Atmosphere-ocean General Circulation Model Research Articles

Tropical Pacific climate variability under solar geoengineering: impacts on ENSO extremes

Abstract. Many modelling studies suggest that the El Niño–Southern Oscillation (ENSO), in interaction with the tropical Pacific background climate, will change with rising atmospheric greenhouse gas concentrations. Solar geoengineering (reducing the solar flux from outer space) has been proposed as a means to counteract anthropogenic climate change. However, the effectiveness of solar geoengineering concerning a variety of aspects of Earth's climate is uncertain. Robust results are particularly challenging to obtain for ENSO because existing geoengineering simulations are too short (typically ∼ 50 years) to detect statistically significant changes in the highly variable tropical Pacific background climate. We here present results from a 1000-year-long solar-geoengineering simulation, G1, carried out with the coupled atmosphere–ocean general circulation model HadCM3L. In agreement with previous studies, reducing the solar irradiance (4 %) to offset global mean surface warming in the model more than compensates the warming in the tropical Pacific that develops in the 4 × CO2 scenario. We see an overcooling of 0.3 ∘C and a 0.23 mm d−1 (5 %) reduction in mean rainfall over the tropical Pacific relative to preindustrial conditions in the G1 simulation, owing to the different latitudinal distributions of the shortwave (solar) and longwave (CO2) forcings. The location of the Intertropical Convergence Zone (ITCZ) in the tropical Pacific, which moved 7.5∘ southwards under 4 × CO2, is restored to its preindustrial position. However, other aspects of the tropical Pacific mean climate are not reset as effectively. Relative to preindustrial conditions, in G1 the time-averaged zonal wind stress, zonal sea surface temperature (SST) gradient, and meridional SST gradient are each statistically significantly reduced by around 10 %, and the Pacific Walker Circulation (PWC) is consistently weakened, resulting in conditions conducive to increased frequency of El Niño events. The overall amplitude of ENSO strengthens by 9 %–10 % in G1, but there is a 65 % reduction in the asymmetry between cold and warm events: cold events intensify more than warm events. Notably, the frequency of extreme El Niño and La Niña events increases by ca. 60 % and 30 %, respectively, while the total number of El Niño events increases by around 10 %. All of these changes are statistically significant at either 95 or 99 % confidence level. Somewhat paradoxically, while the number of total and extreme events increases, the extreme El Niño events become weaker relative to the preindustrial state, while the extreme La Niña events become even stronger. That is, such extreme El Niño events in G1 become less intense than under preindustrial conditions but also more frequent. In contrast, extreme La Niña events become stronger in G1, which is in agreement with the general overcooling of the tropical Pacific in G1 relative to preindustrial conditions.

CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles

Despite numerous studies in statistical downscaling methodologies, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.

Roles of air–sea coupling and horizontal resolution in the climate model simulation of Indian monsoon low pressure systems

The roles of air–sea coupling and horizontal resolution in the representation of Indian monsoon low pressure systems (LPS) in Met Office Unified Model (MetUM) global climate simulations are investigated. To avoid the generally large sea surface temperature (SST) biases in standard coupled atmosphere–ocean global climate models (GCMs), the analysis is performed on experiments from an atmosphere model coupled to a mixed-layer ocean model (MetUM-GOML2), which allows coupling to be applied regionally as well as globally, while constraining the ocean mean state in coupled regions. Compared to the standard AMIP-style MetUM atmosphere-only simulations, the MetUM-GOML2 simulations produce more monsoon LPS, which is attributed to effects of relatively small remaining (Indian Ocean) SST biases that somewhat strengthen the atmospheric monsoon base state. However, the MetUM-GOML2 simulations, all starting from the same atmospheric and oceanic base state, allow for an idealised approach to evaluate the relative effects of coupling and resolution. When the effects of SST biases are excluded, global coupling has a neutral impact on the number of LPS formed, while the associated rainfall is somewhat reduced due to a local negative air–sea feedback reducing the strength of atmospheric convection and weakening individual LPS. The MetUM-GOML2 simulations show particular sensitivity to localised coupling in the Indian and Pacific Oceans, which appears to enhance the effect of monsoon LPS. Although, in contrast to the global coupling comparison, the comparison of regionally coupled simulations is affected by both differences in interannual SST variability and SST biases, and it is likely that this causes at least part of the positive effects from Indian and Pacific Ocean coupling. More importantly, however, is that the effects of air–sea coupling are substantially smaller than the positive effects of the increase in horizontal resolution from N96 (approx. 200 km) to N216 (approx. 90 km). The resolution effect is also larger than that seen in older MetUM configurations.

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Abstract. Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

What causes the spread of model projections of ocean dynamic sea-level change in response to greenhouse gas forcing?

Sea levels of different atmosphere–ocean general circulation models (AOGCMs) respond to climate change forcing in different ways, representing a crucial uncertainty in climate change research. We isolate the role of the ocean dynamics in setting the spatial pattern of dynamic sea-level (ζ) change by forcing several AOGCMs with prescribed identical heat, momentum (wind) and freshwater flux perturbations. This method produces a ζ projection spread comparable in magnitude to the spread that results from greenhouse gas forcing, indicating that the differences in ocean model formulation are the cause, rather than diversity in surface flux change. The heat flux change drives most of the global pattern of ζ change, while the momentum and water flux changes cause locally confined features. North Atlantic heat uptake causes large temperature and salinity driven density changes, altering local ocean transport and ζ. The spread between AOGCMs here is caused largely by differences in their regional transport adjustment, which redistributes heat that was already in the ocean prior to perturbation. The geographic details of the ζ change in the North Atlantic are diverse across models, but the underlying dynamic change is similar. In contrast, the heat absorbed by the Southern Ocean does not strongly alter the vertically coherent circulation. The Arctic ζ change is dissimilar across models, owing to differences in passive heat uptake and circulation change. Only the Arctic is strongly affected by nonlinear interactions between the three air-sea flux changes, and these are model specific.

Optimal Estimation of Stochastic Energy Balance Model Parameters

AbstractThis study has developed a rigorous and efficient maximum likelihood method for estimating the parameters in stochastic energy balance models (with any k > 0 number of boxes) given time series of surface temperature and top-of-the-atmosphere net downward radiative flux. The method works by finding a state-space representation of the linear dynamic system and evaluating the likelihood recursively via the Kalman filter. Confidence intervals for estimated parameters are straightforward to construct in the maximum likelihood framework, and information criteria may be used to choose an optimal number of boxes for parsimonious k-box emulation of atmosphere–ocean general circulation models (AOGCMs). In addition to estimating model parameters the method enables hidden state estimation for the unobservable boxes corresponding to the deep ocean, and also enables noise filtering for observations of surface temperature. The feasibility, reliability, and performance of the proposed method are demonstrated in a simulation study. To obtain a set of optimal k-box emulators, models are fitted to the 4 × CO2 step responses of 16 AOGCMs in CMIP5. It is found that for all 16 AOGCMs three boxes are required for optimal k-box emulation. The number of boxes k is found to influence, sometimes strongly, the impulse responses of the fitted models.

FGOALS-g3 Model Datasets for CMIP6 Flux-Anomaly-Forced Model Intercomparison Project

The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) is an endorsed Model Intercomparison Project in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The goal of FAFMIP is to investigate the spread in the atmosphere-ocean general circulation model projections of ocean climate change forced by increased CO2, including the uncertainties in the simulations of ocean heat uptake, global mean sea level rise due to ocean thermal expansion and dynamic sea level change due to ocean circulation and density changes. The FAFMIP experiments have already been conducted with the Flexible Global Ocean-Atmosphere-Land System Model, gridpoint version 3.0 (FGOALS-g3). The model datasets have been submitted to the Earth System Grid Federation (ESGF) node. Here, the details of the experiments, the output variables and some baseline results are presented. Compared with the preliminary results of other models, the evolutions of global mean variables can be reproduced well by FGOALS-g3. The simulations of spatial patterns are also consistent with those of other models in most regions except the North Atlantic and the Southern Ocean, indicating large uncertainties in the regional sea level projections of these two regions.

Temperature and precipitation responses to El Niño-Southern Oscillation in a hierarchy of datasets with different levels of observational constraints

El Nino-Southern Oscillation (ENSO) is the dominant mode of climate variability, affecting climate conditions over large areas of the globe. There are, however, substantial differences in how ENSO teleconnections with regional climate variability are represented in different datasets such as gridded observations and climate models. Here we examine the global responses of temperature and precipitation over land to ENSO within a hierarchy of datasets with different levels of observational constraints. Anomaly maps of observed El Nino and La Nina responses are compared to reanalysis, atmospheric models driven by observed sea surface temperature (SST), and coupled atmosphere–ocean general circulation models. There is a gradual decline in anomaly pattern agreement relative to observations moving down the dataset hierarchy to lower observational constraints. We find a positive relationship between the models’ fidelity in representing ENSO temperature and precipitation, though the relationship is stronger for El Nino teleconnections than La Nina. The models also reproduce El Nino response patterns with greater fidelity than La Nina patterns. Additionally, the fidelity of model-simulated responses to El Nino is related to the magnitude of SST variability in the tropical Pacific, but no such relationship could be found for La Nina responses. This comprehensive evaluation highlights the importance of realistically simulating atmospheric circulation and SST variability to better capturing the patterns of regional climate variability in response to ENSO in climate models.

Ocean‐Only FAFMIP: Understanding Regional Patterns of Ocean Heat Content and Dynamic Sea Level Change

AbstractThere is large uncertainty in the future regional sea level change under anthropogenic climate change. Our study presents and uses a novel design of ocean general circulation model (OGCM) experiments to investigate the ocean's response to surface buoyancy and momentum flux perturbations without atmosphere‐ocean feedbacks (e.g., without surface restoring or bulk formulae), as part of the Flux‐Anomaly‐Forced Model Intercomparison Project (FAFMIP). In an ensemble of OGCMs forced with identical surface flux perturbations, simulated dynamic sea level (DSL) and ocean heat content (OHC) change demonstrate considerable disagreement. In the North Atlantic, the disagreement in DSL and OHC change between models is mainly due to differences in the residual (resolved and eddy) circulation change, with a large spread in the Atlantic meridional overturning circulation (AMOC) weakening (20–50%). In the western North Pacific, OHC change is similar among the OGCM ensemble, but the contributing physical processes differ. For the Southern Ocean, isopycnal and diapycnal mixing change dominate the spread in OHC change. In addition, a component of the atmosphere‐ocean feedbacks are quantified by comparing coupled, atmosphere‐ocean GCM (AOGCM) and OGCM FAFMIP experiments with consistent ocean models. We find that there is 10% more AMOC weakening in AOGCMs relative to OGCMs, since the extratropical North Atlantic SST cooling due to heat redistribution amplifies the surface heat flux perturbation. This component of the atmosphere‐ocean feedbacks enhances the pattern of North Atlantic OHC and DSL change, with relatively stronger increases and decreases in the tropics and extratropics, respectively.

Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations

Abstract. This work evaluates the statistical predictability of the Arctic sea ice volume (SIV) anomaly – here defined as the detrended and deseasonalized SIV – on the interannual timescale. To do so, we made use of six datasets, from three different atmosphere–ocean general circulation models, with two different horizontal grid resolutions each. Based on these datasets, we have developed a statistical empirical model which in turn was used to test the performance of different predictor variables, as well as to identify optimal locations from where the SIV anomaly could be better reconstructed and/or predicted. We tested the hypothesis that an ideal sampling strategy characterized by only a few optimal sampling locations can provide in situ data for statistically reproducing and/or predicting the SIV interannual variability. The results showed that, apart from the SIV itself, the sea ice thickness is the best predictor variable, although total sea ice area, sea ice concentration, sea surface temperature, and sea ice drift can also contribute to improving the prediction skill. The prediction skill can be enhanced further by combining several predictors into the statistical model. Applying the statistical model with predictor data from four well-placed locations is sufficient for reconstructing about 70 % of the SIV anomaly variance. As suggested by the results, the four first best locations are placed at the transition Chukchi Sea–central Arctic–Beaufort Sea (79.5∘ N, 158.0∘ W), near the North Pole (88.5∘ N, 40.0∘ E), at the transition central Arctic–Laptev Sea (81.5∘ N, 107.0∘ E), and offshore the Canadian Archipelago (82.5∘ N, 109.0∘ W), in this respective order. Adding further to six well-placed locations, which explain about 80 % of the SIV anomaly variance, the statistical predictability does not substantially improve taking into account that 10 locations explain about 84 % of that variance. An improved model horizontal resolution allows a better trained statistical model so that the reconstructed values better approach the original SIV anomaly. On the other hand, if we inspect the interannual variability, the predictors provided by numerical models with lower horizontal resolution perform better when reconstructing the original SIV variability. We believe that this study provides recommendations for the ongoing and upcoming observational initiatives, in terms of an Arctic optimal observing design, for studying and predicting not only the SIV values but also its interannual variability.

Analysis of performance criteria and sustainability index in urban stormwater systems under the impacts of climate change

Urban stormwater systems are of important infrastructures in urban planning, and any mitigation in their design and implementation can be problematic. Changes in the hydrologic regime due to the climate change lead to exacerbation of extreme events such as flood. In this study, firstly, the impacts of climate change in the extreme precipitations of a part of Tehran in Iran were investigated during the period 2020–2049 using 10 Atmospheric-Ocean General Circulation Models (AOGCMs) under two Representative Concentration Pathway scenarios (i.e., RCP2.6 and RCP8.5). Then, Using the Storm Water Management Model (SWMM), the urban stormwater system of the study area was modelled during the base period (1981–2010) along with the moderate, pessimistic and optimistic conditions of the AOGCM models during the future period (2020–2049). Finally, the network performance was evaluated based on reliability, resiliency, and vulnerability criteria, along with the sustainability index. The simulation results revealed that the sustainability of the urban stormwater system is 77.89% during the base period while its value will be 77.73% and 77.61% under RCP2.6 and RCP8.5 scenarios, respectively, during the future period based on the moderate conditions of the AOGCM models. Furthermore, the network sustainability was 73.10% and 81.98%, respectively, according to the results of CanESM2 model for pessimistic and CESM1-WACCM model for optimistic conditions under RCP2.6 scenario. It was found that during the future period, under the influence of both studied scenarios, there will be no significant increase in the number of network flooding points compared to the baseline period. The approach presented in this study can be used to evaluate the performance of stormwater systems in other urban areas.

Estimating the Effect of Radiative Feedback Uncertainties on Climate Response to Changes in the Concentration of Stratospheric Aerosols

Using the two-box energy balance model (EBM), we explore the climate system response to radiative forcing generated by variations in the concentrations of stratospheric aerosols and estimate the effect of uncertainties in radiative feedbacks on changes in global mean surface temperature anomaly used as an indicator of the response of the climate system to external radiative perturbations. Radiative forcing generated by stratospheric sulfate aerosols from the second-largest volcanic eruption in the 20th century, the Mount Pinatubo eruption in June 1991, was chosen for this research. The global mean surface temperature response to a specified change in radiative forcing is estimated as a convolution of the derived impulse response function corresponding to EBM with a function that describes the temporal change in radiative forcing. The influence of radiative feedback uncertainties on changes in the global mean surface temperature is estimated using several “versions” of the EBM. The parameters for different “versions” were identified by applying a specific procedure for calibrating the two-box EBM parameters using the results of climate change simulations conducted with coupled atmosphere–ocean general circulation models from the Coupled Model Intercomparison Project phase 5 (CMIP5). Changes in the global mean surface temperature caused by stratospheric aerosol forcing are found to be highly sensitive not only to radiative feedbacks but also to climate system inertia defined by the effective heat capacity of the atmosphere–land–ocean mixed layer system, as well as to deep-ocean heat uptake. The results obtained have direct implications for a better understanding of how uncertainties in climate feedbacks, climate system inertia and deep-ocean heat uptake affect climate change modelling.

The Effect of Substellar Continent Size on Ocean Dynamics of Proxima Centauri b

Abstract The potential habitability of tidally locked planets orbiting M-dwarf stars has been widely investigated in recent work, typically with a nondynamic ocean and without continents. On Earth, ocean dynamics are a primary means of heat and nutrient distribution. Continents are a critical source of nutrients, strongly influence ocean dynamics, and participate in climate regulation. In this work, we investigate how the size of a substellar land mass affects the oceans ability to transport heat and upwell nutrients on the tidally locked planet Proxima Centauri b using the ROCKE-3D coupled ocean-atmosphere general circulation model (GCM). We find that dayside ice-free ocean and nutrient delivery to the mixed layer via upwelling are maintained across all continent sizes. We also find that Proxima Centauri b’s climate is more sensitive to differences among atmospheric GCMs than to the inclusion of ocean dynamics in ROCKE-3D. Finally, we find that Proxima Centauri b transitions from a “lobster” state where ocean heat transport distributes heat away from the substellar point to an “eyeball” state where heat transport is restricted and surface temperature decreases symmetrically from the substellar point when the continent size exceeds ∼20% of the surface area. Our work suggests that both a dynamic ocean and continents are unlikely to decrease the habitability prospects of nearby tidally locked targets like Proxima Centauri b that could be investigated with future observations by the James Webb Space Telescope.

Sea Ice—Ocean Interactions in the Barents Sea Modeled at Different Resolutions

The Barents Sea is one of the most rapidly changing Arctic regions in terms of sea ice. As it is almost ice-free in summer, most recent changes in the Barents Sea have occurred in winter, with a reduction of about 50% of its March sea-ice area between 1979 and 2018. This sea-ice loss is clearly linked to an increase in the Atlantic Ocean heat transport, especially through the Barents Sea Opening, in the western part of the Barents Sea. In this study, we investigate the links between the March Barents sea-ice area and ocean heat transport at the Barents Sea Opening using seven different coupled atmosphere-ocean general circulation models, with at least two different horizontal resolutions for each model. These models follow the High Resolution Model Intercomparison Project protocol, and we focus on the historical record (1950-2014). We find that all models capture the anticorrelation between March sea-ice area and annual mean ocean heat transport in the Barents Sea. Furthermore, the use of an increased ocean resolution allows to better resolve the different ocean pathways into the Barents Sea and the Atlantic Water heat transport at the Barents Sea Opening (reduced transect). A higher ocean resolution also improves the strong water cooling at the sea-ice edge and further formation of warm intermediate Atlantic Water. However, the impact of a higher ocean resolution on the mean March Barents sea-ice area and ocean heat transport at the Barents Sea Opening (large transect) varies among models. A potential reason for a different effect of model resolution on ocean heat transport when considering a reduced or a large transect is that the Atlantic Water and Norwegian Coastal Current inflows are under-represented at lower ocean resolution. Finally, we do not find a systematic effect of resolution on the strength of the sea-ice area - ocean heat transport relationship.

Oceanographic Considerations for Exoplanet Life Detection

Abstract Liquid water oceans are at the center of our search for life on exoplanets because water is a strict requirement for life as we know it. However, oceans are dynamic habitats—and some oceans may be better hosts for life than others. In Earth’s ocean, circulation transports essential nutrients such as phosphate and is a first-order control on the distribution and productivity of life. Of particular importance is upward flow from the dark depths of the ocean in response to wind-driven divergence in surface layers. This “upwelling” returns essential nutrients that tend to accumulate at depth via sinking of organic particulates back to the sunlit regions where photosynthetic life thrives. Ocean dynamics are likely to impose constraints on the activity and atmospheric expression of photosynthetic life in exo-oceans as well, but we lack an understanding of how ocean dynamics may differ on other planets. We address this issue by exploring the sensitivity of ocean dynamics to a suite of planetary parameters using ROCKE-3D, a fully coupled ocean–atmosphere general circulation model. Our results suggest that planets that rotate slower and have higher surface pressure than Earth may be the most attractive targets for remote life detection because upwelling is enhanced under these conditions, resulting in greater nutrient supply to the surface biosphere. Seasonal deepening of the mixed layer on high-obliquity planets may also enhance nutrient replenishment from depth into the surface mixed layer. Efficient nutrient recycling favors greater biological activity, more biosignature production, and thus more detectable life. More generally, our results demonstrate the importance of considering oceanographic phenomena for exoplanet life detection and motivate future interdisciplinary contributions to the emerging field of exo-oceanography.

Spatial Radiative Feedbacks from Internal Variability Using Multiple Regression

AbstractThe sensitivity of the climate to CO2 forcing depends on spatially varying radiative feedbacks that act both locally and nonlocally. We assess whether a method employing multiple regression can be used to estimate local and nonlocal radiative feedbacks from internal variability. We test this method on millennial-length simulations performed with six coupled atmosphere–ocean general circulation models (AOGCMs). Given the spatial pattern of warming, the method does quite well at recreating the top-of-atmosphere flux response for most regions of Earth, except over the Southern Ocean where it consistently overestimates the change, leading to an overestimate of the sensitivity. For five of the six models, the method finds that local feedbacks are positive due to cloud processes, balanced by negative nonlocal shortwave cloud feedbacks associated with regions of tropical convection. For four of these models, the magnitudes of both are comparable to the Planck feedback, so that changes in the ratio between them could lead to large changes in climate sensitivity. The positive local feedback explains why observational studies that estimate spatial feedbacks using only local regressions predict an unstable climate. The method implies that sensitivity in these AOGCMs increases over time due to a reduction in the share of warming occurring in tropical convecting regions and the resulting weakening of associated shortwave cloud and longwave clear-sky feedbacks. Our results provide a step toward an observational estimate of time-varying climate sensitivity by demonstrating that many aspects of spatial feedbacks appear to be the same between internal variability and the forced response.

Combining the Monthly Drought Code and Paleoecological Data to Assess Holocene Climate Impact on Mediterranean Fire Regime

Currently, indexes from the Fire Weather Index System (FWI) are used to predict the daily fire hazard, but there is no reliable index available in the Mediterranean region to be compared with paleofire records and check for their long-term reliability. In order to assess the past fire hazard and the fire-season length, based on data availability and requirements for fire index computation, we first chose and tested the efficiency of the Drought Code (DC) in Corsica (the main French Mediterranean fire-prone region) over the current period (1979–2016). We then used DC as a benchmark to assess the efficiency of the Monthly Drought Code (MDC) and used it to assess the Fire-Season Length (FSL), which were both used to characterize the fire hazard. Finally, we computed the Holocene MDC and FSL based on the HadCM3B-M1 climate model (three dimensional, fully dynamic, coupled atmosphere-ocean global climate model without flux adjustment) datasets and compared both index trends with those from proxies of paleofire, vegetation, and land use retrieved from sedimentary records in three Corsican lakes (Bastani, Nino, and Creno). Our strategy was to (i) assess fire hazard without the constraint of the daily weather-data requirement, (ii) reconstruct Holocene fire hazard from a climate perspective, and (iii) discuss the role of climate and human fire drivers based on the MDC-Paleofire proxy comparisons. Using both the Prométhée fire database and the ERA-Interim climate database over Corsica for the current period, we showed that DC values higher than 405 units efficiently discriminated fire-days from no-fire-days. The equivalent threshold value from MDC was set at 300 units. MDC and FSL indexes calculated for each of the past 11 millennia Before Present (11 ka BP) showed high values before 7 ka BP (above 300 units for MDC) and then lower values for the mid- to late Holocene (below 300 units for MDC). Climate appeared as a key driver to predict fire occurrences, promoting fires between 11 and 8 ka BP when summers were warmer than the current ones and reducing fire hazard after 7–6 ka BP due to wetter conditions. Since 5 ka BP, humans have taken control of the fire regime through agro-pastoralism, favoring large and/or frequent events despite less fire-prone climate conditions. The current fire hazard and fire-season length computed over the last few decades (1979–2016) both reported values that were respectively higher and longer than those assessed for the previous six millennia at least and comparable for those before 7 ka BP. For the next decades, due to climate warming associated with land abandonment (fuel accumulation) and the increase in human-related sources of ignition, we can expect an increase in fire hazard and larger fire events.

Including vegetation dynamics in an atmospheric chemistry-enabled general circulation model: linking LPJ-GUESS (v4.0) with the EMAC modelling system (v2.53)

Abstract. Central to the development of Earth system models (ESMs) has been the coupling of previously separate model types, such as ocean, atmospheric, and vegetation models, to address interactive feedbacks between the system components. A modelling framework which combines a detailed representation of these components, including vegetation and other land surface processes, enables the study of land–atmosphere feedbacks under global climate change. Here we present the initial steps of coupling LPJ-GUESS, a dynamic global vegetation model, to the atmospheric chemistry-enabled atmosphere–ocean general circulation model EMAC. The LPJ-GUESS framework is based on ecophysiological processes, such as photosynthesis; plant and soil respiration; and ecosystem carbon, nitrogen, and water cycling, and it includes a comparatively detailed individual-based representation of resource competition, plant growth, and vegetation dynamics as well as fire disturbance. Although not enabled here, the model framework also includes a crop and managed-land scheme, a representation of arctic methane and permafrost, and a choice of fire models; and hence it represents many important terrestrial biosphere processes and provides a wide range of prognostic trace-gas emissions from vegetation, soil, and fire. We evaluated an online one-way-coupled model configuration (with climate variable being passed from EMAC to LPJ-GUESS but no return information flow) by conducting simulations at three spatial resolutions (T42, T63, and T85). These were compared to an expert-derived map of potential natural vegetation and four global gridded data products: tree cover, biomass, canopy height, and gross primary productivity (GPP). We also applied a post hoc land use correction to account for human land use. The simulations give a good description of the global potential natural vegetation distribution, although there are some regional discrepancies. In particular, at the lower spatial resolutions, a combination of low-temperature and low-radiation biases in the growing season of the EMAC climate at high latitudes causes an underestimation of vegetation extent. Quantification of the agreement with the gridded datasets using the normalised mean error (NME) averaged over all datasets shows that increasing the spatial resolution from T42 to T63 improved the agreement by 10 %, and going from T63 to T85 improved the agreement by a further 4 %. The highest-resolution simulation gave NME scores of 0.63, 0.66, 0.84, and 0.53 for tree cover, biomass, canopy height, and GPP, respectively (after correcting tree cover and biomass for human-caused deforestation which was not present in the simulations). These scores are just 4 % worse on average than an offline LPJ-GUESS simulation using observed climate data and corrected for deforestation by the same method. However, it should be noted that the offline LPJ-GUESS simulation used a higher spatial resolution, which makes the evaluation more rigorous, and that excluding GPP from the datasets (which was anomalously better in the EMAC simulations) gave 10 % worse agreement for the EMAC simulation than the offline simulation. Gross primary productivity was best simulated by the coupled simulations, and canopy height was the worst. Based on this first evaluation, we conclude that the coupled model provides a suitable means to simulate dynamic vegetation processes in EMAC.

Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution

Abstract. Changes in ocean temperature and salinity are expected to be an important determinant of the Greenland ice sheet's future sea level contribution. Yet, simulating the impact of these changes in continental-scale ice sheet models remains challenging due to the small scale of key physics, such as fjord circulation and plume dynamics, and poor understanding of critical processes, such as calving and submarine melting. Here we present the ocean forcing strategy for Greenland ice sheet models taking part in the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6), the primary community effort to provide 21st century sea level projections for the Intergovernmental Panel on Climate Change Sixth Assessment Report. Beginning from global atmosphere–ocean general circulation models, we describe two complementary approaches to provide ocean boundary conditions for Greenland ice sheet models, termed the “retreat” and “submarine melt” implementations. The retreat implementation parameterises glacier retreat as a function of projected subglacial discharge and ocean thermal forcing, is designed to be implementable by all ice sheet models and results in retreat of around 1 and 15 km by 2100 in RCP2.6 and 8.5 scenarios, respectively. The submarine melt implementation provides estimated submarine melting only, leaving the ice sheet model to solve for the resulting calving and glacier retreat and suggests submarine melt rates will change little under RCP2.6 but will approximately triple by 2100 under RCP8.5. Both implementations have necessarily made use of simplifying assumptions and poorly constrained parameterisations and, as such, further research on submarine melting, calving and fjord–shelf exchange should remain a priority. Nevertheless, the presented framework will allow an ensemble of Greenland ice sheet models to be systematically and consistently forced by the ocean for the first time and should result in a significant improvement in projections of the Greenland ice sheet's contribution to future sea level change.

Contribution of mean climate to hot temperature extremes for present and future climates

The occurrence of very high temperatures (hot extremes) is often linked with negative impacts in human health, natural ecosystems and the economy (e.g., energy, water supply and agriculture). Studies have invariably shown that the intensity and frequency of hot extremes will increase in the future thus increasing their associated risks. While much progress has been made in quantifying and understanding hot temperature extremes and their future changes, there are still open questions. This paper focusses on the sources of hot extremes and their changes by applying a simple and unambiguous methodology that describes daily hot extremes as the superposition of four well known physical terms that include information on the annual mean temperature, the amplitude of the annual cycle, the diurnal temperature range and the local temperature anomaly on the day of the extreme. The methodology was applied to 30-year daily temperature records from 6 observation-based datasets and 31 atmosphere-ocean global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The comparison between observed and simulated hot extremes shows a remarkably consistent picture where most CMIP5 models overestimate the term describing the local temperature extreme anomaly over most regions of the globe regardless of the observed dataset considered. Simultaneously, CMIP5 models show a systematic cold bias in the annual mean temperature and in the diurnal temperature range terms leading to substantial error compensation over some regions. This prompted us to define a new error estimator as the sum of errors in individual terms that appears to be much more effective at characterising model's performance compared to the traditional bias estimator. The assessment of future changes in hot extremes shows that changes are dominated by changes in annual mean temperatures with varying contributions from the other terms that strongly depend on the specific region being considered. Western Europe appears as a hot spot for extreme temperature changes (increases of ~8 ∘C by the end of the century) due to significant contributions from all decomposition terms including the summer mean anomaly, the diurnal temperature range and the daily extreme anomaly. Tropical South America also appears as a hot spot for extreme temperature changes (increases of ~7 ∘C) largely due to an increase in the daily extreme anomaly term (explaining about 30% of the full change) making this region one of the most sensitive regions in the world in terms of hot extremes. The analysis reveals that the separation of future changes according to terms describing mean, variability and tails is very sensitive to the specific way the mean component is defined including assumptions about stationarity.