Boreal permafrost over the Northern Hemisphere high latitudes, defined as areas where the ground temperature is below 0 °C for two or more years, stores more than twice as much carbon as the atmosphere. Therefore, thawing of the permafrost, a tipping element, due to global warming may lead to additional carbon emissions and accelerate the warming. To investigate the permafrost response to increase and decrease of CO2 emissions, we conducted a series of numerical experiments using an emission-driven Earth System Model, MIROC-ES2L, and adopting idealized overshooting scenarios in which a prescribed CO2 emission of 10 PgC yr−1 is given until the global warming level reaches different values between 2 and 8 °C followed by the negative emission until the cumulative emission becomes zero. We found that the response of permafrost area to surface warming and cooling is reversible but has hysteresis for all the emission scenarios. Furthermore, the permafrost property such as the ratio of frozen to liquid water was shown to have irreversibility in the deep soil layer; part of the frozen area in the initial condition was replaced by a mixed water-ice area in the final state despite ground temperature returning almost to the initial condition. Sensitivity experiments reveal that the hysteresis and irreversibility are attributed to the delay of the soil freezing and melting associated with the soil heat conductivity and specific heat of water phase change. This result indicates that once permafrost thaws with warming it will continue for decades after warming diminishes and the delay in the permafrost recovery is larger at global warming levels greater than 2 °C. An offline calculation shows that the additional CO2 emission during the permafrost hysteresis cycle accounts for about 0.6 %–41 % of the cumulative carbon emission released from thawed permafrost in flat10 and NEC experiments.
Abstract. Stratospheric aerosol injection (SAI) simulations are often short relative to climatic timescales and conducted against a background that evolves due to changes in anthropogenic greenhouse gas emissions and other forcings. This can cause challenges in assessing certain impacts of the intervention, especially for aspects of the climate that respond slowly to such changes. The early Geoengineering Model Intercomparison Project (GeoMIP) G2 experiment prescribes solar dimming to offset 1 % CO2 forcing in a preindustrial control background. Here we propose a new G2-SAI experiment, in which SAI is applied in the same scenario, to isolate SAI climate responses from transient changes other than CO2. Using the Community Earth System Model (CESM2), we present three 150-year “G2-SAI” simulations which use contemporary SAI strategies: two use the commonly-used “three degree-of-freedom” (“3DOF”) strategy, in which independent injections at 30° N, 15° N, 15° S, and 30° S are used to manage global mean temperature (T0) and large-scale meridional temperature gradients (T1, T2). Our third G2-SAI simulation uses a “1DOF” strategy that injects at 30° N and 30° S to manage global mean temperature only. Our two 3DOF simulations both maintain the same temperature targets; however, one simulation, which injects mostly at 15° S, slows but does not prevent the decline of the Atlantic Meridional Overturning Circulation (AMOC) compared to the baseline simulation, while the other, which injects mostly at 30° N and 30° S, stops the decline of AMOC entirely, similarly to the 1DOF simulation. These results demonstrate that multiple distinct Earth system states can satisfy the same temperature targets, challenging the assumption of linearity commonly used in strategy design. In addition, the results highlight that long simulations are required to identify some of the long-term impacts of SAI, such as AMOC changes. Using this knowledge, we revisit the ARISE-SAI-1.5 experiment and modify the injection strategy without changing the temperature targets, producing an “ARISE-hybrid” ensemble. We demonstrate that this results in some significant differences in the climate response to SAI, with implications for the perceived effects of the intervention.
The sustainable management of global groundwater resources is a key societal challenge and is central to the Sustainable Development Goals. The localized dynamics of groundwater abstraction, topography, and surface-water interactions, as well as the sensitivity of groundwater-dependent ecosystems, call for high-resolution information to support effective groundwater management. At the same time, groundwater observations are very limited and concentrated in a few regions, rendering large parts of groundwater resources ungauged. To address limited observations and coarse global models, we applied the global groundwater model GLOBGM (v1.1) to simulate past and future groundwater heads and water table depth at 30 arcsec (∼ 1 km) on a monthly time step. Model calibration improved mean bias in water table depth predictions from −4.8 to 3.6 m compared to GLOBGM v1.0, with depth-weighted bias reduced from 34.2 to 32.5 m across 34 800 observation wells. Groundwater dynamics are simulated for a historical reference period (1960–2019) to support model evaluation and attribution of observed impacts to climate variability and change. Baselines (1960–2014) and three combined socioeconomic-climate scenarios (2015–2100; SSP1-RCP2.6, SSP3-RCP7.0, SSP5-RCP8.5) are simulated with five global climate models, supporting detection and impact assessment of future change. Validation against monthly observations yielded skillful predictions (KGE-NPskill) in approximately 60 % of deep wells (> 60 m) and 83 % of shallow to intermediate wells (0–60 m). When validated against annual observations, 66 % of deep wells (> 60 m) and 71 % of shallow to intermediate wells (0–60 m) were skillful. Simulations are further bias corrected using a machine learning approach. Historical trend analysis (1960–2019) accurately reproduced known groundwater depletion regions such as the U.S. High Plains, Arabian Peninsula, and Indo-Gangetic Plain, while also identifying rising water tables in northern latitudes and Arctic regions, which are linked to climate-driven recharge changes. Future scenario-based simulations suggest rising water tables for most continents in the next century, with Europe being a notable exception. However, known regions of groundwater depletion are expected to persist. Regions that disagree with observations or show reduced reliability are mapped, and quality assurance flags are provided to guide the appropriate use and interpretation of the results. The resulting data set offers high-resolution information to assess groundwater dynamics for the past and future, supporting improved global water resource management and climate impact assessments.
Skilful predictions of European summer climate are increasingly relevant due to an increasing probability of temperature extremes, but prediction skill beyond the forced trend has so far proven limited. Atlantic Multidecadal Variability (AMV), characterised at the surface by North Atlantic sea surface temperatures (SSTs), is both active and predictable during boreal summer, and previous studies have linked it to surface impacts in Europe. Current understanding largely relies on the relatively short observational record of decadal variability and the predictability of impacts and associated mechanisms are poorly studied. In this study, single model large ensemble historical and decadal hindcast simulations using the MPI-ESM-LR model are used to understand the role that AMV plays for North Atlantic-Europe sector climate prediction. It is found that strong AMV-associated SST anomalies in the subpolar gyre region are better represented in the initialised hindcasts than in the uninitialised historical ensemble, and they are highly predictable at lead years 1–7. The observed cyclonic response to positive AMV in the extratropical North Atlantic is not present in historical simulations, but it is found to be predictable in decadal hindcasts, although with underestimated amplitude. The hindcast pressure anomaly nonetheless skilfully predicts the observed anomaly and highlights a potential role for AMV in the yet-unsolved “signal-to-noise paradox”. The upper tropospheric (200 hPa) geopotential height response to AMV is analysed and it is found to differ in reanalyses and models. Further investigation reveals a high frequency component relating to tropical SST anomalies and resembling a Rossby wave train emanating from the Caribbean, and a low frequency component relating to the surface level response, with an imbalance between the two mechanisms in models due to the weak surface response.
Abstract. The Atlantic Multidecadal Variability (AMV) and the North Atlantic Oscillation (NAO) are the dominant modes of oceanic and atmospheric variability in the North Atlantic, respectively, and are key sources of predictability from seasonal to decadal timescales. However, the physical processes and feedback mechanisms linking the AMV and NAO, and the role of diabatic processes in these feedbacks, remain debated. We present a data-driven dynamical modelling framework which captures coupled decadal variability in AMV, NAO, and North Atlantic precipitation. Applying equation discovery methods to observational data, we identify low-order models consisting of three coupled ordinary differential equations. These models reproduce observed decadal variability and show robust out-of-sample predictive skill on multi-annual to decadal lead times. The resulting model dynamics include a distinct quasi-periodic 20-year oscillation consistent with a damped oceanic mode of variability. Notably, precipitation-related terms feature prominently in the low-order models, suggesting an important role for latent heat release and freshwater fluxes in mediating ocean–atmosphere interactions. We propose new feedback mechanisms between North Atlantic sea surface temperature and the NAO, with precipitation acting as a dynamical bridge. Overall, these results illustrate how equation discovery can provide mechanistic hypotheses and new insight beyond conventional analyses of observations and climate model simulations.
The Coupled Model Intercomparison Project Phase 6 (CMIP6) archive was analysed for the occurrence of Strong Nonlinear Surprises (SNS) in future climate-change projections. To this end, we built an automated detection algorithm to identify SNS in a reproducible manner. Two different types of SNS were addressed: abrupt changes measured over decadal timescales and state transitions developing over multiple decades, too large to be explained by the forcing without invoking strong internal feedbacks in the climate system. We also discuss when one SNS case features a cascade from one variable to another (where sea-ice, deep convection, and temperature and salinity changes interact) and when two SNS interact to form a cascade, which occurs for the subpolar mixed-layer collapse triggering an Atlantic Meridional Overturning Circulation (AMOC) shutdown. Data from 54 models were analysed for five shared socio-economic pathways for ocean, sea ice, and atmospheric variables that are involved in the coupling to ocean and sea ice. The algorithm isolates regions of at least 106 km2 and utilizes stringent criteria to select SNS. In total 73 SNS were found, divided in 11 categories of which 4 apply to abrupt change and 7 to state transitions. Of the identified SNS 45 % relate to sea-ice cover, 19 % to ocean currents, 29 % to mixed-layer depth, and 7 % to atmospheric systems like the Intertropical Convergence Zone. For each category, probability density functions for time-windows of maximal change indicate SNS occurring earlier and at lower global temperature rise than assessed in previous reviews, in particular the ones associated with winter Arctic Sea ice disappearance, northern North Atlantic winter mixed-layer collapse and subsequent transition of the AMOC to a weak state in which the cell associated with North Atlantic Deep Water formation has vanished.
Atmospheric rivers (ARs) transport vast amounts of water vapor and cause weather extremes. However, they have typically been studied as isolated events rather than as components of a global transport system.By mapping ARs worldwide, we reveal that their transport is organized along a sparse set of preferred pathways forming a global network.Recognizing ARs as a globally interconnected system is critical not only for advancing atmospheric science but also for improving forecasts of extreme precipitation, droughts, and polar ice melt under climate change. Beyond the familiar storm tracks, we identify hubs of pronounced vapor transport changes and demonstrate that polar regions act as structural convergence zones for persistent ARs.ARs preferentially travel along circumglobal atmospheric highways shaped by teleconnection patterns and circulation regimes, providing new opportunities for AR prediction. While previous research recognized only five AR basins, we uncover a larger, hierarchically organized set of interconnected basins that provides a vastly improved understanding of how regional AR hotspots are embedded within large-scale flow.The global AR transport network links synoptic storms to planetary circulation, illuminating hidden pathways in the global water cycle.
Abstract. Boreal permafrost over the Northern Hemisphere high latitudes, defined as areas where the ground temperature is below 0 °C for two or more years, stores more than twice as much carbon as the atmosphere. Therefore, thawing of the permafrost, a tipping element, due to global warming may lead to additional carbon emissions and accelerate the warming. To investigate the permafrost response to increase and decrease of CO2 emissions, we conducted a series of numerical experiments using an emission-driven Earth System Model, MIROC-ES2L, and adopting idealized overshooting scenarios in which a prescribed CO2 emission of 10 PgC yr−1 is given until the global warming level reaches different values between 2 and 8 °C followed by the negative emission until the cumulative emission becomes zero. We found that the response of permafrost area to surface warming and cooling is reversible but has hysteresis for all the emission scenarios. Furthermore, the permafrost property such as the ratio of frozen to liquid water was shown to have irreversibility in the deep soil layer; part of the frozen area in the initial condition was replaced by a mixed water-ice area in the final state despite ground temperature returning almost to the initial condition. Sensitivity experiments reveal that the hysteresis and irreversibility are attributed to the delay of the soil freezing and melting associated with the soil heat conductivity and specific heat of water phase change. This result indicates that once permafrost thaws with warming it will continue for decades after warming diminishes and the delay in the permafrost recovery is larger at global warming levels greater than 2 °C. An offline calculation shows that the additional CO2 emission during the permafrost hysteresis cycle accounts for about 0.6 %–41 % of the cumulative carbon emission released from thawed permafrost in flat10 and NEC experiments.
The climate in Europe is warming faster than the global average, raising concerns about how climate change will affect extreme fire events. In this study, we use ERA5-Land reanalysis data and an ensemble of 33 high-resolution regional climate models (RCMs) from the EURO-CORDEX framework to compute the Canadian Forest Fire Weather Index (FWI) and investigate both recent and projected changes in atmospheric conditions favorable for wildfires across Europe. Historical trends (1950-2023) based on ERA5-Land data reveal statistically significant increases in the frequency and intensity of extreme fire weather in regions such as the Iberian Peninsula, Central Europe, and parts of Eastern Europe. All RCM input fields were bias-adjusted prior to FWI calculation using Quantile Delta Mapping, resulting in improved FWI representation relative to unadjusted simulations. Projections based on the bias-adjusted EURO-CORDEX ensemble indicate that future extreme fire weather will become more frequent, more intense, and more widespread across Europe as global warming progresses. The strongest signals are projected for southern Europe, with a northward expansion of fire-prone conditions under higher global warming levels (GWLs). At 3 degrees C GWL, the spatial extent of robust changes in extreme fire weather metrics nearly doubles compared to 2 degrees C, with one metric increasing fivefold. Relative increases in frequency-based metrics generally exceed those in magnitude-based metrics. These changes coincide with rising vapor pressure deficit, suggesting that thermodynamic processes play a key role through atmospheric drying. The projected intensification of extreme fire weather in Europe highlights the growing need for coordinated climate action along with proactive mitigation strategies.
Under historical warming, terrestrial ecosystems within the northern high latitudes have been a net carbon sink, providing vital mitigation against anthropogenic emissions of CO2. However, the long-term stability of this net sink is uncertain due to complex carbon cycle feedbacks in response to future climate change. Here, the PRIME framework is used to probabilistically quantify if and when this region will transition from a net carbon sink to a carbon source in a range of plausible future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), including overshoot (SSP5-3.4-OS). JULES - the land surface model component of PRIME, has the capability to explicitly simulate permafrost physics, dynamic vegetation and fire; key processes within northern high-latitude terrestrial ecosystems that are yet to be coupled together in Earth system models. In a low emission scenario, permafrost carbon emissions increase the risk of northern high latitudes becoming a net carbon source by more than 50 % at 2 degrees C of warming, and at greater levels of warming in high emission scenarios. Conversely, in all emission scenarios dynamic vegetation is found to limit the sink-to-source transition at all warming levels by enhancing the carbon sink. Fire emissions can further weaken the sink by reducing its resilience to warming. A high temperature overshoot further limits the resilience of the carbon sink due to a reduction in temperatures after the peak, providing less optimal conditions for vegetation growth. These results highlight the importance of vegetation on the strength of the Arctic terrestrial carbon sink under warming and emphasise the need for representing comprehensive terrestrial climate feedbacks in Earth system models to improve projections of the land carbon response in future climate change trajectories.
Recent studies have highlighted that state-of-the-art climate models are not able to simulate the large observed trend in Earth's energy imbalance. Here we evaluate climate models' ability to represent both the trend and the magnitude of the imbalance, while accounting for model energy leakage and remnant drift. As reference we use satellite observations and we find that every observed annual mean energy imbalance is within the range simulated by models, including the record year 2023, and when averaged over the 2001-2025 period, 13 out of 30 models simulate magnitudes of the imbalance that are statistically consistent with the observations. Models, however, generally underestimate the positive trend in the energy imbalance, albeit barely within the range of uncertainty. We suspected that a discontinuity in volcanic forcing between the historical and future scenario in 2014-2015 could have caused the underestimated trend, but only found evidence of such artifacts for a few models. Finally, we find a weak correlation between short-term decadal warming and energy imbalance, but a surprisingly close relationship between energy imbalance and equilibrium climate sensitivity. Based on observational constraints, the relationship suggests that models with climate sensitivities of 3 to 5 K best simulate the observed energy imbalance.
This study presents regional simulations over the Iberian Peninsula between 2010 and 2022 with the atmospheric (ICOLMDZ) and land surface (ORCHIDEE) components of the IPSL climate model in a new limited area model configuration (25 km resolution). It uses a recently developed river routing and irrigation scheme based on a water-conservative supply-and-demand approach. Two simulations, with and without irrigation, are compared to isolate the impacts of simulated irrigation on land-atmosphere interactions and the water cycle. First, an evaluation of the simulations is conducted to characterize existing model biases in river discharge, precipitation, evapotranspiration (ET) and surface soil moisture (SSM), and assess whether they can be improved by simulating irrigation. The simulated irrigation is too low in southern Spain because of a lack of available water in the reservoirs, and likely because of the absence of representation of river dams. In northern regions such as the Ebro Valley, the simulated irrigation is more realistic and reduces the biases of river discharge and ET in summer and autumn. In general, SSM is not strongly impacted by irrigation as most additional water is evaporated. Second, atmospheric changes induced by irrigation are studied in summer (JJA). Large atmospheric responses are found over intensely irrigated areas, mainly consisting of a shift in energy partitioning between the turbulent fluxes (increase in latent heat flux and decrease in sensible heat flux, up to 50 Wm-2), and a lowering of the atmospheric boundary layer (-100 m) and of the lifting condensation level (-250 m). Increases in precipitation are statistically significant only over the mountainous areas surrounding the Ebro Valley, and are closely linked to increases in convective available potential energy. Finally, atmospheric moisture recycling over the Iberian Peninsula is identified by showing that the increase in ET in the presence of irrigation exceeds the amount of water added by irrigation. This is made possible by an increase in precipitation over land, although most of this increase is located in lightly irrigated areas rather than in intensively irrigated areas. These results point to remote atmospheric effects of irrigation and motivate further investigation into land-atmosphere coupling processes in the presence of irrigation in the IPSL model.
The Earth's climate sensitivity remains a significant source of uncertainty in climate projections. A key metric is the Transient Climate Response (TCR), which incorporates aspects of Equilibrium Climate Sensitivity (ECS), ocean heat uptake and pattern effects, and is closely correlated with historical global warming by Earth System Models (ESMs). CMIP6 ESMs display a wider range of TCR values compared to earlier phases, with many exceeding the IPCC AR6 very likely (90 % confidence) range of 1.2-2.4 K. These high-sensitivity models also predict that warming will exceed the 2 degrees C Paris climate agreement limit, even under the relatively low emissions SSP1-2.6 scenario. Record global temperatures in 2023 and 2024 highlight how close the world already is to 1.5 degrees C of warming, raising doubts about whether the 2 degrees C limit remains within reach. Here, we use the latest observational data to update emergent constraints on TCR and projected warming. We estimate a TCR of 1.81 K with a very likely range of 1.28 to 2.33 K, which represents a small increase compared to estimates that use observational data through to 2019. Furthermore, we find that warming projections constrained by data through to 2024 fall within the low to mid-range of CMIP6 ESM projections for both the mid- and late-21st century, indicating that limiting global warming to below 2 degrees C remains feasible.
Precipitation partitioning into blue (runoff) and green water (transpiration) flows is a fundamental hydroecological process shaping freshwater availability across vegetated and hydrologically active land areas. This partitioning is determined by interactions among climatic conditions, land surface characteristics, and vegetation dynamics, which change with rising temperatures and CO2 concentrations. Yet, future global shifts in blue-green water partitioning, their controlling factors, and their broader implications remain uncertain. We address this knowledge gap using Earth system model simulations and define the Blue-Green Water Share (BGWS) metric to quantify changes in the relative partitioning of precipitation into runoff and transpiration. Here, we show that projected BGWS changes are spatially heterogeneous rather than dominated by a uniform global shift. Increases in extreme five-day precipitation are most strongly associated with these changes, favouring larger blue water shares. This effect is independent of mean precipitation increases and occurs under both drying and wetting conditions. Additionally, increases in leaf area index tend to favour larger green water shares and counteract the blueward influence of stronger precipitation extremes. Our results provide a process-based perspective on projected blue-green water partitioning and its hydroecological implications.
Fire schemes within Earth System Models capture long-term historical trends in burnt area, but they struggle to reproduce the pronounced decline observed over the past two decades. This study investigates whether the observed decline in global burnt area during 1998-2016 can be better represented in the JULES-INFERNO fire model by introducing a globally uniform dependence on the Human Development Index (HDI) as a proxy for socio-economic fire controls.This approach substantially reduces regional biases in annual burned area. In Temperate North America, model bias decreases from +735.57 % to +44.46 %, with similarly large reductions in Central America, Southern Hemisphere South America, Europe, and the Middle East. HDI also improves the representation of burned area trends in eight of the 14 GFED4s regions with significant negative trends in observations. However, correcting large positive regional biases removes compensating errors in the original model, leading to a stronger global negative bias, which shifts from -34.35 Mha in JULES-INFERNO to approximately -111 Mha in JULES-INFERNO with the HDI implementation.Overall, while HDI improves regional performance and better captures observed downward trends in some regions, it also reduces interannual variability and underestimates larger fires. This highlights both the potential and limitations of representing socio-economic influences within fire models using a simplified globally uniform formulation.
The future evolution of the Greenland ice sheet (GrIS) depends on the rate and intensity of climate change and can transition to a mostly ice-free state under strong enough global warming. By applying different rates of temperature change in a state-of-the-art ice-sheet model coupled to a regional energy-moisture balance atmospheric model, oscillations in the total ice-sheet volume are found under warming magnitudes between 1.0 and 1.3 K above present-day temperatures. These oscillations are due to two ice streams located in the northern GrIS that each alternate between fast streaming and stagnation, manifesting in build-up/surge variability. These ice streams interact due to their spatial proximity, resulting in irregular periodicity. The ice streams are situated in a region where the collapse of the GrIS to an ice-free state initiates, impacting the time it takes before this transition occurs. For a fixed warming magnitude and an ensemble of warming rates and initial conditions, the timing of the collapse can differ by tens or hundreds of thousands of years. This delay is proposed to be due to a chaotic transient, suggesting that ice-stream oscillations are a potential source of internal chaotic variability in ice sheets and can complicate prospects of anticipating a collapse.
Seasonal predictability is an active field of research given its strong potential to guide decision-making in many societal and economic sectors. In this study, we compare the predictive skill of the climate model EC-Earth3 at two different horizontal resolutions. The standard resolution – SR – (high resolution – HR) is of around 70 (40) km in the atmosphere and 100 (25) km in the ocean. Both forecast systems are initialised in the same way in May and cover the period 1990–2015, with a forecast period of 8 months. We focus on the Tropical Pacific, and particularly on El Niño Southern Oscillation (ENSO), the main source of predictability at seasonal timescales. Small but statistically significant improvements are found in HR with respect to SR for predicting ENSO, skill improvements that cannot be generalized to all regions and initialization times. In the May initialised predictions, skill drops quickly in the Western Equatorial Pacific (WEP) in both configurations, more pronouncedly in SR. The poor skill in the WEP is directly linked to a misrepresentation of its relationship with the ENSO region, which is ultimately associated with an overly strong westward extension of ENSO-related variability, a model error more pronounced in SR. This erroneous spatial simulation of ENSO is related to the mean cold bias of the cold tongue, which progressively extends westwards with the forecast time. An overly weak air-sea coupling, more pronounced in SR, prevents the model from simulating the correct ENSO development. We also show that a more realistic simulation of the Atlantic Niño teleconnection with the tropical Pacific in HR compared to SR leads to better ENSO prediction. Increasing model resolution can improve the predictive skill of forecast systems in certain regions and for certain seasons by improving the simulation of the mean state and atmospheric teleconnections. However, ENSO simulation errors and mean state biases need to be better understood to improve forecasts, in particular in the WEP, a region of convection particularly important for teleconnections to extratropics.
Strong near-surface westerly winds drive the Southern Ocean circulation and play a key role in setting regional and global climate. In the latter half of the 20th century, depletion of stratospheric ozone over Antarctica has caused these winds to accelerate and move polewards, particularly in austral summer. However, the future evolution of these winds remains uncertain. We use reanalysis data and the UK Earth System Model (UKESM1), with full atmospheric chemistry, to assess the drivers of winds over the recent past and coming century. We first characterize the wind mean state, distribution, and trends over 1980-2019 in the most commonly used atmospheric reanalyses (ERA5, JRA3Q, and MERRA2) to gain insight into observed wind behaviour in the past. We show that while the representation of the mean wind is similar among reanalyses, MERRA2 shows stronger wind acceleration trends that persist year-round, while JRA3Q and ERA5 show weaker acceleration, primarily in austral summer. Using an observational Southern Annular Mode (SAM) index, we show that the weaker, summer-focused trends of JRA3Q and ERA5 are likely more realistic. UKESM1 represents historical trends in winds accurately compared to ERA5 and is within the range of other CMIP6 models for wind and SAM trends over the historical period. Targeted simulations with UKESM1 show ozone depletion is overwhelmingly responsible for the wind acceleration observed in 1980-2020, primarily in austral summer. The effect of ozone depletion on wind speeds peaks in 1980-2000, when it is roughly double that for the entire 40-year period. Ozone recovery is then associated with a slowdown of winds from 2000 to 2050. Beyond 2050, the ozone effect becomes minimal and winds accelerate primarily due to greenhouse gas induced warming, with this trend more evenly distributed across seasons.
The land carbon cycle currently absorbs about one-third of anthropogenic CO2 emissions, but multi-model studies project a future weakening of this sink and a possible shift to a carbon source. Large inter-model differences limit confidence in these projections, and some of these discrepancies may arise from parameter uncertainty. Parameter optimization using global Earth observations could reduce this uncertainty, but it is computationally expensive and complicated by equifinality, where different parameter combinations yield similar performance through compensating effects. To address these challenges, this study uses a genetic algorithm to optimize 28 model parameters against 13 global observation datasets. A Gaussian process emulator is then used to approximate the relationship between model performance and parameter values, explore equifinality, and identify alternative parameter sets with comparable performance. These sets are used to generate an ensemble of simulations, providing an estimate of uncertainty associated with parameter optimization. Results show that optimization improves global model performance, especially for leaf area index and gross primary productivity (GPP). Optimized global GPP decreases by 5 %, resulting in a 61 % reduction in global net biome productivity (NBP) compared with the default simulation. While equifinality arises from compensating effects among many parameters, the reductions in GPP and NBP remain robust and are confirmed by the emulator-derived parameter sets. These findings highlight that parameter tuning can substantially alter carbon fluxes, and modelling groups should integrate advanced parameter optimization frameworks into their development cycle.
Climate change is expected to increase the frequency and severity of Multi-Year Droughts (MYDs), but their impacts on vegetation remain poorly understood. While satellite records offer valuable insights, they only cover recent decades, limiting the number of MYDs available for analysis. The dynamic global vegetation model LPJmL-5 can simulate vegetation dynamics under varying climate conditions and over longer temporal scales than are typically available from satellite observations. However, its ability to capture vegetation responses to drought, including MYDs, has not yet been systematically evaluated against observation-based datasets. In this study, we benchmark LPJmL-5 against MODIS-derived gross primary production (GPP) to assess how well the model reproduces vegetation responses to drought. We find that LPJmL-5 captures the key temporal and spatial dynamics of drought-related GPP observed by MODIS, although notable differences remain. In particular, LPJmL-5 tends to overestimate vegetation response at the onset of MYDs and shows some rapid recovery behaviour, resulting in muted overall drought impacts. Vegetation responses also vary by type: drought dynamics in croplands are captured relatively well, whereas responses in boreal and temperate vegetation are underestimated in magnitude. These discrepancies appear to be linked to simplified model representations of vegetation stress and mortality, which limit long-term vegetation loss. Beyond drought conditions, LPJmL-5 reproduces absolute GPP values reasonably well in some regions, but performance declines in parts of the Southern Hemisphere and in cropland-dominated areas. This suggests that general GPP simulation performance is not necessarily linked to performance during drought conditions. Overall, this benchmarking highlights strengths and limitations in how LPJmL-5 represents vegetation responses to drought and provides a foundation for future studies of vegetation responses to multi-year droughts.
