Coupled Earth System Model Research Articles

Global warming projections using the human–earth system model BNU-HESM1.0

Future climate change is usually projected by coupled earth system models under specific emission scenarios designed by integrated assessment models (IAMs), and this offline approach means there is no interaction between the coupled earth system models and the IAMs. This paper introduces a new method to design possible future emission scenarios and corresponding climate change, in which a simple economic and climate damage component is added to the coupled earth system model of Beijing Normal University (BNU-ESM). With the growth of population and technological expertise and the declining emission-to-output ratio described in the Dynamic Integrated Climate-Economy model, the projected carbon emission is 13.7Gt C, resulting in a 2.4 °C warming by the end of the twenty-first century (2080–2099) compared with 1980–1999. This paper also suggests the importance of the land and ocean carbon cycle in determining the CO2 concentration in the atmosphere. It is hoped that in the near future the next generation of coupled earth system models that include both the natural system and the social dimension will be developed.

Impact of tropical Atlantic sea-surface temperature biases on the simulated atmospheric circulation and precipitation over the Atlantic region: An ECHAM6 model study

As many coupled atmosphere-ocean general circulation models, the coupled Earth System Model developed at the Max Planck Institute for Meteorology suffers from severe sea-surface temperature (SST) biases in the tropical Atlantic. We performed a set of SST sensitivity experiments with its atmospheric model component ECHAM6 to understand the impact of tropical Atlantic SST biases on atmospheric circulation and precipitation. The model was forced by a climatology of observed global SSTs to focus on simulated seasonal and annual mean state climate. Through the superposition of varying tropical Atlantic bias patterns extracted from the MPI-ESM on top of the control field, this study investigates the relevance of the seasonal variation and spatial structure of tropical Atlantic biases for the simulated response. Results show that the position and structure of the Intertropical Convergence Zone (ITCZ) across the Atlantic is significantly affected, exhibiting a dynamically forced shift of annual mean precipitation maximum to the east of the Atlantic basin as well as a southward shift of the oceanic rain belt. The SST-induced changes in the ITCZ in turn affect seasonal rainfall over adjacent continents. However not only the ITCZ position but also other effects arising from biases in tropical Atlantic SSTs, e.g. variations in the wind field, change the simulation of precipitation over land. The seasonal variation and spatial pattern of tropical Atlantic SST biases turns out to be crucial for the simulated atmospheric response and is essential for analyzing the contribution of SST biases to coupled model mean state biases. Our experiments show that MPI-ESM mean-state biases in the Atlantic sector are mainly driven by SST biases in the tropical Atlantic while teleconnections from other basins seem to play a minor role.

Attribution analyses of temperature extremes using a set of 16 indices

Abstract Detection and attribution studies have demonstrated that anthropogenic forcings have been driving significant changes in temperature extremes since the middle of the 20th century. Moreover, new methodologies have been developed for the attribution of extreme events that assess how human influence may have changed their characteristics. Here we combine formal statistical analyses based on optimal fingerprinting to attribute observed long term changes in temperature extremes with an ensemble-based approach for event attribution. Our analyses are applied to 16 indices constructed with daily temperature data that focus on different characteristics of extremes and together build up a more complete representation of historical changes in warm and cold extremes than previous studies. For each index we compute an annual value for all years of the post-1960 period using data from observations and experiments with a coupled Earth System model for the analysis of multi-decadal changes and a high-resolution atmospheric model for event attribution. The models indicate that anthropogenic forcings have influenced almost all indices in recent decades and led to more prominent changes in the frequency of extremes. The optimal fingerprinting analyses show that for most indices the anthropogenic signal is detectable in changes during 1961–2010 both in Europe and on a quasi-global scale. The weaker natural effect, resulting mainly from volcanic eruptions, is in most cases not detectable, with the exception of large scale changes in indices linked to the frequency of cold night-time extremes. Our event analyses estimate how anthropogenic forcings alter the chances of getting new record index values in Europe and find that such extremes would be markedly rare if human influence were not accounted for, whereas in the current climate their return times range from a few years to a few decades.

Human-induced greening of the northern extratropical land surface

Observed northern extratropical land greening is consistent with anthropogenic forcings, where greenhouse gases play a dominant role, but not with simulations that include only natural forcings and internal climate variability. Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades1,2,3,4,5. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales6,7,8. Discernible human impacts on the Earth’s climate system have been revealed by using statistical frameworks of detection–attribution9,10,11. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets12,13, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm14,15. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts11.

Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions

Abstract. Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.

Alleviating the bias induced by the linear analysis update with an isopycnal ocean model

This work is based on the Norwegian Climate Prediction Model (NorCPM), which applies the ensemble Kalman filter (EnKF) to a fully coupled Earth System Model (NorESM) with an isopycnic ocean model (MICOM) for climate predictions. An idealized assimilation framework is first developed to identify the origin of assimilation‐created bias in the current version of NorCPM. It is found that the bias in ocean heat and salt contents is introduced by the non‐negativity constraint of isopycnic layer thickness values. Secondly, a new and computationally efficient method (referred to as upscaling) is proposed and tested in the idealized framework. In the upscaling method, layers for which analysis yields negative values are grouped iteratively with neighbouring layers, resulting in a probability density function with a larger mean and smaller standard deviation that prevents the appearance of negative layer thickness values. Analysis increments of the grouped layer are then distributed proportionally, which also prevents empty layers from becoming filled and vice versa. The upscaling method acts as a moderation in the location where the non‐negativity constraint is not satisfied and, as such, is suboptimal. The upscaling method shows a reduction in heat and salt contents and sea‐surface height bias by a factor of 10. A small bias remains, due to the update of ensemble anomalies, but the upscaling method would be unbiased for the heat and salt contents with data assimilation methods that utilize a static forecast‐error covariance matrix (e.g. EnOI). Finally, the upscaling method is demonstrated in a realistic framework with NorCPM by assimilating sea‐surface temperature observations. Over a 25 year analysis period, the new method does not impair the predictive skill of the system but corrects the assimilation‐created bias in steric sea‐level rise and provides an estimation in better agreement with the Intergovernmental Panel on Climate Change.

Quantifying differences in land use emission estimates implied by definition discrepancies

Abstract. The quantification of CO2 emissions from anthropogenic land use and land use change (eLUC) is essential to understand the drivers of the atmospheric CO2 increase and to inform climate change mitigation policy. Reported values in synthesis reports are commonly derived from different approaches (observation-driven bookkeeping and process-modelling) but recent work has emphasized that inconsistencies between methods may imply substantial differences in eLUC estimates. However, a consistent quantification is lacking and no concise modelling protocol for the separation of primary and secondary components of eLUC has been established. Here, we review differences of eLUC quantification methods and apply an Earth System Model (ESM) of Intermediate Complexity to quantify them. We find that the magnitude of effects due to merely conceptual differences between ESM and offline vegetation model-based quantifications is ~ 20 % for today. Under a future business-as-usual scenario, differences tend to increase further due to slowing land conversion rates and an increasing impact of altered environmental conditions on land-atmosphere fluxes. We establish how coupled Earth System Models may be applied to separate secondary component fluxes of eLUC arising from the replacement of potential C sinks/sources and the land use feedback and show that secondary fluxes derived from offline vegetation models are conceptually and quantitatively not identical to either, nor their sum. Therefore, we argue that synthesis studies should resort to the "least common denominator" of different methods, following the bookkeeping approach where only primary land use emissions are quantified under the assumption of constant environmental boundary conditions.

Climate response to Amazon forest replacement by heterogeneous crop cover

Abstract. Previous modeling studies with atmospheric general circulation models and basic land surface schemes to balance energy and water budgets have shown that by removing the natural vegetation over the Amazon, the region's climate becomes warmer and drier. In this study we use a fully coupled Earth system model and replace tropical forests by a distribution of six common tropical crops with variable planting dates, physiological parameters and irrigation. There is still general agreement with previous studies as areal averages show a warmer (+1.4 K) and drier (−0.35 mm day−1) climate. Using an interactive crop model with a realistic crop distribution shows that regions of vegetation change experience different responses dependent upon the initial tree coverage and whether the replacement vegetation is irrigated, with seasonal changes synchronized to the cropping season. Areas with initial tree coverage greater than 80 % show an increase in coupling with the atmosphere after deforestation, suggesting land use change could heighten sensitivity to climate anomalies, while irrigation acts to dampen coupling with the atmosphere.

Assimilation of oceanic observations in a global coupled Earth system model with the SEIK filter

We present results from the assimilation of observed oceanic 3-D temperature and salinity fields into the global coupled Max Planck Institute Earth system model with the SEIK filter from January 1996 to December 2010. Our study is part of an effort to perform and evaluate assimilation and prediction within the same coupled climate model without the use of re-analysis data. We use two assimilation setups, one where oceanic observations over the entire water column are assimilated, and one where only oceanic observations below 50 m depth are assimilated. We compare the results from both assimilations with an unconstrained control experiment. While we do not find significant improvements due to assimilation in terms of the root-mean-square error of simulated temperature, 0–700 m heat content, sea surface height (SSH), and the Atlantic meridional overturning circulation (AMOC) against observations, we find the variability in terms of correlation with observations significantly improved due to assimilation, most prominently in the tropical oceans. Improvements over the control experiment are stronger in the sub-50 m assimilation experiment and in integrated quantities (SSH, AMOC).

The impact of geoengineering on vegetation in experiment G1 of the GeoMIP

AbstractSolar Radiation Management (SRM) has been proposed as a mean to partly counteract global warming. The Geoengineering Model Intercomparison Project (GeoMIP) has simulated the climate consequences of a number of SRM techniques. Thus far, the effects on vegetation have not yet been thoroughly analyzed. Here the vegetation response to the idealized GeoMIP G1 experiment from eight fully coupled Earth system models (ESMs) is analyzed, in which a reduction of the solar constant counterbalances the radiative effects of quadrupled atmospheric CO2 concentrations (abrupt4 × CO2). For most models and regions, changes in net primary productivity (NPP) are dominated by the increase in CO2, via the CO2 fertilization effect. As SRM will reduce temperatures relative to abrupt4 × CO2, in high latitudes this will offset increases in NPP. In low latitudes, this cooling relative to the abrupt4 × CO2 simulation decreases plant respiration while having little effect on gross primary productivity, thus increasing NPP. In Central America and the Mediterranean, generally dry regions which are expected to experience increased water stress with global warming, NPP is highest in the G1 experiment for all models due to the easing of water limitations from increased water use efficiency at high‐CO2 concentrations and the reduced evaporative demand in a geoengineered climate. The largest differences in the vegetation response are between models with and without a nitrogen cycle, with a much smaller CO2 fertilization effect for the former. These results suggest that until key vegetation processes are integrated into ESM predictions, the vegetation response to SRM will remain highly uncertain.

Quantifying the biological impact of surface ocean light attenuation by colored detrital matter in an ESM using a new optical parameterization

Abstract. Light attenuation by colored detrital material (CDM) was included in a fully coupled Earth system model (ESM). This study presents a modified parameterization for shortwave attenuation, which is an empirical relationship between 244 concurrent measurements of the diffuse attenuation coefficient for downwelling irradiance, chlorophyll concentration and light absorption by CDM. Two ESM model runs using this parameterization were conducted, with and without light absorption by CDM. The light absorption coefficient for CDM was prescribed as the average of annual composite MODIS Aqua satellite data from 2002 to 2013. Comparing results from the two model runs shows that changes in light limitation associated with the inclusion of CDM decoupled trends between surface biomass and nutrients. Increases in surface biomass were expected to accompany greater nutrient uptake and therefore diminish surface nutrients. Instead, surface chlorophyll, biomass and nutrients increased together. These changes can be attributed to the different impact of light limitation on surface productivity versus total productivity. Chlorophyll and biomass increased near the surface but decreased at greater depths when CDM was included. The net effect over the euphotic zone was less total biomass leading to higher nutrient concentrations. Similar results were found in a regional analysis of the oceans by biome, investigating the spatial variability of response to changes in light limitation using a single parameterization for the surface ocean. In coastal regions, surface chlorophyll increased by 35 % while total integrated phytoplankton biomass diminished by 18 %. The largest relative increases in modeled surface chlorophyll and biomass in the open ocean were found in the equatorial biomes, while the largest decreases in depth-integrated biomass and chlorophyll were found in the subpolar and polar biomes. This mismatch of surface and subsurface trends and their regional dependence was analyzed by comparing the competing factors of diminished light availability and increased nutrient availability on phytoplankton growth in the upper 200 m. Understanding changes in biological productivity requires both surface and depth-resolved information. Surface trends may be minimal or of the opposite sign than depth-integrated amounts, depending on the vertical structure of phytoplankton abundance.

Major modes of short-term climate variability in the newly developed NUIST Earth System Model (NESM)

Abstract A coupled earth system model (ESM) has been developed at the Nanjing University of Information Science and Technology (NUIST) by using version 5.3 of the European Centre Hamburg Model (ECHAM), version 3.4 of the Nucleus for European Modelling of the Ocean (NEMO), and version 4.1 of the Los Alamos sea ice model (CICE). The model is referred to as NUIST ESM1 (NESM1). Comprehensive and quantitative metrics are used to assess the model’s major modes of climate variability most relevant to subseasonal-to-interannual climate prediction. The model’s assessment is placed in a multi-model framework. The model yields a realistic annual mean and annual cycle of equatorial SST, and a reasonably realistic precipitation climatology, but has difficulty in capturing the spring-fall asymmetry and monsoon precipitation domains. The ENSO mode is reproduced well with respect to its spatial structure, power spectrum, phase locking to the annual cycle, and spatial structures of the central Pacific (CP)-ENSO and eastern Pacific (EP)-ENSO; however, the equatorial SST variability, biennial component of ENSO, and the amplitude of CP-ENSO are overestimated. The model captures realistic intraseasonal variability patterns, the vertical-zonal structures of the first two leading predictable modes of Madden-Julian Oscillation (MJO), and its eastward propagation; but the simulated MJO speed is significantly slower than observed. Compared with the T42 version, the high resolution version (T159) demonstrates improved simulation with respect to the climatology, interannual variance, monsoon-ENSO lead-lag correlation, spatial structures of the leading mode of the Asian-Australian monsoon rainfall variability, and the eastward propagation of the MJO.

Partially coupled spin-up of the MPI-ESM: implementation and first results

Abstract. Large-scale fully coupled Earth system models (ESMs) are usually applied in climate projections like the IPCC (Intergovernmental Panel on Climate Change) reports. In these models internal variability is often within the correct order of magnitude compared with the observed climate, but due to internal variability and arbitrary initial conditions they are not able to reproduce the observed timing of climate events or shifts as for instance observed in the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), or the Atlantic Meridional Overturning Circulation (AMOC). Additional information about the real climate history is necessary to constrain ESMs; not only to emulate the past climate, but also to introduce a potential forecast skill into these models through a proper initialisation. We attempt to do this by extending the fully coupled climate model Max Planck Institute Earth System Model (MPI-ESM) using a partial coupling technique (Modini-MPI-ESM). This method is implemented by adding reanalysis wind-field anomalies to the MPI-ESM's inherent climatological wind field when computing the surface wind stress that is used to drive the ocean and sea ice model. Using anomalies instead of the full wind field reduces potential model drifts, because of different mean climate states of the unconstrained MPI-ESM and the partially coupled Modini-MPI-ESM, that could arise if total observed wind stress was used. We apply two different reanalysis wind products (National Centers for Environmental Prediction, Climate Forecast System Reanalysis (NCEPcsfr) and ERA-Interim reanalysis (ERAI)) and analyse the skill of Modini-MPI-ESM with respect to several observed oceanic, atmospheric, and sea ice indices. We demonstrate that Modini-MPI-ESM has a significant skill over the time period 1980–2013 in reproducing historical climate fluctuations, indicating the potential of the method for initialising seasonal to decadal forecasts. Additionally, our comparison of the results achieved with the two reanalysis wind products NCEPcsfr and ERAI indicates that in general applying NCEPcsfr results in a better reconstruction of climate variability since 1980.

Assessment of Ecosystem Photosynthetic Parameters along Two California Climate Gradients

Improving coupled Earth system models of current and future climate requires robust observations that accurately provide parameters and observations for evaluation across spatial scales relevant for the model. Photosynthetic parameters Vcmax and Jmax help to characterize the ability of vegetation to assimilate carbon, a required parameter in most land surface modules of climate models. Remote sensing, flux tower data, and field measurements were collected to develop a methodology to estimate the variability in these parameters across diverse landscapes in Southern California and the Sierras, regions experiencing prolonged drought which is expected to become more common in the future. Vcmax maps were generated with NASA hyperspectral airborne AVIRIS imagery by scaling up leaf level measurements to the canopy and evaluated using flux tower data for nine sites across California. These maps illustrate the expected temporal and spatial changes in the parameter. However, Vcmax estimated from inverse modeling of flux tower data did not fall in the range found in field measurements. The methods developed in this study expand the applicability of imaging spectroscopy in estimating ecosystem metabolism.

A simplified permafrost-carbon model for long-term climate studies with the CLIMBER-2 coupled earth system model

Abstract. We present the development and validation of a simplified permafrost-carbon mechanism for use with the land surface scheme operating in the CLIMBER-2 earth system model. The simplified model estimates the permafrost fraction of each grid cell according to the balance between modelled cold (below 0 °C) and warm (above 0 °C) days in a year. Areas diagnosed as permafrost are assigned a reduction in soil decomposition rate, thus creating a slow accumulating soil carbon pool. In warming climates, permafrost extent reduces and soil decomposition rates increase, resulting in soil carbon release to the atmosphere. Four accumulation/decomposition rate settings are retained for experiments within the CLIMBER-2(P) model, which are tuned to agree with estimates of total land carbon stocks today and at the last glacial maximum. The distribution of this permafrost-carbon pool is in broad agreement with measurement data for soil carbon content. The level of complexity of the permafrost-carbon model is comparable to other components in the CLIMBER-2 earth system model.

Projected pH reductions by 2100 might put deep North Atlantic biodiversity at risk

Abstract. This study aims to evaluate the potential for impacts of ocean acidification on North Atlantic deep-sea ecosystems in response to IPCC AR5 Representative Concentration Pathways (RCPs). Deep-sea biota is likely highly vulnerable to changes in seawater chemistry and sensitive to moderate excursions in pH. Here we show, from seven fully coupled Earth system models, that for three out of four RCPs over 17% of the seafloor area below 500 m depth in the North Atlantic sector will experience pH reductions exceeding −0.2 units by 2100. Increased stratification in response to climate change partially alleviates the impact of ocean acidification on deep benthic environments. We report on major pH reductions over the deep North Atlantic seafloor (depth >500 m) and at important deep-sea features, such as seamounts and canyons. By 2100, and under the high CO2 scenario RCP8.5, pH reductions exceeding −0.2 (−0.3) units are projected in close to 23% (~15%) of North Atlantic deep-sea canyons and ~8% (3%) of seamounts – including seamounts proposed as sites of marine protected areas. The spatial pattern of impacts reflects the depth of the pH perturbation and does not scale linearly with atmospheric CO2 concentration. Impacts may cause negative changes of the same magnitude or exceeding the current target of 10% of preservation of marine biomes set by the convention on biological diversity, implying that ocean acidification may offset benefits from conservation/management strategies relying on the regulation of resource exploitation.

Global reductions in seafloor biomass in response to climate change

Seafloor organisms are vital for healthy marine ecosystems, contributing to elemental cycling, benthic remineralization, and ultimately sequestration of carbon. Deep-sea life is primarily reliant on the export flux of particulate organic carbon from the surface ocean for food, but most ocean biogeochemistry models predict global decreases in export flux resulting from 21st century anthropogenically induced warming. Here we show that decadal-to-century scale changes in carbon export associated with climate change lead to an estimated 5.2% decrease in future (2091–2100) global open ocean benthic biomass under RCP8.5 (reduction of 5.2 Mt C) compared with contemporary conditions (2006–2015). Our projections use multi-model mean export flux estimates from eight fully coupled earth system models, which contributed to the Coupled Model Intercomparison Project Phase 5, that have been forced by high and low representative concentration pathways (RCP8.5 and 4.5, respectively). These export flux estimates are used in conjunction with published empirical relationships to predict changes in benthic biomass. The polar oceans and some upwelling areas may experience increases in benthic biomass, but most other regions show decreases, with up to 38% reductions in parts of the northeast Atlantic. Our analysis projects a future ocean with smaller sized infaunal benthos, potentially reducing energy transfer rates though benthic multicellular food webs. More than 80% of potential deep-water biodiversity hotspots known around the world, including canyons, seamounts, and cold-water coral reefs, are projected to experience negative changes in biomass. These major reductions in biomass may lead to widespread change in benthic ecosystems and the functions and services they provide.

Influence of climate change on short term management of field crops – A modelling approach

Climatic change is likely to have an influence on arable farms in Central Europe. We use a modelling approach to assess the effects of weather and its long term development due to climate change on short-term decisions like planting and harvesting, as well as yields. Two models are coupled, a farm management model FarmActor and the crop growth model system Expert-N to investigate the interplay between management and crop growth on a daily basis. We examine different methods of adapting expectations concerning the timing of cropping actions and annual yields to actual observed weather and yield data. Our study focuses on the two major crops winter wheat and silage maize in the Swabian Alb in southwestern Germany. Results show that the model can satisfactorily reproduce the development of planting and harvesting as well as yields that have occurred in the past. Different methods of expectation formation only show minor differences in their effect on action dates and yields. Future climatic change is likely to shift the timing of field actions. Assuming no change in technology (e.g. cultivars), summer crops may be seeded earlier while winter crops could tend to be sown later; harvest may occur earlier and yields might slightly decrease while showing more volatility. This modelling approach has the potential to increase the knowledge about risk profiles of short time agricultural management actions and to improve the land use modelling part of coupled earth system models.

Intercomparison of the capabilities of simplified climate models to project the effects of aviation CO2 on climate

This study evaluates the capabilities of the carbon cycle and energy balance treatments relative to the effect of aviation CO2 emissions on climate in several existing simplified climate models (SCMs) that are either being used or could be used for evaluating the effects of aviation on climate. Since these models are used in policy-related analyses, it is important that the capabilities of such models represent the state of understanding of the science. We compare the Aviation Environmental Portfolio Management Tool (APMT) Impacts climate model, two models used at the Center for International Climate and Environmental Research-Oslo (CICERO-1 and CICERO-2), the Integrated Science Assessment Model (ISAM) model as described in Jain et al. (1994), the simple Linear Climate response model (LinClim) and the Model for the Assessment of Greenhouse-gas Induced Climate Change version 6 (MAGICC6). In this paper we select scenarios to illustrate the behavior of the carbon cycle and energy balance models in these SCMs. This study is not intended to determine the absolute and likely range of the expected climate response in these models but to highlight specific features in model representations of the carbon cycle and energy balance models that need to be carefully considered in studies of aviation effects on climate. These results suggest that carbon cycle models that use linear impulse-response-functions (IRF) in combination with separate equations describing air–sea and air–biosphere exchange of CO2 can account for the dominant nonlinearities in the climate system that would otherwise not have been captured with an IRF alone, and hence, produce a close representation of more complex carbon cycle models. Moreover, results suggest that an energy balance model with a 2-box ocean sub-model and IRF tuned to reproduce the response of coupled Earth system models produces a close representation of the globally-averaged temperature response of more complex energy balance models.

The Arctic response to remote and local forcing of black carbon

Abstract. Recent studies suggest that the Arctic temperature response to black carbon (BC) forcing depend strongly on the location of the forcing. We investigate how atmospheric BC in the mid-latitudes remotely influence the Arctic climate, and compare this with the response to atmospheric BC located in the Arctic itself. In this study, idealized climate simulations are carried out with a fully coupled Earth System Model, which includes a comprehensive treatment of aerosol microphysics. In order to determine how BC transported to the Arctic and BC sources not reaching the Arctic impact the Arctic climate, atmospheric BC concentrations are scaled up in the mid-latitudes (28–60° N) and in the Arctic (60–90° N), respectively. Estimates of the impact on the Arctic energy budget are represented by analyzing radiation fluxes at the top of the atmosphere and at the surface, surface turbulent fluxes, and meridional heat transport in the atmosphere. Our calculations show that increased BC forcing in the Arctic atmosphere reduces the surface air temperature in the Arctic with a corresponding increase in the sea-ice fraction, despite the increased planetary absorption of sunlight. The analysis indicates that this effect is due to a combination of a weakening of the northward heat transport caused by a reduction in the meridional temperature gradient and a dimming at the surface. On the other hand we find that BC forcing at the mid-latitudes warms the Arctic surface significantly and decreases the sea-ice fraction. Our model calculations indicate that atmospheric BC forcing outside the Arctic may be more important for the Arctic climate change than the forcing in the Arctic itself. These results suggest that mitigation strategies for the Arctic climate should also address BC sources in locations outside the Arctic even if they do not contribute much to BC in the Arctic.