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Emissions of anthropogenic aerosols are rapidly changing, in amounts, composition and geographical distribution. In East and South Asia in particular, strong aerosol trends combined with high population densities imply high potential vulnerability to climate change. Improved knowledge of how near‐term climate and weather influences these changes is urgently needed, to allow for better‐informed adaptation strategies. To understand and decompose the local and remote climate impacts of regional aerosol emission changes, we perform a set of Systematic Regional Aerosol Perturbations (SyRAP) using the reduced‐complexity climate model FORTE 2.0 (FORTE2). Absorbing and scattering aerosols are perturbed separately, over East Asia and South Asia, to assess their distinct influences on climate. In this paper, we first present an updated version of FORTE2, which includes treatment of aerosol‐cloud interactions. We then document and validate the local responses over a range of parameters, showing for instance that removing emissions of absorbing aerosols over both East Asia and South Asia is projected to cause a local drying, alongside a range of more widespread effects. We find that SyRAP‐FORTE2 is able to reproduce the responses to Asian aerosol changes documented in the literature, and that it can help us decompose regional climate impacts of aerosols from the two regions. Finally, we show how SyRAP‐FORTE2 has regionally linear responses in temperature and precipitation and can be used as input to emulators and tunable simple climate models, and as a ready‐made tool for projecting the local and remote effects of near‐term changes in Asian aerosol emissions. Plain Language Summary Emissions of anthropogenic aerosols are rapidly changing, both in amounts, composition, and geographical distribution. Aerosol‐climate impacts follow patterns and time evolutions different to those from greenhouse gas‐driven surface warming, potentially enhancing climate risk. However, our understanding of these patterns and processes is still limited. In East and South Asia, strong aerosol trends and high population densities imply a high potential vulnerability to climate change. To allow for better‐informed adaptation strategies, there is an urgent need for improved knowledge of how near‐term climate and weather influences these changes. Here we perform a set of Systematic Regional Aerosol Perturbations (SyRAP) using the reduced‐complexity climate model FORTE 2 to decompose the climate impacts of regional aerosol emission changes. We developed a new functionality in the model, allowing for the ability to emulate the indirect aerosol effect—in isolation or in combination with aerosol radiation interactions. We investigate the separate roles of both light‐absorbing and ‐scattering aerosols, and the distinct impacts of emission perturbations in East versus South Asia. We find that SyRAP‐FORTE2 is able to reproduce the responses to Asian aerosol changes documented in the literature, and that it can help us decompose regional climate impacts of aerosols from the two regions. Key Points Removing emissions of absorbing aerosols over both East Asia and South Asia is projected to cause a local drying In certain subregions, the impact of SO4 on precipitation is strongly dependent on the background climate state Results show regionally linear responses in temperature and precipitation and can be used as input to emulators and simple climate models

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

North Florida's karst springs are among the largest and most abundant in the world. Despite relatively stable spring discharges, flow reversals can episodically occur in some springs when river waters backflow into the aquifer during flood events. Reversals are normal features of the springs along the Suwanee River, but the changing incidence of these reversals in response to anthropogenic activities or climate change remains unclear and the mechanisms responsible for these reversals remain poorly described. Here we develop a reduced‐complexity hydrogeological model of the Suwannee River catchment to explore conditions needed to induce spring flow reversals. Our model demonstrates that reversals require two conditions: (a) a hydrogeological setting that combines an upstream catchment with rapid hydrological responses to meteorological drivers, which freely drains to a downstream catchment containing the karst aquifer (i.e., the spring‐fed river segment); and (b) meteorological conditions that create sufficient temporal variability in recharge. Given both conditions, recharge events can propagate from the upstream catchment and fill the downstream river segment faster than it can drain, causing river stage to rise above the aquifer head, resulting in temporary spring flow reversal (or bank storage). Our model accurately predicts significant post‐flood increases in spring flow as bank storage recedes, and using measured electrical conductivity at a major river‐adjacent spring we also quantify the enhancement of limestone dissolution (cave enlargement) due to reversal events. A comprehensive assessment of the incidence and duration of reversal events shows a predominant influence of climate and vegetation changes over that of groundwater pumping. Plain Language Summary North Florida's karst springs are among the largest and most abundant in the world. Spring flow is very stable and usually into the main river, but reversals of flow direction may occur temporarily, such that water from the river enters the subsurface through the springs. This is a natural feature, but its causes and future behavior under climate change and human activities are poorly understood. In this work, we develop a simple model of the Suwannee River catchment that describes spring flow and reversals, showing that reversals require both appropriate geological as well as meteorological conditions. We also show that spring flow reversals contribute significantly to the limestone cave enlargement near the river, and that the future characteristics of reversal events are most dependent on climate and vegetation changes. Key Points A reduced‐complexity model explains flow reversals in Florida karst springs due to fast discharge response from upstream surficial aquifer The occurrence of reversals requires a certain hydrogeological setting and sufficient temporal variability in aquifer recharge The model also quantifies enhanced limestone dissolution rates (cave enlargement) near the river due to flow reversals

Reduced complexity climate models are useful tools for quantifying decision‐relevant uncertainties, given their flexibility, computational efficiency, and suitability for large‐ensemble frameworks necessary for statistical estimation using resampling techniques (e.g., Markov chain Monte Carlo). Here we document a new version of the simple, open‐source, global climate model Hector, coupled with a 1‐D diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model) and a sea level change module (Building blocks for Relevant Ice and Climate Knowledge) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. We apply a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters with prescribed radiative forcing, using observational information from global surface temperature, thermal expansion, and other contributors to sea level change. We find the addition of thermal expansion as an observational constraint sharpens inference for the upper tail of posterior equilibrium climate sensitivity estimates (the 97.5 percentile is tightened from 7.1 to 6.6 K), while other contributors to sea level change play a lesser role. The thermal expansion constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5 2100 temperature decreases 0.3 K). Due to the model's parameterization of thermal expansion as an uncertain function of global ocean heat, we note a trade‐off between two ways of incorporating thermal expansion information: Ocean heat data provide a somewhat sharper equilibrium climate sensitivity estimate while thermal expansion data allow for constrained sea level projections. Plain Language Summary Global warming poses considerable climate risks, such as increasing sea level and temperature extremes. Constraining the upper bounds of these salient and decision‐relevant aspects of climate change can provide important information for assessing vulnerabilities and risk and adaptation planning. Simple climate models that are both flexible and computationally efficient can be constrained by historical observations to statistically estimate key uncertain climate parameters and characterize climate upper bounds. Previous studies have shown that statistical estimates of the global temperature response to atmospheric CO2 depend on both global surface temperature and ocean heat content observational constraints. Here we use the Hector simple climate model to statistically estimate the temperature response to CO2 using several different sets of observational constraints, including several contributors to sea level. We find that the inclusion of thermal expansion tightens estimates of the temperature response to atmospheric CO2 and the upper bounds of temperature projections, while other contributors to sea level play a lesser role. Key Points We document a version of the Hector climate model featuring a sea level component with expansion, polar land ice, and glacier contributions Our calibration approach examines the effect of constraints related to sea level on estimates of equilibrium climate sensitivity Including thermal expansion information in the calibration sharpens the upper tail of equilibrium climate sensitivity

The same prescribed anthropogenic aerosol forcing was implemented into three climate models. The atmosphere components of these participating climate models were the GAMIL, ECHAM, and CAM models. Ensemble simulations were carried out to obtain a reliable estimate of anthropogenic aerosol effective radiative forcing (ERF). The ensemble mean ERFs from these three participating models with this aerosol forcing were −0.27, −0.63, and −0.54 W∙m−2. The model diversity in ERF is clearly reduced as compared with those based on the models’ own default approaches (−1.98, −0.21, and −2.22 W∙m−2). This is consistent with the design of this aerosol forcing. The modeled ERF can be decomposed into two basic components, i.e., the instantaneous radiative forcing (RF) from aerosol–radiation interactions (RFari) and the aerosol-induced changes in cloud forcing (△Fcloud*). For the three participating models, the model diversity in RFari (−0.21, −0.33, and −0.29 W∙m−2) could be constrained by reducing the differences in natural aerosol radiative forcings. However, it was difficult to figure out the reason for the model diversity in △Fcloud* (−0.05, −0.28, and −0.24 W∙m−2), which was the dominant source of the model diversity in ERF. The variability of modeled ERF was also studied. Ensemble simulations showed that the modeled RFs were very stable. The rapid adjustments (ERF − RF) had an important role to play in the quantification of the perturbation of ERF. Fortunately, the contribution from the rapid adjustments to the mean ERF was very small. This study also showed that we should pay attention to the difference between the aerosol climate effects we want and the aerosol climate effects we calculate.

Salt marshes are highly valued coastal environments for different services: coastline protection, biodiversity, and blue carbon. They are vulnerable to climate changes, particularly to sea-level rise. For this reason, it is essential to project the evolution of marsh areas until the end of the century. This work presents a reduced complexity model to quantify salt marshes’ evolution in a sea-level rise (SLR) context through combining field and remote sensing data: SMRM (Simplified Marsh Response Model). SMRM is a two-dimensional rule-based model that requires four parameters: a digital terrain model (DTM), local tidal levels, a sea-level rise projection, and accretion rates. A MATLAB script completes the process, and the output is a GeoTIFF file. Two test areas were selected in Tróia sandspit (Setúbal, Portugal). Additionally, a sensitivity analysis for each parameter’s influence and a comparison with SLAMM (another rule-based model) were undertaken. The sensitivity analysis indicates that SLR is the most relevant parameter, followed by accretion rates. The comparison of SMRM with SLAMM shows quite similar results for both models. This new model application indicates that the studied salt marshes could be resilient to conservative sea-level rise scenarios but not to more severe sea-level rise projections.

Shorelines adjacent to tidal inlets are highly dynamic landforms affected by oceanic (e.g., sea-level rise) and terrestrial (e.g., fluvial sediment supply) processes. Climate change is thus expected to have substantial physical impacts on these inlet-interrupted coasts. Numerical simulation of such impacts requires a holistic approach where at least the major governing processes that affect the local sediment budget are considered. The Generalized-Scale-aggregated Model for Inlet-interrupted Coasts (i.e., G-SMIC) is such a model that is capable of holistically simulating the evolution of inlet-interrupted coasts over multi-decadal to century time periods. However, in its present form, G-SMIC does not consider the effects of ebb-delta dynamics in its computations. Here, we improve the model to include ebb-delta dynamics and pilot the improved model (G-SMIC+) at two selected case study sites in Vietnam (Thu Bon estuary) and Wales, United Kingdom (Mawddach estuary). Model hindcasts of G-SMIC+ at both case study locations show reasonable agreement with available records of shoreline variations. The evolution of the two inlet-estuary systems was assessed over the 21 st century under four of the IPCC’s sixth assessment report climate scenarios (viz., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Results show that both systems switch between sediment exporting and sediment importing systems over the study period (2031 - 2100). Moreover, while the inclusion of ebb-delta dynamics may decrease the erosion volumes of the up-drift shoreline by up to 37% and 46% at Thu Bon and Mawddach estuaries, respectively (by 2100, relative to 2030), the down-drift coast is only affected in a noticeable way at the Mawddach estuary, where the accretion volume is projected to reduce by ~50%. As a result, the ebb-delta effect decreases the up-drift shoreline retreat by up to 37% and 48% at Thu Bon and Mawddach estuaries, respectively, while it reduces shoreline progradation of the down-drift coast of Mawddach estuary by up to ~50%. These results highlight the importance of including ebb-delta dynamics in modelling efforts to assess the climate change responses of inlet-interrupted coasts worldwide.

This paper describes the application of a hydrogeomorphological numerical model (CAESAR) to simulate, at hourly time steps, changes in the hydrological and sediment regime of the Petit lac d'Annecy catchment. The outputs of the model were validated in three ways. In the short term (~5 years), water discharge outputs were compared against observed instrumental data. Over the longer term, modelled sediment discharge (AD 1825—2005) was compared with proxies for detrital sediment influx (environmental magnetism) and accumulation rates discerned from a 210Pb chronology for the lake sediments. Finally, spatial validation of the modelled erosion and deposition of sediment was undertaken by comparison with a field and remotely sensed survey of catchment geomorphology. The results suggest that while minor perturbations in forest cover during the last 180 years have partially conditioned the response of the sediment system, the bulk of modelled sediment discharge and particularly the peaks in sediment discharge were controlled by flood duration and magnitude, which in turn is driven by precipitation (storms/floods) and snowmelt. Basin geometry and geomorphology of each sub-catchment (Ire and Tamie) were also important in producing differences in the modelled sediment discharge. In essence, these differences were a function of sediment accommodation space and the ability of each system to store and release sediments. Modelled sediment discharge and χpara (lake sediments) display similar histories, and thus are both interpreted as reflecting variations in detrital sediment supply. Intriguingly the style of modelled sediment discharge from the Ire, a confined mountain torrent, displays a greater similarity to and perhaps dominates the lake sediment record. These results provide partial validation of the CAESAR model and indicate that perhaps in the future it may be used as an exploratory and predictive tool in determining the impact of changes in climate, meteorology and land use on lake-catchment systems.