Atmosphere-ocean General Circulation Model Research Articles

Closure of tropical seaways favors the climate and vegetation in tropical Africa and South America approaching their present conditions

The tectonically induced closure/constriction of the Central American Seaway (CAS) and Indonesian Seaway (Indo) during the early to mid-Pliocene has been associated with many climatic events, such as the onset of Northern Hemisphere glaciation, the intensification of the Atlantic meridional overturning circulation (AMOC) and Australian aridification. However, studies on how the closure/constriction of tropical seaways affects the tropical climate system are still sparse and not systematic. Previous studies have linked the constriction of Indo to the aridification over East Africa and discussed the role of CAS closure in affecting the moisture supply over South America, but the underlying mechanism and combined effect of both tropical seaways are not well studied. In this study, we evaluate the impacts of tropical seaways' closure/constriction and distinguish the relative roles of CAS and Indo on climate in tropical Africa and South America using the NorESM-L Atmosphere-Ocean General Circulation Model (AOGCM) and a dynamic vegetation model (LPJ-GUESS). Our results show that the closure of the CAS leads mainly to aridification in northeastern Brazil, resulting in an expansion of tropical xerophytic shrubland and savanna in this region. The narrowing of the Indo mainly leads to enhanced aridification in eastern tropical Africa, and reduces the extent of tropical forests in eastern and northern tropical Africa, which is generally consistent with the data. The closure/narrowing of the two tropical seaways results in a superposition of the individual seaway's effect, particularly over northeastern Brazil region, which exhibits enhanced aridification compared to the closure of the individual CAS. The seaways' changes are shown to be pivotal for the evolution of climate and vegetation over East Africa and northeastern South America to contemporary conditions.

A new conceptual model ofglobaloceanheatuptake.

We formulate a new conceptual model, named "MT2", to describe global ocean heat uptake, as simulated by atmosphere-ocean general circulation models (AOGCMs) forced by increasing atmospheric CO2, as a function of global-mean surface temperature change T and the strength of the Atlantic meridional overturning circulation (AMOC, M). MT2 has two routes whereby heat reaches the deep ocean. On the basis of circumstantial evidence, we hypothetically identify these routes as low- and high-latitude. In low latitudes, which dominate the global-mean energy balance, heat uptake is temperature-driven and described by the two-layer model, with global-mean T as the temperature change of the upper layer. In high latitudes, a proportion p (about 14%) of the forcing is taken up along isopycnals, mostly in the Southern Ocean, nearly like a passive tracer, and unrelated to T. Because the proportion p depends linearly on the AMOC strength in the unperturbed climate, we hypothesise that high-latitude heat uptake and the AMOC are both affected by some characteristic of the unperturbed global ocean state, possibly related to stratification. MT2 can explain several relationships among AOGCM projections, some found in this work, others previously reported: Ocean heat uptake efficiency correlates strongly with the AMOC. Global ocean heat uptake is not correlated with the AMOC. Transient climate response (TCR) is anticorrelated with the AMOC. T projected for the late twenty-first century under high-forcing scenarios correlates more strongly with the effective climate sensitivity than with the TCR.

Uncertainties in the Arctic Ocean response to CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document}: a process-based analysis

Using an ensemble of atmosphere–ocean general circulation models (AOGCMs) in an idealized climate change experiment, this study evaluates the contribution of different ocean processes to Arctic Ocean warming. On the AOGCM-mean, the Arctic Ocean warming is greater than the global ocean warming, both in the volume-weighted mean, and at most depths within the upper 2000 m. However, the uncertainty of Arctic Ocean warming is much larger than the uncertainty of global ocean warming. The Arctic warming is greatest a few 100 m below the surface and is dominated by the import of extra heat, which is added to the ocean at lower latitudes and is conveyed to the Arctic mostly by the large-scale barotropic ocean circulation. The change in strength of this circulation in the North Atlantic is relatively small and not correlated with the Arctic Ocean warming. The Arctic Ocean warming is opposed and substantially mitigated by the weakening of the Atlantic meridional overturning circulation (AMOC), though the magnitude of this effect has a large model spread. By reducing the northward transport of heat, the AMOC weakening causes a redistribution of heat from high latitudes to low latitudes. Within the Arctic Ocean, the propagation of heat anomalies is influenced by broadening of cyclonic circulation in the east and weakening of anticyclonic circulation in the west. On the model-mean, the Arctic Ocean warming is most pronounced in the Eurasian Basin, with large spread across the AOGCMs, and accompanied by subsurface cooling by diapycnal mixing and heat redistribution by mesoscale eddies.

MIROC6 Large Ensemble (MIROC6-LE): experimental design and initial analyses

Abstract. Single model initial-condition large ensembles (LEs) are a useful approach to understand the roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76 000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities. We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850–2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000–2020 relative to 1850–1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119. Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Niño/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951–2000, from 1951–2000 to 2051–2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability.

Climate Change Assessment Using Atmosphere-Ocean General Circulation Model) AOGCM( and Its Effect on Runoff Using HEC-HMS model

Climate Change Assessment Using Atmosphere-Ocean General Circulation Model) AOGCM( and Its Effect on Runoff Using HEC-HMS model

Comparison between statistical and dynamical downscaling of rainfall over the Gwadar‐Ormara basin, Pakistan

AbstractThis paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study.

Role of the tropical Indian Ocean in orbitally induced mid-Holocene precipitation variation in northwest China

Investigating long-term natural precipitation variability in Northwest China is crucial for our understanding of the recent wetting trend in this region. This study explores the mechanisms of precipitation variations in Northwest China during the mid-Holocene (MH) using the Community Earth System Model (CESM). We emphasize the role of ocean feedbacks in the response of precipitation changes in the summer half-year (April–September; the rainy season of Northwest China) to orbital forcing, via comparing simulation results from the single atmosphere general circulation model (AGCM) and fully coupled atmosphere–ocean general circulation model (AOGCM) of the CESM. The AOGCM simulations reveal a decrease of precipitation in Northwest China during the MH compared to the pre-Industrial (PI) era, matching well with proxy records. The drier MH is reproduced by the AGCM with orbital forcing when the tropical Indian Ocean cooling is taken into account. The tropical Indian Ocean cooling is affected by the decrease of spring insolation, contrasting to Eurasian warming caused by the increase of summer insolation during the MH. The tropical Indian Ocean cooling contributes to a reduction of the upper-level westerlies to the south of the westerly jet axis and hence a northward shift of the westerly jet during the MH relative to the PI, via amplifying the decrease of meridional thermal gradient from the tropical Indian Ocean to northern Eurasia and weakening the strength of the South Asian high. The northward migration of the westerly jet benefits anomalous downward motion and ultimately weakens precipitation in Northwest China during the MH. Therefore, the tropical Indian Ocean plays an important role in orbital forcing of MH precipitation variations in Northwest China. We further infer that abnormal warming of the tropical Indian Ocean is probably a key factor for the recent wetting trend in Northwest China.

Climate Response to Atlantic Meridional Energy Transport Variations

Abstract The climate responses to Atlantic meridional overturning circulation (AMOC) fluctuations are investigated in a hierarchy of sensitivity experiments. We modify the baroclinic component of the North Atlantic Ocean currents online in an atmosphere–ocean general circulation model to reproduce typical AMOC multidecadal variability found in a preindustrial control simulation in the same model. An analogous experiment is also conducted using a slab-ocean experiment. The responses to a strong AMOC include a widespread warming in the Northern Hemisphere and a northward shift of the intertropical convergence zone over the Atlantic Ocean. The driving mechanism of climate responses is then investigated with the changes in the energy flows in the ocean and atmosphere. The large-scale atmospheric changes in the tropics are organized by an anomalous cross-equatorial Hadley circulation transporting energy southward and moisture and heat northward. Changes in the Indo-Pacific Ocean circulation and heat transport, driven by the wind stress associated with the abnormal Hadley cell, damp the atmospheric responses. The lack of Indo-Pacific transport and ocean heat storage leads to amplified atmospheric changes in the slab-ocean experiments, which are further amplified by a positive feedback due to the interhemispheric antisymmetric changes in low cloud cover.

PALEO‐PGEM‐Series: A spatial time series of the global climate over the last 5 million years (Plio‐Pleistocene)

AbstractMotivationClimate change plays an important role in the generation and maintenance of biodiversity by driving processes such as diversification and range shifts. As a result, biodiversity patterns are often found to carry the imprints of palaeoclimatic changes. However, we still know little about the spatial and temporal variation in climate over the scale of millennia affecting eco‐evolutionary dynamics, mostly because of the scarcity of user‐friendly and freely available spatio‐temporal palaeoclimate series at such temporal scales. Here, we address this gap by presenting PALEO‐PGEM‐Series, a global spatio‐temporal dataset of the last 5 Myr, with 1 kyr resolution, spatially downscaled from emulations performed with the intermediate‐complexity atmosphere–ocean general circulation model PALEO‐PGEM. PALEO‐PGEM‐Series holds the potential to advance our understanding of the mechanisms behind the strong relationship between biodiversity and climate, a pressing need given projected biodiversity responses to anthropogenic climatic change.Main Types of Variables ContainedSpatio‐temporal series of monthly temperature and precipitation and 17 derived bioclimatic variables over the Pliocene–Pleistocene, along with standard error estimates from multiple runs of the emulator.Spatial Location and GrainGlobal landmasses, at 1° × 1°.Time Period and GrainLast 5 Myr at 1000 year resolution.Major Taxa and Level of MeasurementNot applicable.Software FormatTab‐delimited text files and accompanying R code to derive bioclimatic variables.

Evaluating uncertainty in climate change impacts on peak discharge and flood volume in the Qaran Talar watershed, Iran

Abstract This study evaluates the effects of climate change (CC) on runoff properties over the case study of the Qaran Talar watershed located in Iran. To consider the two main sources of uncertainty, i.e., Greenhouse Gases emission (GHG) scenarios and outputs of Atmosphere-Ocean General Circulation Models (AOGCMs), a daily rainfall time series was generated for two future periods (2021-2050 and 2070-2099) at three risk levels of 0.1, 0.25 and 0.50. 22 AOGCMs outputs following two emission scenarios (RCP4.5 and RCP8.5) were used. The results showed that the uncertainty of climate change scenarios was primarily owing to the uncertainty of GCMs outputs. Regarding the 2021-2050 period, under both emission scenarios, the increases in peak discharge and flood volume (FV) were estimated to reach 70, 50, and 30% at three risk levels of 0.1, 0.25 and 0.50, respectively, compared to the recent past period. As the current century draws to a close, the difference between the results of the two emission scenarios becomes apparent so that in the far-future period (2070-2099), the RCP8.5 scenario estimates and FV more than the RCP4.5 scenario does. Additionally, it was found that the uncertainty caused by AOGCMs was more than that by GHG emission scenarios.

Bias Correction and Statistical Modeling of Variable Oceanic Forcing of Greenland Outlet Glaciers

AbstractVariability in oceanic conditions directly impacts ice loss from marine outlet glaciers in Greenland, influencing the ice sheet mass balance. Oceanic conditions are available from Atmosphere‐Ocean Global Climate Model (AOGCM) output, but these models require extensive computational resources and lack the fine resolution needed to simulate ocean dynamics on the Greenland continental shelf and close to glacier marine termini. Here, we develop a statistical approach to generate ocean forcing for ice sheet model simulations, which incorporates natural spatiotemporal variability and anthropogenic changes. Starting from raw AOGCM ocean heat content, we apply: (a) a bias‐correction using ocean reanalysis, (b) an extrapolation accounting for on‐shelf ocean dynamics, and (c) stochastic time series models to generate realizations of natural variability. The bias‐correction reduces model errors by ∼25% when compared to independent in‐situ measurements. The bias‐corrected time series are subsequently extrapolated to fjord mouth locations using relations constrained from available high‐resolution regional ocean model results. The stochastic time series models reproduce the spatial correlation, characteristic timescales, and the amplitude of natural variability of bias‐corrected AOGCMs, but at negligible computational expense. We demonstrate the efficiency of this method by generating >6,000 time series of ocean forcing for >200 Greenland marine‐terminating glacier locations until 2100. As our method is computationally efficient and adaptable to any ocean model output and reanalysis product, it provides flexibility in exploring sensitivity to ocean conditions in Greenland ice sheet model simulations. We provide the output and workflow in an open‐source repository, and discuss advantages and future developments for our method.

North Atlantic Oscillation impact on the Atlantic Meridional Overturning Circulation shaped by the mean state

Accurate representation of the Atlantic Meridional Overturning Circulation (AMOC) in global climate models is crucial for reliable future climate predictions and projections. In this study, we used 42 coupled atmosphere–ocean global climate models to analyze low-frequency variability of the AMOC driven by the North Atlantic Oscillation (NAO). Our results showed that the influence of the simulated NAO on the AMOC differs significantly between the models. We showed that the large intermodel diversity originates from the diverse oceanic mean state, especially over the subpolar North Atlantic (SPNA), where deep water formation of the AMOC occurs. For some models, the climatological sea ice extent covers a wide area of the SPNA and restrains efficient air–sea interactions, making the AMOC less sensitive to the NAO. In the models without the sea-ice-covered SPNA, the upper-ocean mean stratification critically affects the relationship between the NAO and AMOC by regulating the AMOC sensitivity to surface buoyancy forcing. Our results pinpoint the oceanic mean state as an aspect of climate model simulations that must be improved for an accurate understanding of the AMOC.

Setting sustainable limits on anchoring to improve the resilience of coral reefs

Boat anchoring is common at coral reefs that have high economic or social value, but anchoring has received relatively little attention in reef resilience studies. We developed an individual-based model of coral populations and simulated the effects of anchor damage over time. The model allowed us to estimate the carrying capacity of anchoring for four different coral assemblages and different starting levels of coral cover. The carrying capacity of small to medium-sized recreational vessels across these four assemblages was between 0 and 3.1 anchor strikes ha−1 day−1. In a case study of two Great Barrier Reef archipelagos, we modelled the benefits of anchoring mitigation under bleaching regimes expected for four climate scenarios. The partial mitigation of even a very mild anchoring incidence (1.17 strikes ha−1 day−1) resulted in median coral gains of 2.6–7.7 % absolute cover under RCP2.6, though benefits varied temporally and depended on the Atmosphere-Ocean General Circulation Model used.

The Role of Coupled North Pacific Atmosphere–Ocean Interactions in Impacts of Tibetan Plateau Snow Anomalies on the Northern Hemisphere Winter Atmospheric Circulation

Abstract Previous studies indicate observed influences of autumn and winter Tibetan Plateau (TP) snow-cover anomalies on the winter Pacific–North American (PNA) teleconnection. This study simulates atmospheric and oceanic responses to persistent autumn–winter TP snow forcing using an atmospheric general circulation model (AGCM) and a coupled atmospheric-oceanic general circulation model (AOGCM), and quantifies the role of atmosphere–ocean interactions over North Pacific in TP snow effects. The AOGCM experiment induces a stronger and more realistic remote PNA response to heavy TP snow anomalies, and also a significant winter horseshoe-like North Pacific sea surface temperature (SST) pattern resulting from an anomalous equivalent barotropic cyclone, or a strengthened Aleutian low, with associated cyclonic wind stress anomalies. The horseshoe-like SST anomaly pattern is used as boundary forcing (without prescribed heavy TP snow) in another AOGCM experiment, which simulates an enhanced winter Aleutian low and a PNA-like response similar to the original AOGCM responses, indicating that that the direct Pacific–North American atmospheric response to persistent TP snow forcing in the AGCM is amplified in the AOGCM by the North Pacific midlatitude atmosphere–ocean interactions. This suggests that the mechanisms of the winter PNA responses to TP snow forcing involve dynamical atmospheric processes such as horizontal propagation of Rossby wave energy and transient eddy feedbacks, and also North Pacific atmosphere–ocean interactions, which provide a positive feedback on the development of the remote PNA teleconnection. Significance Statement The Tibetan Plateau has a 2.5 million km2 area and a 4000 m average elevation, and is often called “the third pole” because of its polar-like climate in midlatitude Eurasia. A heavy Tibetan Plateau snow cover increases the albedo effect and causes tropospheric cold temperature and low height changes that can be carried or advected to the North Pacific by the prevailing westerlies. The purpose of this study is to better understand how Tibetan Plateau snow anomalies influence the North Pacific and the remote atmospheric circulation through numerical simulations. Our results find that those North Pacific responses amply the direct atmospheric response to Tibetan Plateau snow-cover anomalies. This greatly improves our understanding of physical mechanisms of Tibetan Plateau snow impacts.

Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)

Abstract. Earth system models (ESMs) integrate previously separate models of the ocean, atmosphere and vegetation into one comprehensive modelling system enabling the investigation of interactions between different components of the Earth system. Global isoprene and monoterpene emissions from terrestrial vegetation, which represent the most important source of volatile organic compounds (VOCs) in the Earth system, need to be included in global and regional chemical transport models given their major chemical impacts on the atmosphere. Due to the feedback of vegetation activity involving interactions with weather and climate, a coupled modelling system between vegetation and atmospheric chemistry is recommended to address the fate of biogenic volatile organic compounds (BVOCs). In this work, further development in linking LPJ-GUESS, a global dynamic vegetation model, to the atmospheric-chemistry-enabled atmosphere–ocean general circulation model EMAC is presented. New parameterisations are included to calculate the foliar density and leaf area density (LAD) distribution from LPJ-GUESS information. The new vegetation parameters are combined with existing LPJ-GUESS output (i.e. leaf area index and cover fractions) and used in empirically based BVOC modules in EMAC. Estimates of terrestrial BVOC emissions from EMAC's submodels ONEMIS and MEGAN are evaluated using (1) prescribed climatological vegetation boundary conditions at the land–atmosphere interface and (2) dynamic vegetation states calculated in LPJ-GUESS (replacing the “offline” vegetation inputs). LPJ-GUESS-driven global emission estimates for isoprene and monoterpenes from the submodel ONEMIS were 546 and 102 Tg yr−1, respectively. MEGAN determines 657 and 55 Tg of isoprene and monoterpene emissions annually. The new vegetation-sensitive BVOC fluxes in EMAC are in good agreement with emissions from the semi-process-based module in LPJ-GUESS. The new coupled system is used to evaluate the temperature and vegetation sensitivity of BVOC fluxes in doubling CO2 scenarios. This work provides evidence that the new coupled model yields suitable estimates for global BVOC emissions that are responsive to vegetation dynamics. It is concluded that the proposed model set-up is useful for studying land–biosphere–atmosphere interactions in the Earth system.

Investigating the performance of data mining, lumped, and distributed models in runoff projected under climate change

This work evaluates the effects of climate change on the surface water resources (river flow) of the Sanjabi basin, Iran, by comparing data-mining, lumped, and distributed models, namely artificial neural networks (ANN), the identification of unit hydrographs and component flows from rainfall, evaporation, and streamflow (IHACRES) model, and the soil and water assessment tool (SWAT). Climate projections in terms of monthly temperature and rainfall made by 17 atmosphere–ocean general circulation models (AOGCMs) by the 5th Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) under emission scenarios of Representative Concentration Pathways (RCPs) (RCP2.6, RCP4.5, and RCP8.5) during the baseline period 1971–2000 and future periods 2040–2069 and 2070–2099 are applied in the Sanjabi basin. The predictive skill of the AOGCMs is evaluated with performance criteria. The evaluation results indicate the CNRM-CM5 model features the best performance in terms of rainfall, average temperature, and minimum temperature projections, and the GFDL-CM3 provides the most accurate maximum temperature projections. Four downscaling methods (change factor (Delta), ClimGEN, LARS-WG, and Genetic Programming (GP)) are compared based on the R2, RMSE, MAE, and NSE. The predictive skill of the LARS-WG method was the highest. ANN, IHACRES, and SWAT are implemented to project future runoff following calibration and testing. The IHACRES model exhibits the best performance. The IHACRES model is applied to project future runoff under climate-change scenarios. The results indicate a reduction in runoff under all emission scenarios in the two future periods, with the RCP8.5 scenario featuring the largest reductions in runoff in 2040–2069 and 2070–2099 and being equal to 42.0 and 44.3%, respectively.

Processes Controlling the Southern Ocean Temperature Change

Abstract Observations show that since the 1950s, the subsurface Southern Ocean has been rapidly warming in the south and cooling in the north. However, what drives these opposing latitudinal patterns of change is not well understood. By analyzing the outputs of atmosphere–ocean general circulation models, we examine and quantify the processes controlling the temperature change in the Southern Ocean. We find that subsurface cooling in the north results from the combined effect of weak warming driven by greenhouse gases and cooling forced by aerosols. Contrary to previous work, we find that the influence of stratospheric ozone forcing is small and unnecessary to explain the observed patterns. Analysis of heat budget diagnostics shows that ocean heat content change between 400 and 1200 m in the Southern Ocean is primarily driven by slight imbalances between the large and opposing changes in residual mean advection and isopycnal diffusion. We show that while surface heat flux anomalies are the main contributor to the warming in the Southern Ocean, changes in wind stress are also required to produce the observed pattern of temperature change. These results highlight the importance of ocean dynamics in subsurface ocean heat content change. The counterbalancing effects of aerosols and greenhouse gases on ocean heat content change in the Southern Ocean will change with aerosols declining in the future, while our results suggest that changes in stratospheric ozone forcing will have only a minor effect on future patterns of Southern Ocean thermohaline structure.

Drivers of uncertainty in precipitation frequency under current and future climate – application to Maryland, USA

The intensity duration frequency (IDF) curves that inform engineering design must reflect the effects of changing climate on extreme precipitation events. These changes can be addressed, in part, by statistically assessing synthetic data/outputs from high-resolution climate model projections to develop IDF curves. The estimation of IDF curves is associated with multiple sources of uncertainty. Most studies that characterize uncertainty in IDF curves under climate change only address the uncertainty due to the choice and processing of climate model outputs, but neglect uncertainty from statistical modeling choices. This study assesses the uncertainty introduced by developing IDF curves for Maryland, USA from model simulations of current and future climate, using statistical methods that are used in U.S. practice. The study analyzes output time series from the North American Regional Climate Change Assessment Program (NARCCAP) suite, which consists of 6 Atmosphere-Ocean General Circulation Models, each spatially downscaled with two different Regional Climate Models. In a separate study, the 3-hour NARCCAP model output was temporally downscaled to 15 min using two different machine learning models. Uncertainty is assessed both across and within models. Across-model uncertainty arises from the differences among synthetic time-series for precipitation and other meteorological variables produced by the 12 NARCCAP climate model projections. Within-model uncertainty arises from the modeling choices used to develop statistical IDF curves from a single climate model time series. These choices include: temporal downscaling method, time-series type, distribution, and parameter estimation method. The choice of climate model (across-model uncertainty) is the dominant contributor under both current and future climate conditions. On the within-model level, the other sources of uncertainty contribute differently for different climate models. The development and application of future-climate IDF curves should acknowledge the uncertainty introduced by statistical modeling choices, as well as by variation among climate model projections.

The simulation of mineral dust in the United Kingdom Earth System Model UKESM1

Abstract. Mineral dust plays an important role in Earth system models and is linked to many components, including atmospheric wind speed, precipitation and radiation, surface vegetation cover and soil properties and oceanic biogeochemical systems. In this paper, the dust scheme in the first configuration of the United Kingdom Earth System Model UKESM1 is described, and simulations of dust and its radiative effects are presented and compared with results from the parallel coupled atmosphere–ocean general circulation model (GCM) HadGEM3-GC3.1. Not only changes in the driving model fields but also changes in the dust size distribution are shown to lead to considerable differences to the present-day dust simulations and to projected future changes. UKESM1 simulations produce a present-day, top-of-the-atmosphere (ToA) dust direct radiative effect (DRE – defined as the change in downward net flux directly due to the presence of dust) of 0.086 W m−2 from a dust load of 19.5 Tg. Under climate change pathways these values decrease considerably. In the 2081–2100 mean of the Shared Socioeconomic Pathway SSP5–8.45 ToA DRE reaches 0.048 W m−2 from a load of 15.1 Tg. In contrast, in HadGEM3-GC3.1 the present-day values of −0.296 W m−2 and 15.0 Tg are almost unchanged at −0.289 W m−2 and 14.5 Tg in the 2081–2100 mean. The primary mechanism causing the differences in future dust projections is shown to be the vegetation response, which dominates over the direct effects of warming in our models. Though there are considerable uncertainties associated with any such estimates, the results presented demonstrate both the importance of the size distribution for dust modelling and also the necessity of including Earth system processes such as interactive vegetation in dust simulations for climate change studies.

Simulations of human migration into North America are more sensitive to demography than choice of palaeoclimate model

Reconstructions of the spatiotemporal dynamics of human dispersal away from evolutionary origins in Africa are important for determining the ecological consequences of the arrival of anatomically modern humans in naïve landscapes and interpreting inferences from ancient genomes on indigenous population history. While efforts have been made to independently validate these projections against the archaeological record and contemporary measures of genetic diversity, there has been no comprehensive assessment of how parameter values and choice of palaeoclimate model affect projections of early human migration. We simulated human migration into North America with a process-explicit migration model using simulated palaeoclimate data from two different atmosphere-ocean general circulation models and did a sensitivity analysis on the outputs using a machine learning algorithm. We found that simulated human migration into North America was more sensitive to uncertainty in demographic parameters than choice of atmosphere-ocean general circulation model used for simulating climate-human interactions. Our findings indicate that the accuracy of process-explicit human migration models will be improved with further research on the population dynamics of ancient humans, and that uncertainties in model parameters must be considered in estimates of the timing and rate of human colonisation and their consequence on biodiversity.