Uncertainty In Future Projections Research Articles

Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades

Greenland’s glaciers have been retreating, thinning and accelerating since the mid-1990s, with the mass loss from the Greenland Ice Sheet (GrIS) now being the largest contributor to global sea level rise. Monitoring changes in glacier dynamics using in-situ or remote sensing methods has been and remains therefore crucial to improve our understanding of glaciological processes and the response of glaciers to changes in climate. Over the past two decades, significant advances in technology have provided improvements in the way we observe glacier behavior and have helped to reduce uncertainties in future projections. This review focuses on advances in in-situ monitoring of glaciological processes, but also discusses novel methods in satellite remote sensing. We further highlight gaps in observing, measuring and monitoring glaciers in Greenland, which should be addressed in order to improve our understanding of glacier dynamics and to reduce in uncertainties in future sea level rise projections. In addition, we review coordination and inclusivity of science conducted in Greenland and provide suggestion that could foster increased collaboration and co-production.

Assessment of the Global Relationship of Different Types of Droughts in Model Simulations Under High Anthropogenic Emissions

AbstractThis study provides a global analysis of the relationship between drought metrics obtained from several climatic, hydrologic and ecological variables in a climate change framework using CMIP6 model data. A comprehensive analysis of the evolution of drought severity on a global scale is carried out for the historical experiment (1850–2014) and for future simulations under a high emissions scenario (SSP5‐8.5). This study focuses on comparing trends in the magnitude and duration of drought events according to different standardized indices over the world land‐surface area. The spatial and temporal relationship between the different drought indices on a global scale was also evaluated. Overall, there is a fairly large consensus among models and drought metrics in pointing to drought increase in southern North America, Central America, the Amazon region, the Mediterranean, southern Africa and southern Australia. Our results show important spatial differences in drought projections, which are highly dependent on the drought metric employed. While a strong relationship between climatic indices was evident, climatic and ecological drought metrics showed less dependency over both space and time. Importantly, our study demonstrates uncertainties in future projections of drought trends and their interannual variability related to the relationship among indices, stressing the importance of coherent climatic, hydrological and plant physiological patterns when analyzing CMIP6 model simulations of droughts under a warming climate scenario.

Asian Anthropogenic Aerosol Forcing Played a Key Role in the Multidecadal Increase in Australian Summer Monsoon Rainfall

Abstract Observations show a significant increase in Australian summer monsoon (AUSM) rainfall since the mid-twentieth century. Yet the drivers of this trend, including the role of anthropogenic aerosols, remain uncertain. We addressed this knowledge gap using historical simulations from a suite of Coupled Model Intercomparison Project phase 6 (CMIP6) models, the CESM2 Large Ensemble, and idealized single-forcing simulations from the Precipitation Driver Response Model Intercomparison Project (PDRMIP). Our results suggest that Asian anthropogenic aerosol emissions played a key role in the observed increase in AUSM rainfall from 1930 to 2014, alongside the influence of internal variability. Sulfate aerosol emissions over Asia led to regional surface cooling and strengthening of the climatological Siberian high over eastern China, which altered the meridional temperature and sea level pressure gradients across the Indian Ocean. This caused an intensification and southward shift of the Australian monsoonal westerlies (and the local Hadley cell) and resulted in a precipitation increase over northern Australia. Conversely, the influence of increased greenhouse gas concentrations on AUSM rainfall was minimal due to the compensation between thermodynamically induced wettening and transient eddy-induced drying trends. At a larger scale, aerosol and greenhouse gas forcing played a key role in the climate response over the Indo-Pacific sector and eastern equatorial Pacific, respectively (coined the “tropical Pacific east–west divide”). These findings contribute to an improved understanding of the drivers of the multidecadal trend in AUSM rainfall and highlight the need to reduce uncertainties in future projections under different aerosol emission trajectories, which is particularly important for northern Australia’s agriculture. Significance Statement Australian summer monsoon (AUSM) rainfall plays a vital role in sustaining northern Australia’s unique biodiversity and extensive agricultural industry. While observations show a significant increase in AUSM rainfall since the mid-twentieth century, the causes remain uncertain. We find that anthropogenic aerosol emissions from Asia played a key role in driving this multidecadal AUSM rainfall trend by inducing dynamic adjustments over the Indo-Pacific sector. These findings highlight the need to consider different aerosol emission trajectories when assessing future projections of AUSM rainfall.

Assessing Arctic wetting: Performances of CMIP6 models and projections of precipitation changes

The Arctic region is experiencing a notable increase in precipitation, known as Arctic wetting, amidst the backdrop of Arctic warming. This phenomenon has implications for the Arctic hydrological cycle and numerous socio-ecological systems. However, the ability of climate models to accurately simulate changes in Arctic wetting has not been thoroughly assessed. In this study, we analyze total precipitation in the Arctic using station data, multiple reanalyses, and 35 models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). By employing the moisture budget equation and an evaluation method for model performance with ERA5 reanalysis as a reference, we evaluated the models' capability to reproduce past Arctic wetting patterns. Our findings indicate that most reanalyses and models are able to replicate Arctic wetting. However, the CMIP6 models generally exhibit an overestimation of Arctic wetting during the warm season and an underestimation during the cold season from 1979 to 2014 when compared to the ERA5 reanalysis. Further investigation reveals that the overestimation of wetting during the warm season is largest over the Arctic Ocean's northern part, specifically the Canadian Arctic Archipelago, and is associated with an overestimation of atmospheric moisture transport. Conversely, the models significantly underestimate wetting over the Barents-Kara Sea during the cold season, which can be attributed to an underestimation of evaporation resulting from the models' inadequate representation of sea ice reduction in that region. The models with the best performance in simulating historical Arctic wetting indicate a projected intensification of Arctic wetting, and optimal models significantly reduce uncertainties in future projections compared to the original models, particularly in the cold season and oceanic regions. Our study highlights significant biases in the CMIP6 models' simulation of Arctic precipitation, and improving the model's ability to simulate historical Arctic precipitation could reduce uncertainties in future projections.

Assessment of Warm and Dry Bias over ARM SGP Site in E3SMv2 and E3SM-MMF

Abstract Many climate models exhibit a dry and warm bias over the central United States during the summer months, including the Energy Exascale Earth System Model (E3SM) and its Multiscale Modeling Framework (MMF) configuration. Understanding the causes of this bias is important to shine a light on this common model error and reduce the uncertainty in future projections. In this study, we use E3SMv2 and E3SM-MMF to assess how parameterized and resolved convection affect temperature and precipitation biases over the Southern Great Plains site of the Atmospheric Radiation Measurement program. Both configurations overestimate near-surface temperature and underestimate precipitation at the ARM SGP site. The bias is associated with a lack of low-level clouds during days without precipitation and too much incoming solar radiation causing the surface to warm. Low-level cloud fraction in E3SM-MMF during the nonprecipitating days is lower in comparison to E3SMv2 and observation, consistent with the larger warm bias. We also find that the underestimated precipitation can be characterized as “too frequent, too weak” in E3SMv2 and “too rare, too intense” in E3SM-MMF. These deficiencies conspire to sustain the warm and dry bias over the central United States.

ASSESSMENT OF UNCERTAINTY IN ESTIMATING FUTURE EXTREME STORM SURGE EVENTS IN OSAKA BAY USING LARGE ENSEMBLE TYPHOON DATA

Risk assessment of low-frequency catastrophic events requires observation/simulation data of a large number of disaster events. When we consider the risk of storm surge, high waves, and strong wind caused by tropical cyclone, the way of stochastic tropical cyclone model approach has been conducted. The stochastic model can create a lot of synthetic tropical cyclone track data which has same statistic characteristics with historical data got in short period. In addition, climate change impact assessment requires comparison of many ensemble results (models and scenarios) in order to consider the uncertainty of future projections. However, there are few proposals for stochastic models that can predict the future climate change effects. Even if a large amount of tropical cyclone track data is produced, it is practically impossible to simulate disasters for all of them. For this reason, methods for calculating storm surge and storm waves using ANN models and regression models have been proposed, but the validity of data screening using such simplified methods needs to be further investigated. This study examines the valid ity of the newly developed regression model and its applicability to the screening of hazardous storm surge events. Future changes in storm surge risk in Osaka Bay are analyzed by combining the regression model results with detailed numerical methods.

Implications of Warm Pool Bias in CMIP6 Models on the Northern Hemisphere Wintertime Subtropical Jet and Precipitation

AbstractAlthough the multi‐model average compares well with observations, individually most of the latest climate models do not simulate a realistic size of the Indo‐Pacific Warm Pool in the present‐day climate. This study explores the implications of this warm pool size bias in climate models in Northern Hemisphere winter. The warm pool size bias in phase 6 of the Coupled Model Intercomparison Project models is related to the subtropical jet and precipitation distribution, both in the present‐day climate and in response to climate change, through extratropical Rossby wave trains and tropical circulation pathways. Based on these relationships, emergent constraints are developed to observationally constrain the future subtropical jet response over Asia and the Atlantic Ocean and precipitation response over North and Central America, which can help to reduce uncertainty in future projections of these features. Thus, accurate model simulation of the warm pool in the present‐day climate is important for future projections of the subtropical jet and precipitation.

Different uncertainty in tropical oceanic and land precipitation sensitivities under global warming

The change in precipitation (P) under global warming has far-reaching implications for society. However, the high degree of intermodel uncertainty in future projections undermines the reliability of the multi-model mean. The present study investigates the sources of intermodel uncertainty in tropical-mean oceanic and land P sensitivities under global warming, based on an analysis of 28 models from CMIP6. Results show that the uncertainty in tropical-mean P sensitivity is greater over ocean compared to that over land, and both are closely tied to uncertainties in tropical-mean circulation sensitivity. Nevertheless, the underlying drivers of the uncertainty in tropical-mean circulation sensitivity vary between ocean and land. For the tropical-mean oceanic part, the tropical sea surface temperature (SST) warming pattern, particularly the SST warming over the western equatorial Pacific, plays a crucial role by modifying the atmospheric stability over the tropical Pacific. On the other hand, for the tropical-mean land part, the combined impacts of changes in land sea level pressure, relative humidity and SST warming over the equatorial Pacific contribute to the intermodel uncertainty in circulation sensitivity. The results highlight the importance of improving the simulation of these factors to reduce the discrepancies in tropical-mean oceanic and land circulation sensitivities among climate models, ultimately leading to a more accurate depiction of P sensitivities under global warming.

Effects of nitrogen addition on the combined global warming potential of three major soil greenhouse gases: A global meta-analysis

Increased nitrogen (N) deposition has a great impact on soil greenhouse gas (GHG) emissions, and numerous studies have revealed the individual effects of N addition on three major GHGs (CO2, CH4, and N2O). Nevertheless, quantitative evaluation of the effects of N addition on the global warming potential (GWP) of GHGs based on simultaneous measurements is needed not only to better understand the comprehensive effect of N deposition on GHGs but also for precise estimation of ecosystem GHG fluxes in response to N deposition. Here, we conducted a meta-analysis using a dataset with 124 simultaneous measurements of the three major GHGs from 54 studies to assess the effects of N addition on the combined global warming potential (CGWP) of these soil GHGs. The results showed that the relative sensitivity of the CGWP to N addition was 0.43%/kg N ha−1 yr−1, indicating an increase in the CGWP. Among the ecosystems studied, wetlands are considerable GHG sources with the highest relative sensitivity to N addition. Overall, CO2 contributed the most to the N addition-induced CGWP change (72.61%), followed by N2O (27.02%) and CH4 (0.37%), but the contributions of the three GHGs varied across ecosystems. Moreover, the effect size of the CGWP had a positive relationship with N addition rate and mean annual temperature and a negative relationship with mean annual precipitation. Our findings suggest that N deposition may influence global warming from the perspective of the CGWP of CO2, CH4, and N2O. Our results also provide reference values that may reduce uncertainties in future projections of the effects of N deposition on GHGs.

Performance evaluation of CMIP6 GCMs for the projections of precipitation extremes in Pakistan

Extreme weather events are more detrimental to human culture and ecosystems than typical weather patterns. A multimodel ensemble (MME) of the top-performing global climate models (GCMs) to simulate 11 precipitation extremes was selected using a hybrid method to project their changes in Pakistan. It also compared the benefits of using all GCMs compared to using only selected GCMs when projecting precipitation extremes for two future periods (2020–2059) and (2060–2099) for four shared socioeconomic pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. Results showed that EC-Earth3-Veg, MRI-ESM2-0 and NorESM2-MM performed best among GCMs in simulating historical and projecting future precipitation extremes. Compared to the MME of all GCMs, the uncertainty in future projections of all precipitation indices of the selected GCMs were significantly smaller. The MME median of the selected GCMs showed increased precipitation extremes over most of Pakistan. The greater increases were in RX1day by 6–12 mm, RX5day by 12–20 mm, Prcptot by 40–50 mm, R95ptot by greater than 30 mm, R99ptot by more than 9 mm, R4mm ≥ 4 mm by 0–4 days, R10mm by 2–6 days, R20mm by 1–3 days, and SDII by 1 mm/day, CWD by one day, CDD by 0–4 days in the northern high elevated areas for SSP5-8.5 in the late future. These results emphasize the greater influence of climate change on precipitation extremes in the northern, high-elevation areas, which provide the majority of the country’s water. This emphasizes the necessity to adopt suitable climate change mitigation strategies for sustainable development, particularly in the country's northern regions.

Variability in Projected North American Mean and Extreme Temperature and Precipitation Trends for the 21st Century: Model‐To‐Model Differences Versus Internal Variability

AbstractWhile climate change is expected to alter mean and extreme temperature and precipitation over the 21st century, regional changes in these fields remain difficult to project. For a given future emissions scenario, the uncertainty in future projections can be attributed to either model‐to‐model differences, which can presumably be reduced if models are improved, or internal variability, which cannot be constrained to a single outcome but can be used to quantify a range of plausible outcomes. This study examines the range of possible trends in North American mean and extreme temperature and precipitation across phase 6 of the Coupled Model Intercomparison Project models under the SSP3‐7.0 emissions scenario. While model‐to‐model differences primarily govern the spread in mean and extreme temperature trends across these model simulations, the spread in mean and extreme precipitation trends is dominated by internal variability in many regions. A storylines approach—a type of regression‐based analysis—is used to identify key dynamical drivers that explain the variance in future trends across model simulations. The dynamical drivers considered include trends in global mean surface temperature (GMST) and four common modes of climate variability. Combinations of these drivers can reinforce each other to create high‐impact storylines. For example, model simulations with large positive GMST and El Niño‐Southern Oscillation trends are associated with a large reduction in the annual maximum number of consecutive dry days in the American Southwest. However, limiting these storylines to only models with climate sensitivities consistent with observational constraints reduces the likelihood of a large reduction of consecutive dry days in this region.

Climate Change Impacts on Irish River Flows: High Resolution Scenarios and Comparison with CORDEX and CMIP6 Ensembles

Climate change is likely to impact water quality, resource availability and riverine ecosystems. While large ensembles are available to assess future impacts (e.g., the Coupled Model Intercomparison Projects (CMIP) and/or CORDEX) many countries have developed their own high-resolution ensembles. This poses a selection problem with robust adaptation dependent on plausible ranges of change being adequately quantified. Therefore, it is important to compare projected changes from available ensembles. Here we assess future climate change impacts for 26 Irish catchments. Using a high-resolution national ensemble of climate models projected impacts in mean, low and high flows are assessed and uncertainties in future projections related to catchment size. We then compare future impacts from CORDEX and CMIP6 ensembles for a subset of catchments. Results suggest increases in winter flows (-3.29 to 59.63%), with wide ranges of change simulated for summer (-59.18 to 31.23%), low (-49.30 to 22.37%) and flood (-19.31 to 116.34%) flows across catchments under RCP8.5 by the 2080s. These changes would challenge water management without adaptation. Smaller catchments tend to show the most extreme impacts and widest ranges of change in summer, low and flood flow changes. Both the ensemble mean and range of changes from the national ensemble were more modest and narrower than the CMIP6 and CORDEX ensembles, especially for summer mean and low flows, highlighting the importance of evaluating impacts across ensembles to ensure adaptation decisions are informed by plausible ranges of change.

Uncertainty in Future Projections of Precipitation Decline over Mesopotamia

Abstract For millennia, Mesopotamia was blessed by enough water supplied by the Tigris and Euphrates Rivers. However, the dwindling freshwater resource is no longer enough. In the future, climate change coupled with a growing population could considerably exacerbate the current water deficit. Based on simulations by carefully selected global and regional climate models, we conclude that these river basins may possibly face further water shortages (mainly due to a reduction in spring-season precipitation) in the next few decades (2021–50) under a scenario of high emissions of greenhouse gases. However, there is no consensus among models regarding these near-term (2021–50) projections of change in precipitation, and society is likely to face the challenge of how to prepare for this uncertain future. The story is different for the late decades of this century: we project, with significantly more confidence, a robust decrease in wet-season (winter to spring) precipitation over the headwaters of these river basins, worsening future water deficits and implying a century-long drying trend over Mesopotamia. Possible physical mechanisms are proposed and discussed. As global warming progresses, higher sea level pressure, centered on the Mediterranean Sea, will likely make upstream storms less frequent and weaker, leading to drying over Mesopotamia. Further, projections show a poleward migration of the fewer Mediterranean storm tracks, decreasing the frequency of storms that penetrate into Mesopotamia. Implementing a global net-zero carbon emissions policy by midcentury could mitigate the severity of the projected droughts in this region.

Changing Trends in Drought Patterns over the Northeastern United States Using Multiple Large Ensemble Datasets

Abstract Since the infamous extreme drought of the 1960s, the climate of the northeastern United States (NEUS) has generally trended toward warmer and wetter conditions. Nonetheless, there is mounting evidence that short-term droughts will continue to pose a significant risk for this region. To better explore the processes governing events such as these, climate models have adopted more complex representations of the fully coupled atmosphere–land–ocean–sea ice system; however, large uncertainties in future projections still persist, with internal variability necessitating large ensembles to understand trends in both rare and high-impact extreme events such as rapidly developing droughts (a term here that includes flash droughts developing on monthly scales). In this study, seven large ensemble (LE) models are employed to answer the outstanding question: How are the frequency and character of drought in the NEUS changing under a warming climate? We find that most LE models indicate the NEUS will experience a long-term wetting trend with more “extremely wet” months, but also more frequent short-term extreme droughts. These changes are associated with increasing precipitation, atmospheric water demand, and climate variability. We also conclude that discrepant trends in precipitation and evapotranspiration variability will lead to increasing anticorrelation of these variables, which is relevant to the intensification of rapidly developing drought, particularly in the spring season. These changes are associated with an increase in evapotranspiration from plants, brought by an earlier emergence of the growing season and denser vegetation. Significance Statement Droughts are extreme events with the potential to produce considerable social and economic damage. Because droughts emerge slowly and are relatively infrequent, large datasets are needed to study these features. Using multiple climate models, each producing multiple long-duration simulations, along with a novel drought index, we characterize rapidly developing droughts (those flash droughts identifiable from monthly data) and understand their drivers in the northeastern United States. We find that short-term extreme droughts are projected to be more frequent, while rapidly developing droughts will become more intense in the spring season. Intensification of rapidly developing droughts is attributed to differences in patterns of precipitation and evapotranspiration (soil evaporation and plant transpiration), which will be magnified by an extended growing season and an increase in vegetation.

Projections of Future Air Quality Are Uncertain. But Which Source of Uncertainty Is Most Important?

AbstractUnderstanding how air pollution events may change in the future is of key importance to decision makers. Multi‐model intercomparison projects focusing on atmospheric chemistry and air quality have been performed to inform the latest IPCC assessments. Future anthropogenic emission changes have generally been the foci of such model experiments, envisaged as the dominant driver of future atmospheric composition. The latest model assessments such as AerChemMIP utilize multi‐model ensembles but also have limited individual model ensembles which permit different sources of uncertainty to be characterized. The recent study by Fiore et al. (2022, https://doi.org/10.1029/2021JD035985) specifically considers a multi‐model and multi‐member ensemble approach. It adds to the quantification of uncertainty in future projections through delineating uncertainty due to model diversity and due to internal or natural climate variability within the climate system, for mean and high PM2.5 air pollution events over the Eastern USA in the 21st century. Exploring the separate roles of internal climate variability and model diversity adds further value to the important research issue of quantifying how future anthropogenic climate change impacts air quality. Future multi‐model intercomparisons need to balance the additional knowledge gained from research into understanding multiple sources of uncertainty that can inform decision making vs. the resource costs of performing these experiments using Earth System Models with interactive chemistry.

Elevation change of the Antarctic Ice Sheet: 1985 to 2020

Abstract. The largest uncertainty in future projections of sea level change comes from the uncertain response of the Antarctic Ice Sheet to the warming oceans and atmosphere. The ice sheet gains roughly 2000 km3 of ice from precipitation each year and loses a similar amount through solid ice discharge into the surrounding oceans. Numerous studies have shown that the ice sheet is currently out of long-term equilibrium, losing mass at an accelerated rate and increasing sea level rise. Projections of sea level change rely on accurate estimates of the contribution of land ice to the contemporary sea level budget. The longest observational record available to study the mass balance of the Earth's ice sheets comes from satellite altimeters. This record, however, consists of multiple satellite missions with different life spans and inconsistent measurement types (radar and laser) of varying quality. To fully utilize these data, measurements from different missions must be cross-calibrated and integrated into a consistent record of change. Here, we present a novel approach for generating such a record that implies improved topography removal, cross-calibration, and normalization of seasonal amplitudes from different mission. We describe in detail the advanced geophysical corrections applied and the processes needed to derive elevation change estimates. We processed the full archive record of satellite altimetry data, providing a seamless record of elevation change for the Antarctic Ice Sheet that spans the period 1985 to 2020. The data are produced and distributed as part of the NASA MEaSUREs ITS_LIVE (Making Earth System Data Records for Use in Research Environments Inter-mission Time Series of Land Ice Velocity and Elevation) project (Nilsson et al., 2021, DOI: https://doi.org/10.5067/L3LSVDZS15ZV​​​​​​​).

The Dynamic Temperate and Boreal Fire and Forest-Ecosystem Simulator (DYNAFFOREST): Development and evaluation

Fire is a dominant disturbance in temperate and boreal biomes, and increasing burned area with climate change may fundamentally alter forests. Improved information about how fire-induced changes to forests may feedback to affect subsequent burning at regional scales could inform forest management and climate-mitigation strategies. However, fire is simplistically represented in Earth System Models, and regional statistical fire models often assume sufficient fuels, contributing to uncertainty in future projections. To address this challenge, we developed the Dynamic Temperate and Boreal Fire and Forest-Ecosystem Simulator (DYNAFFOREST). DYNAFFOREST represents the hierarchical structuring of forests, from individual cohorts to continental extents, making it possible to simulate feedbacks between fire and forests at broad scales over decades to centuries. We parameterized DYNAFFOREST for the western United States of America and benchmarked simulations with observations. DYNAFFOREST recreated patterns of forest cover, structure, and downed fuels, and was capable of capturing average 20th-century fire activity.

Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)

Abstract. Ice-dynamical processes constitute a large uncertainty in future projections of sea-level rise caused by anthropogenic climate change. Improving our understanding of these processes requires ice-sheet models that perform well at simulating both past and future ice-sheet evolution. Here, we present version 2.0 of the ice-sheet model IMAU-ICE, which uses the depth-integrated viscosity approximation (DIVA) to solve the stress balance. We evaluate its performance in a range of benchmark experiments, including simple analytical solutions and both schematic and realistic model intercomparison exercises. IMAU-ICE has adopted recent developments in the numerical treatment of englacial stress and sub-shelf melt near the grounding line, which result in good performance in experiments concerning grounding-line migration (MISMIP, MISMIP+) and buttressing (ABUMIP). This makes it a model that is robust, versatile, and user-friendly, which will provide a firm basis for (palaeo-)glaciological research in the coming years.

A locally relevant framework for assessing the risk of sea level rise under changing temperature conditions: Application in New Caledonia, Pacific Ocean

Sea level rise is a key feature in a warmer world and its impact can be seen globally. Assessing climate change-induced sea level rise, therefore, is urgently needed particularly in small island nations, where the threats of sea level rise are immediate, but the level of preparedness is low. Here, we propose a stochastic simulator to link changes in Mean Annual Temperature (MAT) to Mean Annual Sea Level (MASEL) at the local scale. This is through what-if scenarios that are developed based on the association between local temperature and sea level. The model can provide a basis for a bottom-up impact assessment by addressing limitations of applying large-scale projections in small islands and facilitating the accessibility of the impact assessment to stakeholders. For this purpose, we decompose the MAT and MASEL signals into their linear trend and autocorrelation components as well as independent and identically distributed residual terms. We further explore the association between trend and residual terms of MAT and MASEL. If such dependencies exist, scenarios of sea level can be synthesized based on the trend and residual terms of temperature. We use linear regression to link trends of MAT and MASEL, and copulas to formulate dependencies between residuals. This allows stochastic sampling of MASEL conditioned to trend and random variability in MAT. This framework is used for retrospective and prospective simulations of MASEL in Nouméa, the capital city of New Caledonia, the Pacific. We set up six different model configurations for developing the stochastic sampler, each including various parametric options. By selecting the best setup from each configuration, we provide a multi-model stochastic projection of MASEL, assuming the persistence in current long-term trend in MAT and MASEL. We demonstrate how such simulations can be used for a risk-based impact assessments and discuss sources of uncertainty in future projections.

Fast local warming is the main driver of recent deoxygenation in the northern Arabian Sea

Abstract. The Arabian Sea (AS) hosts one of the most intense oxygen minimum zones (OMZs) in the world. Observations suggest a decline in O2 in the northern AS over the recent decades accompanied by an intensification of the suboxic conditions there. Over the same period, the local sea surface temperature has risen significantly, particularly over the Arabian Gulf (also known as Persian Gulf, hereafter the Gulf), while summer monsoon winds may have intensified. Here, we simulate the evolution of dissolved oxygen in the AS from 1982 through 2010 and explore its controlling factors, with a focus on changing atmospheric conditions. To this end, we use a set of eddy-resolving hindcast simulations forced with winds and heat and freshwater fluxes from an atmospheric reanalysis. We find a significant deoxygenation in the northern AS, with O2 inventories north of 20∘ N dropping by over 6 % per decade between 100 and 1000 m. These changes cause an expansion of the OMZ volume north of 20∘ N at a rate of 0.6 % per decade as well as an increase in the volume of suboxia and the rate of denitrification by 14 and 15 % per decade, respectively. We also show that strong interannual and decadal variability modulate dissolved oxygen in the northern AS, with most of the O2 decline taking place in the 1980s and 1990s. Using a set of sensitivity simulations we demonstrate that deoxygenation in the northern AS is essentially caused by reduced ventilation induced by the recent fast warming of the sea surface, including in the Gulf, with a contribution from concomitant summer monsoon wind intensification. This is because, on the one hand, surface warming enhances vertical stratification and increases Gulf water buoyancy, thus inhibiting vertical mixing and ventilation of the thermocline. On the other hand, summer monsoon wind intensification causes a rise in the thermocline depth in the northern AS that lowers O2 levels in the upper ocean. Our findings confirm that the AS OMZ is strongly sensitive to upper-ocean warming and concurrent changes in the Indian monsoon winds. Finally, our results also demonstrate that changes in the local climatic forcing play a key role in regional dissolved oxygen changes and hence need to be properly represented in global models to reduce uncertainties in future projections of deoxygenation.