular difficulty in distinguishing between high-impact events at the tail of the wind gust distribution and moderate-impact events. Thus, the loss distribution in LI is likely not steep enough, and the tail is probably underestimated. Nevertheless, it is an effective index that is suitable for estimating the impacts of storm events and ranking storm events, precisely because of its simplicity. [ABSTRACT FROM AUTHOR]

Dimethyl sulfide (DMS) is a naturally emitted trace gas that can affect the Earth's radiative budget by changing cloud albedo. Most atmospheric models that represent aerosol processes depend on regional or global distributions of seawater DMS concentrations and sea–air flux parameterizations to estimate its emissions. In this study, we analyse the differences between three estimations of seawater DMS, one of which is an observation-based interpolation method following Hulswar et al. (2022) (hereafter referred to as H22) and two of which are proxy-based parameterization methods following Galí et al. (2018) (hereafter referred to as G18) and Wang et al. (2020a) (hereafter referred to as W20). The interpolation-based method depends on the distribution of observations and the methods used to fill data between observations, while the parameterization-based methods rely on establishing a relationship between DMS and environmental parameters such as chlorophyll a , mixed-layer depth, nutrients, sea surface temperature, etc., which can then be used to predict DMS concentrations. On average, the interpolation-based methods show higher DMS values compared to the parameterization-based methods. Even though the interpolation method shows higher values than the parameterization-based methods, it fails to capture mesoscale variability. The regression-based parameterization method (G18) shows the lowest values compared to other estimations, especially in the Southern Ocean, which is the high-DMS region in austral summer. The parameterization-based methods suggest positive long-term trends in seawater DMS (3.82±0.79 % per decade for G18 and 2.13±0.32 % per decade for W20). Since large differences, often more than 100 %, are observed between the different estimations of seawater DMS, the derived sea–air fluxes and, hence, the impact of DMS on the radiative budget are sensitive to the estimate used. [ABSTRACT FROM AUTHOR]

Lightning is an important atmospheric process for generating reactive nitrogen, resulting in the production of tropospheric ozone, as well as igniting wildland fires, which result in potentially large emissions of many pollutants and short-lived climate forcers. Lightning is also expected to change in frequency and location with the changing climate. As such, lightning is an important component of Earth system models. Until now, the Canadian Earth System Model (CanESM) did not contain an interactive-lightning parameterization. The fire parameterization in CanESM5.1 was designed to use prescribed monthly climatological lightning. In this study, we have added a logistical regression lightning model that predicts lightning occurrence interactively based on three environmental variables and their interactions in CanESM5.1's atmospheric model, CanAM5.1 (Canadian Atmospheric Model), creating the capacity to interactively model lightning, allowing for future projections under different climate scenarios. The modelled lightning and resulting burned area were evaluated against satellite measurements over the historical period, and model biases were found to be acceptable. Modelled lightning had a small negative bias and excellent land–ocean ratio compared to satellite measurements. The modified version of CanESM5.1 was used to simulate two future climate scenarios (SSP2-4.5 and SSP5-8.5; Shared Socioeconomic Pathway) to assess how lightning and burned area change in the future. Under the higher-emissions scenario (SSP5-8.5), CanESM5.1 predicts almost no change to the global mean lightning flash rate by the end of the century (2081–2100 vs. 2015–2035 average). However, there are substantial regional changes to lightning – particularly over land – such as a mean increase of 6 % in the northern mid-latitudes and decrease of -8 % in the tropics. By the century's end, the change in global total burned area with prescribed climatological lightning was about 2 times greater than that with interactive lightning (42 % vs. 26 % increase, respectively). Conversely, in the northern mid-latitudes the use of interactive lightning resulted in 3 times more burned area compared to that with unchanging lightning (48 % vs. 16 % increase, respectively). These results show that the future changes to burned area are greatly dependent on a model's lightning scheme, both spatially and overall. [ABSTRACT FROM AUTHOR]

Using the regionally refined mesh (RRM) configuration of the US Department of Energy's Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM), we simulate and evaluate four meteorologically distinct atmospheric river events over California. We test five different RRM configurations, each differing in terms of the areal extent of the refined mesh and the resolution (ranging from 800 m to 3.25 km). We find that SCREAM RRM generally has a good representation of the AR-generated precipitation in CA, even for the control simulation which has a very small 3 km refined patch, and is able to capture the fine-scale regional distributions that are controlled largely by the fine-scale topography of the state. It is found that SCREAM generally has a wet bias over topography, most prominently over the Sierra Nevada mountain range, with a corresponding dry bias on the lee side. We find that refining the resolution beyond 3 km (specifically 1.6 km and 800 m) has virtually no benefit towards reducing systematic precipitation biases but that improvements can be found when increasing the areal extent of the upstream refined mesh. However, these improvements are relatively modest and only realized if the size of the refined mesh is expanded to the scale where employing RRM no longer achieves the substantial cost benefit it was intended for. [ABSTRACT FROM AUTHOR]

Under the Paris Agreement, countries report their anthropogenic greenhouse gas emissions in national inventories, which are used to track progress towards mitigation goals, but they must be independently verified. Atmospheric observations of CO2 , interpreted using inverse methods, can potentially provide that verification. Conventional CO2 inverse methods infer natural CO2 fluxes by subtracting a priori estimates of fuel combustion from the a posteriori net CO2 fluxes, assuming that a priori knowledge for combustion emissions is better than for natural fluxes. We describe an inverse method that uses measurements of CO2 and carbon monoxide (CO), a trace gas that is co-emitted with CO2 during combustion, to report self-consistent combustion emissions and natural fluxes of CO2. We use an ensemble Kalman filter and the GEOS-Chem atmospheric transport model to explore how satellite observations of CO and CO2 collected by the TROPOspheric Monitoring Instrument (TROPOMI) and Orbiting Carbon Observatory-2 (OCO-2), respectively, can improve understanding of combustion emissions and natural CO2 fluxes across the UK and mainland Europe in 2018–2021. We assess the value of using satellite observations of CO2 , with and without CO, above what is already available from the in situ network. Using CO2 satellite observations leads to small corrections to a priori emissions that are inconsistent with in situ observations, due partly to the insensitivity of the atmospheric CO2 column to CO2 emission changes. When we introduce satellite CO observations, we find better agreement with our in situ inversion and a better model fit to atmospheric CO2 observations. Our regional mean a posteriori combustion CO2 emission ranges from 4.6–5.0 Gta-1 (1.5 %–2.4 % relative standard deviation), with all inversions reporting an overestimate for Germany's wintertime emissions. Our national a posteriori CO2 combustion emissions are highly dependent on the assumed relationship between CO2 and CO uncertainties, as expected. Generally, we find better results when we use grid-scale-based a priori CO2:CO uncertainty estimates rather than a fixed relationship between the two species. [ABSTRACT FROM AUTHOR]

The sudden leaks from the Nord Stream gas pipelines, which began on 26 September 2022, released a substantial amount of methane (CH4) into the atmosphere. From the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard EUMETSAT's MetOp-B, we document the first satellite-based retrievals of column-average CH4 (XCH4) that clearly show the large CH4 plume emitted from the pipelines. The data display elevations greater than 200 ppb (parts per billion, ∼ 11 %) above observed background values (1882 ± 21 ppb). Based on the IASI data, together with an integrated mass enhancement technique and formal model-based inversions applied for the first time to thermal infrared satellite methane plume data, we quantify the total mass of CH4 emitted into the atmosphere during the first 2 d of the leaks to be 219–427 GgCH4. Substantial temporal heterogeneity is displayed in our model-derived flux rate, with three or four distinct peaks in emission rate over the first 2 d. Our range overlaps with other previous estimates, which were 75–230 GgCH4 and were mostly based on inversions that assimilated in situ observations from nearby tower sites. However, our derived values are generally larger than those previous results, with the differences likely due to the fact that our results are the first to use satellite-based observations of XCH4 from the days following the leaks. We incorporate multiple satellite overpasses that monitored the CH4 plume as it was transported across Scandinavia and the North Sea up to the evening of 28 September 2022. We produced model simulations of the atmospheric transport of the plume using the Eulerian atmospheric transport model, TOMCAT, which show good representation of the plume location in the days following the leaks. The performance of simulated CH4 mixing ratios at four nearby in situ measurement sites compared to the observed in situ values is mixed, which highlights the challenges inherent in representing short-term plume movement over a specific location using a model such as TOMCAT with a relatively coarse Eulerian grid. Our results confirm the leak of the Nord Stream pipes to clearly be the largest individual fossil-fuel-related leak of CH4 on record, greatly surpassing the previous largest leak (95 GgCH4) at the Aliso Canyon gas facility in California in 2015–2016. [ABSTRACT FROM AUTHOR]

As an important source of sub-micrometer particles, atmospheric new particle formation (NPF) has been observed in various environments. However, most studies provide little more than snapshots of the NPF process due to their underlying observations being limited in space and time. To obtain statistically relevant evidence on NPF across various environments, we investigated the characteristics of NPF based on a 5-year dataset of the German Ultrafine Aerosol Network (GUAN). The results were also compared with observations in previous studies, with the aim to depict a relatively complete picture of NPF in central Europe. The highest NPF occurrence frequency was observed in regional background sites, with an average of about 19 %, followed by urban background (15 %), low-mountain-range (7 %), and high Alpine (3 %) sites. The annual mean growth rate between 10 and 25 nm varied from 3.7–4.7 nm h -1 , while the formation rate with same size range 10–25 nm from 0.4 to 2.9 cm -3 s -1. The contribution of NPF to ultrafine particles (UFPs) was about 13 %, 21 %, and 7 % for the urban background, regional background, and low mountain range, respectively. The influence of NPF on cloud condensation nuclei (CCN) number concentration and the aerosol extinction coefficient for NPF days was the highest in mountainous areas. These findings underscore the importance of local environments when assessing the potential impact of NPF on regional climate in models, and they also emphasize the usefulness of a long-term aerosol measurement network for understanding the variation in NPF features and their influencing factors over a regional scale. [ABSTRACT FROM AUTHOR]

Radiative transfer is a 3D process, but most atmospheric models consider radiation only in the vertical direction for computational efficiency. This results in inaccurate surface radiation fields, as the horizontal transport of radiation is neglected. Previous work on 3D radiative effects mainly used 3D radiative transfer uncoupled from the flow solver. In contrast, our current work uses 3D radiative transfer coupled to the flow solver to study its impact on the development of clouds and the resulting impact on the domain-averaged surface solar irradiance. To this end, we performed a series of realistic large-eddy simulations with MicroHH. To improve the level of realism of our radiation, we first included the direct effect of aerosols using aerosol data from the Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis. Next, we performed simulations with 1D radiative transfer and with a coupled ray tracer for 12 d on which shallow cumulus clouds formed over Cabauw, the Netherlands. In general, simulations with the coupled ray tracer have a higher domain-averaged liquid water path, larger clouds, and similar cloud cover compared to simulations with 1D radiative transfer. Furthermore, the domain-averaged direct radiation is decreased with 3D radiative transfer, and the diffuse radiation is increased. However, the average difference in global radiation is less than 1 W m -2 , as the increase in global radiation from uncoupled 3D radiative transfer is counterbalanced by a decrease in global radiation caused by changes in cloud properties. [ABSTRACT FROM AUTHOR]

Fungal spore organic aerosol emissions have been recognised as a significant source of particulate matter as PM10 ; however, they are not widely considered in current air quality models. In this work, we have implemented the parameterisation of fungal spore organic aerosol (OA) emissions introduced by Heald and Spracklen (2009) (H&S) and further modified by Hoose et al. (2010) in the CHIMERE regional chemistry-transport model. This simple parameterisation is based on two variables, leaf area index (LAI) and specific humidity. We have validated the geographical and temporal representativeness of this parameterisation on a large scale by using yearly polyol observations and primary biogenic organic aerosol factors from positive matrix factorisation (PMF) analysis at 11 French measurement sites. For a group of sites in northern and eastern France, the seasonal variation of fungal spore emissions, displaying large summer and small winter values, is correctly depicted. However, the H&S parameterisation fails to capture fungal spore concentrations for a smaller group of Mediterranean sites with less data availability in terms of both absolute values and seasonal variability, leading to strong negative biases, especially during the autumn and winter seasons. Two years of CHIMERE simulations with the H&S parameterisation have shown a significant contribution of fungal spore OA to PM10 mass, which is lower than 10 % during winter and reaches up to 20 % during summer in high-emission zones, especially over large forested areas. In terms of contributions to organic matter (OM) concentrations, the simulated fungal spore contribution in autumn is as high as 40 % and reaches at most 30 % of the OM for the other seasons. As a conclusion, the fungal spore OA contribution to the total OM concentrations is shown to be substantial enough to be considered a major PM10 fraction and should then be included in state-of-the-art chemistry-transport models. [ABSTRACT FROM AUTHOR]

A comprehensive characterization of drought's impact on biogenic volatile organic compound (BVOC) emissions is essential for understanding atmospheric chemistry under global climate change, with implications for both air quality and climate model simulation. Currently, the effects of drought on BVOC emissions are not well characterized. Our study aims to test (i) whether instantaneous changes in meteorological conditions can serve as a better proxy for drought-related changes in BVOC emissions compared to the absolute values of the meteorological parameters, as indicated by previous BVOC mixing-ratio measurements and (ii) the impact of a plant under drought stress receiving a small amount of precipitation on BVOC emission rate, and on the manner in which the emission rate is influenced by meteorological parameters. To address these objectives, we conducted our study during the warm and dry summer conditions of the eastern Mediterranean region, focusing on the impact of drought on BVOC emissions from natural vegetation. Specifically, we conducted branch-enclosure sampling measurements in Ramat Hanadiv Nature Park, under natural drought and after irrigation (equivalent to 5.5–7 mm precipitation) for six selected branches of Phillyrea latifolia, the highest BVOC emitter in this park, in September–October 2020. The samplings were followed by gas chromatography–mass spectrometry analysis for BVOC identification and flux quantification. The results corroborate the finding that instantaneous changes in meteorological parameters, particularly relative humidity (RH), offer the most accurate proxy for BVOC emission rates under drought compared to the absolute values of either temperature (T) or RH. However, after irrigation, the correlation of the detected BVOC emission rate with the instantaneous changes in RH became significantly more moderate or even reversed. Our findings highlight that under drought, the instantaneous changes in RH and to a lesser extent in T are the best proxy for the emission rate of monoterpenes (MTs) and sesquiterpenes (SQTs), whereas under moderate drought conditions, T or RH serves as the best proxy for MT and SQT emission rate, respectively. In addition, the detected emission rates of MTs and SQTs increased by 150 % and 545 %, respectively, after a small amount of irrigation. [ABSTRACT FROM AUTHOR]

The Priestley–Taylor (PT) coefficient (α) is generally set as a constant value or is fitted as an empirical function of environmental variables, and it can bias the evaporation estimation or hydrological projections under global warming. By using an atmospheric boundary layer model, this study derives a theoretical and parameter-free equation for estimating α as a function of air temperature (T) and specific humidity (Q). With observations from several waterbodies and non-water-limited land sites, we demonstrate that, in addition to estimating the value of α well, the derived expressions can also capture the sensitivity of α to T and Q , that is, dα/dT and dα/dQ. α is generally negatively associated with T and Q , in which regard T plays a more fundamental role in controlling α behaviors. Based on climate model data, we further show that this negative relationship between α and T is of great importance for long-term hydrological predictions. We also provide a lookup graph for practical and broad uses to directly find the values of dα/dT and dα/dQ under specific conditions. Overall, the derived expression gives a physically clear and straightforward approach to quantify changes in α , which is essential for PT-based hydrological simulation and projections. [ABSTRACT FROM AUTHOR]

Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its global stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023), provides a consolidated synthesis of CH 4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use updated national GHG inventories (NGHGIs) reported by Annex I parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available biennial update reports (BURs) reported by non-Annex I parties. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH 4 emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH 4 emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH 4 yr -1) and the mean of the BU CH 4 emissions (17.8 (16–19) Tg CH 4 yr -1) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH 4 yr -1 and 24 (22–25) Tg CH 4 yr -1 for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH 4 yr -1 (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH 4 estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH 4 pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH 4 emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at 10.5281/zenodo.12818506 (Petrescu et al., 2024). [ABSTRACT FROM AUTHOR]

A newly available radiative flux dataset specifically designed to enable the evaluation of the diurnal cycle in top-of-atmosphere (TOA) fluxes as captured by climate and Earth system models is presented. Observations over the period 2007–2012 made by the Geostationary Earth Radiation Budget (GERB) instrument are used to derive monthly hourly mean outgoing longwave radiation (OLR) and reflected shortwave (RSW) fluxes on a regular 1° latitude–longitude grid covering approximately 60° N–60° S and 60° E–60° W. The impact of missing data is evaluated in detail, and a data-filling solution is implemented using estimates of broadband fluxes from the Spinning Enhanced Visible and Infrared Imager flying on the same Meteosat platform, scaled to the GERB observations. This relatively simple approach is shown to deliver an approximate improvement by a factor of 10 in both the bias caused by missing data and the associated variability in the error. To demonstrate the utility of this V1.1 filled GERB Observations for Climate Model Intercomparison Projects (Obs4MIPs) dataset, comparisons are made to radiative fluxes from two climate configurations of the Hadley Centre's Global Environmental Model: HadGEM3-GC3.1 and HadGEM3-GC5.0. Focusing on marine stratocumulus and deep convective cloud regimes, diurnally resolved comparisons between the models and observations highlight discrepancies between the model configurations in terms of their ability to capture the diurnal amplitude and the phase in TOA fluxes, details that cannot be diagnosed by comparisons at lower temporal resolutions. For these cloud regimes the GC5.0 configuration shows improved fidelity to the observations relative to GC3.1, although notable differences remain. The V1.1 filled GERB Obs4MIPs monthly hourly TOA fluxes are available from the Centre for Environmental Data Analysis, with the OLR fluxes accessible at https://doi.org/10.5285/90148d9b1f1c40f1ac40152957e25467 (Bantges et al., 2023a) and the RSW fluxes accessible at https://doi.org/10.5285/57821b58804945deaf4cdde278563ec2 (Bantges et al., 2023b). [ABSTRACT FROM AUTHOR]

Rising global sea levels are one of many impacts associated with current anthropogenic global warming. The Antarctic Ice Sheet (AIS) has the potential to contribute several meters of sea level rise over the next few centuries. To predict future sea level rise contributions from ice sheets, both global and regional climate model (RCM) outputs are used as forcing in ice sheet model simulations. While the impact of different global models on future projections is well-studied, the effect of different regional models on the evolution of the AIS is mostly unknown. In our study, we present the impact of the choice of present-day reference RCM forcing on the evolution of the AIS. We used the Parallel Ice Sheet Model (PISM) to study the AIS in a quasi-equilibrium state and under future projections, combining present-day RCM output with global climate model projections. Our study suggests differences in projected Antarctic sea level contributions due to the choice of different present-day surface mass balance (SMB) and temperature baseline forcings of 10.6 mm in the year 2100 and 70.0 mm in 2300 under the RCP8.5 scenario. Those uncertainties are an order of magnitude smaller than what is estimated from uncertainties related to ice sheet and climate models. However, we observe an increase in RCM-induced uncertainties over time and for higher-emission scenarios. Additionally, our study shows that the complex relationship between the selected RCM baseline climatology and its impact on future sea level rise is closely related to the stability of West Antarctic Ice Sheet (WAIS), particularly the dynamic response of Thwaites and Pine Island glaciers. On millennial timescale, the choice of the RCM reference leads to ice volume differences up to 2.3 m and can result in the long-term collapse of the West Antarctic Ice Sheet. [ABSTRACT FROM AUTHOR]

Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes. [ABSTRACT FROM AUTHOR]

The next version of the polar Regional Atmospheric Climate Model (referred to as RACMO2.4p1) is presented in this study. The principal update includes embedding of the package of physical parameterizations of the Integrated Forecast System (IFS) cycle 47r1. This constitutes changes in the precipitation, convection, turbulence, aerosol and surface schemes and includes a new cloud scheme with more prognostic variables and a dedicated lake model. Furthermore, the standalone IFS radiation physics module ecRad is incorporated into RACMO, and a multilayer snow module for non-glaciated regions is introduced. Other updates involve the introduction of a fractional land–ice mask, new and updated climatological data sets (such as aerosol concentrations and leaf area index), and the revision of several parameterizations specific to glaciated regions. As a proof of concept, we show first results for Greenland, Antarctica and a region encompassing the Arctic. By comparing the results with observations and the output from the previous model version (RACMO2.3p3), we show that the model performs well regarding the surface mass balance, surface energy balance, temperature, wind speed, cloud content and snow depth. The advection of snow hydrometeors strongly impacts the ice sheet's local surface mass balance, particularly in high-accumulation regions such as southeast Greenland and the Antarctic Peninsula. We critically assess the model output and identify some processes that would benefit from further model development. [ABSTRACT FROM AUTHOR]

The impact of a slightly modified broadband snow surface albedo parameterization, which explicitly considers the cloud dependence of the snow albedo, is evaluated in simulations of a coupled regional climate model of the Arctic. The cloud dependence of the snow albedo leads to a more realistic simulation of the variability of the surface albedo during the snowmelt period in late May and June. In particular, the reproduction of lower albedo values under cloud-free/broken-cloud conditions during the snowmelt period represents an improvement and results in an earlier disappearance of the snow cover and an earlier onset of sea-ice melt. In this way, the consideration of the cloud dependence of the snow albedo results in an amplification of the two-stage snow/ice albedo feedback in the model. This finds expression in considerably increased sea-ice melt during the summer months and ends up in a new quasi-stationary equilibrium in sea ice with statistically significant lower sea-ice volume and statistically significant lower summer sea-ice area. [ABSTRACT FROM AUTHOR]

We present an overview of the UK's Global Sea Ice model configuration version 9 (GSI9), the sea ice component of the latest Met Office Global Coupled model, GC5. The GC5 configuration will, amongst other uses, form the physical basis for the HadGEM3 (Hadley Centre Global Environment Model version 3) climate model and UKESM2 (UK Earth System Model version 2) Earth system model that will provide the Met Office Hadley Centre/UK model contributions to CMIP7 (Coupled Model Intercomparison Project Phase 7). Although UK ocean model configurations have been developed for many years around the NEMO (Nucleus for European Modelling of the Ocean) ocean modelling framework, the GSI9 configuration is the first UK sea ice model configuration to use the new native NEMO sea ice model, SI3 (Sea Ice modelling Integrated Initiative). This replaces the CICE (Community Ice CodE) model used in previous configuration versions. In this paper we document the physical and technical options used within the GSI9 sea ice configuration. We provide details of the implementation of SI3 into the Met Office coupled model and the adaptations required to work with our "conductivity coupling" approach and provide a thorough description of the GC5 coupling methodology. A brief evaluation of sea ice simulated by the GC5 model is included, with results compared to observational references and a previous Global Coupled model version (GC3.1) used for CMIP6, to demonstrate the scientific credibility of the results. [ABSTRACT FROM AUTHOR]

The phenomenon of electric fields applied to droplets, inducing droplet coalescence, is called the electro-coalescence effect. An analytic expression for electro-coalescence with the accurate electrostatic force for a pair of droplets with opposite-sign charges is established by treating the droplets as conducting spheres (CSs). To investigate this effect, we applied a weak electric field to a cumulus cloud using a cloud model that employs the super-droplet method, a probabilistic particle-based microphysics method. This study employs a two-dimensional (2D) large-eddy simulation (LES) in a flow-coupled model to examine aerosol microphysics (such as collision–coalescence enhancement, velocity fluctuations, and supersaturation fluctuations) in warm cumulus clouds without relying on subgrid dynamics. In the simulation, we assume that droplets carry opposite-sign charges and are well mixed within the cloud. The charge is not treated as an individual particle attribute. To assess fluctuation effects, we conducted 50 simulations with varying pseudo-random number sequences for each electro-coalescence treatment. The results show that, with CS treatment, the electrostatic force contributes a larger effect on cloud evolution than in previous research. With a lower charge limit of the maximum charge amount on the droplet, the domain total precipitation with CS treatment for droplets with opposite signs is higher than that with the no-charge (NC) setting. Compared to previous work, the multi-image dipole treatment of CS results in higher precipitation. It is found that the electro-coalescence effect could affect rain formation even when the droplet charge is at the lower charge limit. High pollution levels result in greater sensitivity to electro-coalescence. The results show that, when the charge ratio between two droplets is over 100, the short-range attractive electric force due to the multi-image dipole would also significantly enhance precipitation for the cumulus. It is indicated that, although the accurate treatment of the electrostatic force with the CS method would require 30 % longer computation time than before, it is worthwhile to include it in cloud, weather, and climate models. [ABSTRACT FROM AUTHOR]

Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach. [ABSTRACT FROM AUTHOR]

The CICERO Simple Climate Model (CICERO-SCM) is a lightweight, semi-empirical model of global climate. Here we present a new open-source Python port of the model for use in climate assessment and research. The new version of CICERO-SCM has the same scientific logic and functionality as the original Fortran version, but it is considerably more flexible and also open-source via GitHub. We describe the basic structure and improvements compared to the previous Fortran version, together with technical descriptions of the global thermal dynamics and carbon cycle components and the emission module, before presenting a range of standard figures demonstrating its application. A new parameter calibration tool is demonstrated to make an example calibrated parameter set to span and fit a simple target specification. CICERO-SCM is fully open-source and available through GitHub (https://github.com/ciceroOslo/ciceroscm , last access: 23 August 2024). [ABSTRACT FROM AUTHOR]

This study advances the understanding of climate projection variabilities in the Nemunas River, Curonian Lagoon, and southeastern Baltic Sea continuum by analyzing the output of a coupled ocean and drainage basin modeling system forced by a subset of climate models. A dataset from a downscaled high-resolution regional atmospheric climate model driven by four different global climate models was bias-corrected and used to set up the hydrological (Soil and Water Assessment Tool, SWAT) and hydrodynamic (Shallow water HYdrodynamic Finite Element Model, SHYFEM) modeling system. This study investigates the variability and trends in environmental parameters such as water fluxes, timing, nutrient load, water temperature, ice cover, and saltwater intrusions under Representative Concentration Pathway 4.5 and 8.5 scenarios. The analysis highlights the differences among model results underscoring the inherent uncertainties in projecting climatic impacts, hence highlighting the necessity of using multi-model ensembles to improve the accuracy of climate change impact assessments. Modeling results were used to evaluate the possible environmental impact due to climate change through the analysis of the cold-water fish species reproduction season. We analyze the duration of cold periods (<1.5 °C) as a thermal window for burbot (Lota lota L.) spawning, calculated assuming different climate forcing scenarios and models. The analysis indicated coherent shrinking of the cold period and presence of changepoints during historical and different periods in the future; however, not all trends reach statistical significance, and due to high variability within the projections, they are less reliable. This means there is a considerable amount of uncertainty in these projections, highlighting the difficulty of making reliable climate change impact assessments. [ABSTRACT FROM AUTHOR]

Cloud–radiation interactions are key to Earth's climate and its susceptibility to change. While their impact on Earth's energy budget has been studied in great detail, their effect on atmospheric temperatures has received little attention, despite its importance for the planetary circulation of the atmosphere and hence for regional climate and weather. Here, we present the first systematic assessment of vertically resolved cloud-radiative heating within the atmosphere in 20 CMIP6 (phase 6 of the Climate Model Intercomparison Project) models, including a comparison to satellite-based estimates. Our analysis highlights model differences in cloud-radiative heating in both the lower and upper troposphere as well as uncertainties related to cloud ice processes. It also illustrates limitations of our ability to observe cloud-radiative heating. Not surprisingly, the response of cloud-radiative heating to surface warming is also uncertain across models. Yet, in the upper troposphere, the response is very well predicted by an upward shift of the present-day heating, which we show results from the fact that cloud-radiative heating in the upper troposphere is a function of air temperature and thus decoupled from surface temperature. Our results have three important implications for upper-tropospheric cloud-radiative heating: they establish a new null hypothesis for its response to warming, offer a physics-based prediction of its response to warming based on present-day observations, and emphasize the need for improving its representation in simulations of the present-day climate, possibly by combining the benefits of upcoming km-scale models and satellite observations. [ABSTRACT FROM AUTHOR]

Marine boundary layer clouds play a critical role in Earth's energy balance. Their microphysical and radiative properties are highly impacted by ambient aerosols and dynamic forcings. In this study, we evaluate the representation of these clouds and related aerosol–cloud interaction processes in the single-column version of the E3SM climate model (SCM) against field measurements collected during the NASA ACTIVATE campaign over the western North Atlantic, as well as intercompare results with high-resolution process level models. We show that E3SM SCM reproduces the macrophysical properties of post-frontal boundary layer clouds in a cold-air outbreak (CAO) case well. However, it generates fewer but larger cloud droplets compared to aircraft measurements. Further sensitivity tests show that the underestimation of both aerosol number concentration and vertical velocity variance contributes to this bias. Aerosol–cloud interactions are examined by perturbing prescribed aerosol properties in E3SM SCM with fixed dynamics. Higher aerosol number concentration or hygroscopicity leads to more numerous but smaller cloud droplets, resulting in a stronger cooling via shortwave cloud forcing. This apparent Twomey effect is consistent with prior climate model studies. The cloud liquid water path shows a weakly positive relation with cloud droplet number concentration due to precipitation suppression. This weak aerosol effect on cloud macrophysics may be attributed to the dominant impact of strong dynamical forcing associated with the CAO. Our findings indicate that the SCM framework is a key tool to bridge the gap between climate models, process level models, and field observations to facilitate process level understanding. [ABSTRACT FROM AUTHOR]

Previous research has suggested that the frequency and intensity of surface hazards associated with thunderstorms and convection, such as severe convective winds (SCWs), could potentially change in a future climate due to global warming. However, because of the small spatial scales associated with SCWs, they are unresolved in global climate models, and future climate projections are uncertain. Here, we evaluate the representation of SCW events in a convection-permitting climate model (Bureau of Meteorology Atmospheric Regional Projections for Australia, BARPAC-M) run over southeastern Australia for the months of December–February. We also assess changes in SCW event frequency in a projected future climate for the year 2050 and compare this with an approach based on identifying large-scale environments favourable for SCWs from a regional parent model (BARPA-R). This is done for three different types of SCW events that have been identified in this region, based on clustering of the large-scale environment. Results show that BARPAC-M representation of the extreme daily maximum wind gust distribution is improved relative to the gust distribution simulated by the regional parent model. This is due to the high spatial resolution of BARPAC-M output, as well as partly resolving strong and short-lived gusts associated with convection. However, BARPAC-M significantly overestimates the frequency of simulated SCW events, particularly in environments having steep low-level temperature lapse rates. A future decrease in SCW frequency under conditions with steep lapse rates is projected by BARPAC-M, along with less frequent favourable large-scale environments. In contrast, an increase in SCW frequency is projected under conditions of high surface moisture, with more frequent favourable large-scale environments. Therefore, overall changes in SCWs for this region remain uncertain, due to different responses between event types, combined with historical model biases. [ABSTRACT FROM AUTHOR]

Meteorological and hydrological hazards present challenges to people and ecosystems worldwide, but the limited length of observational data means that the possible extreme range is not fully understood. Here, a large ensemble of climate model data is combined with a simple grid-based hydrological model to assess unprecedented but plausible hydrological extremes in the current climate across England. Two case studies are selected – dry (summer 2022) and wet (autumn 2023) – with the hydrological model initialised from known conditions and then run forward for several months using the large climate ensemble. The modelling chain provides a large set of plausible events including extremes outside the range from use of observed data, with the lowest flows around 28 % lower on average for the summer 2022 drought study and the highest flows around 42 % higher on average for the autumn 2023 flood study. The temporal evolution and spatial dependence of extremes are investigated, including the potential timescale of the recovery of flows to normal levels and the chance of persistent extremes. Being able to plan for such events could help improve the resilience of water supply systems to drought and improve flood risk management and incident response. [ABSTRACT FROM AUTHOR]

Paleoclimatological field reconstructions are valuable for understanding past hydroclimatic variability, which is crucial for assessing potential future hydroclimate changes. Despite being as impactful on societies as temperature variability, hydroclimatic variability – particularly beyond the instrumental record – has received less attention. The reconstruction of globally complete fields of climate variables lacks adequate proxy data from tropical regions like South America, limiting our understanding of past hydroclimatic changes in these areas. This study addresses this gap using low-resolution climate archives, including speleothems, previously omitted from reconstructions. Speleothems record climate variations on decadal to centennial timescales and provide a rich dataset for the otherwise proxy-data-scarce region of tropical South America. By employing a multi-timescale paleoclimate data assimilation approach, we synthesize climate proxy records and climate model simulations capable of simulating water isotopologs in the atmosphere to reconstruct 2000 years of South American climate. This includes surface air temperature, precipitation amount, drought index, isotopic composition of precipitation amount and the intensity of the South American Summer Monsoon. The reconstruction reveals anomalous climate periods: a wetter and colder phase during the Little Ice Age (∼ 1500–1850 CE) and a drier, warmer period corresponding to the early Medieval Climate Anomaly (∼ 600–900 CE). However, these patterns are not uniform across the continent, with climate trends in northeastern Brazil and the Southern Cone not following the patterns of the rest of the continent, indicating regional variability. The anomalies are more pronounced than in previous reconstructions but match trends found in local proxy record studies, thus highlighting the importance of including speleothem proxies. The multi-timescale approach is essential for reconstructing multi-decadal and centennial climate variability. Despite methodological uncertainties regarding climate model biases and proxy record interpretations, this study marks a crucial first step in incorporating low-resolution proxy records such as speleothems into climate field reconstructions using a multi-timescale approach. Adequately extracting and using the information from speleothems potentially enhances insights into past hydroclimatic variability and hydroclimate projections. [ABSTRACT FROM AUTHOR]

Understanding orbital-scale changes in East Asian summer monsoon (EASM) precipitation is a fundamental issue in paleoclimate research as it helps assess the response of the East Asian monsoon to different climatic forcings, such as insolation, ice volume, and greenhouse gases. However, due to inconsistencies between different proxies, the fundamental driving force for EASM variability remains controversial. The present study simulated the global climate under given insolation changes over the past 450 kyr using a climate model, version 2.3 of the Meteorological Research Institute's Coupled General Circulation Model (MRI-CGCM2.3). Changes in summer insolation over East Asia resulted in distinct climatic responses in China and Japan: an increase in summer insolation led to increased summer precipitation in China and decreased summer precipitation in Japan. Composite analyses of simulated climate under strong boreal-summer insolation suggest that warming of the Indian Ocean occurs under intense insolation, resulting in the intensification of the North Pacific subtropical high (sub-high). The northern shift in the monsoon front, associated with the intensified sub-high, leads to an increase in rainfall in the coastal area of China. In contrast, the intensity of the EASM around Japan is affected by the strength of the North Pacific High. Under strong insolation, the increase in thermal contrast between the North American continent and the North Pacific Ocean intensifies the North Pacific High, decreasing summer precipitation around Japan. Thus, strong regional differences in the effects of solar-insolation variability on summer precipitation in East Asia exist due to interactions with different ocean basins. [ABSTRACT FROM AUTHOR]

We present a reconstruction of the surface climate of the mid-Pliocene Warm Period (mPWP), specifically Marine Isotope Stage (MIS) KM5c or 3.205 Ma. We combine the ensemble of climate model simulations, which contributed to the Pliocene Model Intercomparison Project (PlioMIP), with compilations of proxy data analyses of sea surface temperature (SST). The different data sets we considered are all sparse with high uncertainty, and the best estimate of annual global mean surface air temperature (SAT) anomaly varies from 2.1 up to 4.8 °C depending on the data source. We argue that the latest PlioVAR analysis of alkenone data is likely more reliable than other data sets we consider, and using this data set yields an SAT anomaly of 3.9±1.1 °C, with a value of 2.8±0.9 °C for SST (all uncertainties are quoted at 1 standard deviation). However, depending on the application, it may be advisable to consider the broader range arising from the various data sets to account for structural uncertainty. The regional-scale information in the reconstruction may not be reliable as it is largely based on the patterns simulated by the models. [ABSTRACT FROM AUTHOR]

Pollen data represent one of the most widely available and spatially resolved sources of information about the past land cover and climate of the Last Glacial Maximum (LGM; 21 000 yr BP). Previous pollen data compilations for Europe, the Mediterranean, and the Middle East, however, have been limited by small numbers of sites and poor dating control. Here we present a new compilation of pollen data from the region that improves on both the number of sites (63) and the quality of the chronological control. Data were sourced from both public data archives and published (digitized) diagrams. The analysis is presented based on a standardized pollen taxonomy and sum, with maps shown for the major pollen taxa and biomes and the total arboreal pollen (AP), and on quantitative reconstructions of forest cover and of winter, summer, and annual temperatures and precipitation. The reconstructions are based on the modern analogue technique (MAT) adapted using plant functional type (PFT) scores and with a modern pollen dataset taken from the latest Eurasian Modern Pollen Database (EMPD) (∼8000 samples). A site-by-site comparison of the MAT and the inverse modelling method shows little or no significant difference between the methods for the LGM, indicating that the presence of low-CO 2 conditions and no modern analogue during the LGM does not appear to have had a major effect on MAT transfer function performance. Previous pollen-based climate reconstructions using modern pollen datasets show a much colder and drier climate for the LGM than both inverse modelling and climate model simulations do, but our new results suggest much greater agreement. Differences between our latest MAT reconstruction and those in earlier studies can largely be attributed to bias in the small modern dataset previously used and to differences in the method itself (Brewer et al., 2008; Salonen et al., 2019). We also find that quantitative forest cover reconstructions show more forest than previously suggested by biome reconstructions but less forest than suggested by simply the percentage of arboreal pollen, although uncertainties remain large. Overall, we find that LGM climatic cooling and drying were significantly greater in winter than in summer but with large site-to-site variance that emphasizes the importance of topography and other local factors in controlling the climate and vegetation of the LGM. [ABSTRACT FROM AUTHOR]

The ocean and the land biosphere are the two major sinks of anthropogenic carbon at present. When anthropogenic carbon emissions become zero and temperatures stabilize, the ocean is projected to become the dominant and only global natural sink of carbon. Despite the ocean's importance for the carbon cycle and hence the climate, uncertainties about the decadal variability in this carbon sink and the underlying drivers of this decadal variability remain large because observing the ocean carbon sink and detecting anthropogenic changes over time remain challenging. The main tools that are used to provide annually resolved estimates of the ocean carbon sink over the last decades are global observation-based p CO 2 products that extrapolate sparse p CO 2 observations in space and time and global ocean biogeochemical models forced with atmospheric reanalysis data. However, these tools (i) are limited in time over the last 3 to 7 decades, which hinders statistical analyses of the drivers of decadal trends; (ii) are all based on the same internal climate state, which makes it impossible to separate externally and internally forced contributions to decadal trends; and (iii) cannot assess the robustness of the drivers in the future, especially when carbon emissions decline or cease entirely. Here, I use an ensemble of 12 Earth system models (ESMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to understand drivers of decadal trends in the past, present, and future ocean carbon sink. The simulations by these ESMs span the period from 1850 to 2100 and include four different future Shared Socioeconomic Pathways (SSPs), from low emissions and high mitigation to high emissions and low mitigation. Using this ensemble, I show that 80 % of decadal trends in the ocean carbon sink can be explained by changes in decadal trends in atmospheric CO 2 as long as the ocean carbon sink remains smaller than 4.5 Pg C yr -1. The remaining 20 % are due to internal climate variability and ocean heat uptake, which result in a loss of carbon from the ocean. When the carbon sink exceeds 4.5 Pg C yr -1 , which only occurs in the high-emission SSP3-7.0 and SSP5-8.5, atmospheric CO 2 rises faster, climate change accelerates, and the ocean overturning and the chemical capacity to take up carbon from the atmosphere reduce, so that decadal trends in the ocean carbon sink become substantially smaller than estimated based on changes in atmospheric CO 2 trends. The breakdown of this relationship in both high-emission pathways also implies that the decadal increase in the ocean carbon sink is effectively limited to ∼1 Pg C yr -1 dec -1 in these pathways, even if the trend in atmospheric CO 2 continues to increase. Previously proposed drivers, such as the atmospheric CO 2 or the growth rate of atmospheric CO 2 , can explain trends in the ocean carbon sink for specific time periods, for example, during exponential atmospheric CO 2 growth, but fail when emissions start to decrease again. The robust relationship over an ensemble of 12 different ESMs also suggests that very large positive and negative decadal trends in the ocean carbon sink by some p CO 2 products are highly unlikely and that the change in the decadal trends in the ocean carbon sink around 2000 is likely substantially smaller than estimated by these p CO 2 products. [ABSTRACT FROM AUTHOR]

There is an increasing need for simulating the evolution of wildland fires. The realism of the simulation increases by accounting for feedbacks between the fire and the atmosphere. These coupled models combine a fire behavior model with a regional numerical weather prediction model and have been used for fire research during the last decades. This is the case, for instance, of the state-of-the-art Weather Research and Forecasting model with fire extensions (WRF-Fire). Typically, the coupling includes specific code for the particular models being coupled such as interpolation procedures to pass variables from the atmospheric grid to the fire grid, and vice versa. However, having a fire modeling framework that can be coupled to different atmospheric models is advantageous to foster collaborations and joint developments. With this aim, we have created, for the first time, a fire behavior model that can be connected to other atmospheric models without the need of developing specific low-level procedures for the particular atmospheric model being used. The fire behavior model, referred to as the Community Fire Behavior model (CFBM), closely follows WRF-Fire version 4.3.3 methods in its version 0.2.0, and makes use of the Earth System Modeling Framework library to communicate information between the fire and the atmosphere. The CFBM can be also run offline using an existing WRF simulation in what we refer to as the standalone model. Herein we describe the fire modeling framework and its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak Fire performed with UFS and WRF-Fire are presented to verify our implementation. Results from both models, as well as with the standalone version, are consistent indicating a proper development of the CFBM and its coupling to the UFS-Atmosphere. These results, and the possibility of using the fire behavior model with other atmospheric models, provide an attractive collaborative framework to further improve the realism of the model in order to meet the growing demand for accurate wildland fire simulations. [ABSTRACT FROM AUTHOR]

With the ongoing decline in Arctic sea ice extent, the accurate simulation of Arctic sea ice in coupled models remains an important problem in climate modelling. In this study, the substantial CMIP6 model spread in Arctic sea ice extent and volume is investigated using a novel, process-based approach. An observational dataset derived from the Arctic Ice Mass Balance buoy (IMB) network is used to evaluate the thermodynamic and mass balance diagnostics produced by a subset of CMIP6 models, to better understand the model processes that underlie the large-scale sea ice states. Due to the sparse nature of the IMB observations, the evaluation is performed by comparing distributions of modelled and observed fluxes in the densely sampled regions of the North Pole and Beaufort Sea. We find that all fluxes are routinely biased high in magnitude with respect to the IMB measurements by nearly all models, with too much melt in summer, and too much conduction and growth in winter, even as a function of ice thickness. We also show that choices of thermodynamic parameterisation substantially influence particular fluxes in physically realistic ways, and that these effects likely modulate the large-scale relationship between ice thickness and ice growth and melt in the CMIP6 models. [ABSTRACT FROM AUTHOR]

Climate change poses a significant threat to agriculture, with potential impacts on food security, economic stability, and human livelihoods. Dairy cattle, a crucial component of the livestock sector, are particularly vulnerable to heat stress, which can adversely affect milk production, immune function, feed intake, and in extreme cases, lead to mortality. The Temperature Humidity Index (THI) is a widely used metric to quantify the combined effects of temperature and humidity on cattle. However, most studies estimate THI using daily-level data, which fails to capture the full extent of daily thermal load and cumulative heat stress, especially during nights when cooling is inadequate. To address this limitation, we developed a machine learning approach to temporally downscale daily climate data to hourly THI values. Utilizing historical ERA5 reanalysis data, we trained an XGBoost model and generated hourly THI datasets for 12 NEX-GDDP-CMIP6 climate models under two emission scenarios (SSP2-4.5 and SSP5-8.5) extending to the end of the century. This high-resolution THI data provides a more accurate assessment of heat stress in dairy cattle, enabling better predictions and management strategies to mitigate the impacts of climate change on this vital agricultural sector. [ABSTRACT FROM AUTHOR]