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Abstract. Historical time series of surface temperature and ocean heat content changesare commonly used metrics to diagnose climate change and estimate propertiesof the climate system. We show that recent trends, namely the slowing ofsurface temperature rise at the beginning of the 21st century and theacceleration of heat stored in the deep ocean, have a substantial impact onthese estimates. Using the Massachusetts Institute of Technology Earth SystemModel (MESM), we vary three model parameters that influence the behavior ofthe climate system: effective climate sensitivity (ECS), the effective oceandiffusivity of heat anomalies by all mixing processes (Kv), and the netanthropogenic aerosol forcing scaling factor. Each model run is compared toobserved changes in decadal mean surface temperature anomalies and the trendin global mean ocean heat content change to derive a joint probabilitydistribution function for the model parameters. Marginal distributions forindividual parameters are found by integrating over the other two parameters.To investigate how the inclusion of recent temperature changes affects ourestimates, we systematically include additional data by choosing periods thatend in 1990, 2000, and 2010. We find that estimates of ECS increase inresponse to rising global surface temperatures when data beyond 1990 areincluded, but due to the slowdown of surface temperature rise in the early21st century, estimates when using data up to 2000 are greater than when dataup to 2010 are used. We also show that estimates of Kv increase inresponse to the acceleration of heat stored in the ocean as data beyond 1990are included. Further, we highlight how including spatial patterns of surfacetemperature change modifies the estimates. We show that including latitudinalstructure in the climate change signal impacts properties with spatialdependence, namely the aerosol forcing pattern, more than properties definedfor the global mean, climate sensitivity, and ocean diffusivity.

Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates.

Nitrogen (N) availability exerts strong control on carbon storage in the forests of Northern Eurasia. Here, using a process-based model, we explore how three factors that alter N availability-permafrost degradation, atmospheric N deposition, and the abandonment of agricultural land to forest regrowth (land-use legacy)-affect carbon storage in the region's forest vegetation over the 21st century within the context of two IPCC global-change scenarios (RCPs 4.5 and 8.5). For RCP4.5, enhanced N availability results in increased tree carbon storage of 27.8 Pg C, with land-use legacy being the most important factor. For RCP8.5, enhanced N availability results in increased carbon storage in trees of 13.4 Pg C, with permafrost degradation being the most important factor. Our analysis reveals complex spatial and temporal patterns of regional carbon storage. This study underscores the importance of considering carbon-nitrogen interactions when assessing regional and sub-regional impacts of global change policies.

Abstract. Historical time series of surface temperature and ocean heat content changesare commonly used metrics to diagnose climate change and estimate propertiesof the climate system. We show that recent trends, namely the slowing ofsurface temperature rise at the beginning of the 21st century and theacceleration of heat stored in the deep ocean, have a substantial impact onthese estimates. Using the Massachusetts Institute of Technology Earth SystemModel (MESM), we vary three model parameters that influence the behavior ofthe climate system: effective climate sensitivity (ECS), the effective oceandiffusivity of heat anomalies by all mixing processes (Kv), and the netanthropogenic aerosol forcing scaling factor. Each model run is compared toobserved changes in decadal mean surface temperature anomalies and the trendin global mean ocean heat content change to derive a joint probabilitydistribution function for the model parameters. Marginal distributions forindividual parameters are found by integrating over the other two parameters.To investigate how the inclusion of recent temperature changes affects ourestimates, we systematically include additional data by choosing periods thatend in 1990, 2000, and 2010. We find that estimates of ECS increase inresponse to rising global surface temperatures when data beyond 1990 areincluded, but due to the slowdown of surface temperature rise in the early21st century, estimates when using data up to 2000 are greater than when dataup to 2010 are used. We also show that estimates of Kv increase inresponse to the acceleration of heat stored in the ocean as data beyond 1990are included. Further, we highlight how including spatial patterns of surfacetemperature change modifies the estimates. We show that including latitudinalstructure in the climate change signal impacts properties with spatialdependence, namely the aerosol forcing pattern, more than properties definedfor the global mean, climate sensitivity, and ocean diffusivity.

The Massachusetts Institute of Technology Integrated Global System Model (IGSM) is designed for analyzing the global environmental changes that may result from anthropogenic causes, quantifying the uncertainties associated with the projected changes, and assessing the costs and environmental effectiveness of proposed policies to mitigate climate risk. The IGSM consists of the Massachusetts Institute of Technology Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model. This paper documents the current version of the MESM, which includes a two-dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly-diffusing ocean model.

Efforts to estimate the physical and economic impacts of future climate change face substantial challenges. To enrich the currently popular approaches to impact analysis-which involve evaluation of a damage function or multi-model comparisons based on a limited number of standardized scenarios-we propose integrating a geospatially resolved physical representation of impacts into a coupled human-Earth system modeling framework. Large internationally coordinated exercises cannot easily respond to new policy targets and the implementation of standard scenarios across models, institutions and research communities can yield inconsistent estimates. Here, we argue for a shift toward the use of a self-consistent integrated modeling framework to assess climate impacts, and discuss ways the integrated assessment modeling community can move in this direction. We then demonstrate the capabilities of such a modeling framework by conducting a multi-sectoral assessment of climate impacts under a range of consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations.

Efforts to estimate the physical and economic impacts of future climate change face substantial challenges. To enrich the currently popular approaches to impact analysis-which involve evaluation of a damage function or multi-model comparisons based on a limited number of standardized scenarios-we propose integrating a geospatially resolved physical representation of impacts into a coupled human-Earth system modeling framework. Large internationally coordinated exercises cannot easily respond to new policy targets and the implementation of standard scenarios across models, institutions and research communities can yield inconsistent estimates. Here, we argue for a shift toward the use of a self-consistent integrated modeling framework to assess climate impacts, and discuss ways the integrated assessment modeling community can move in this direction. We then demonstrate the capabilities of such a modeling framework by conducting a multi-sectoral assessment of climate impacts under a range of consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations.

For over 20 years, the Massachusetts Institute of Technology Earth System Model (MESM) has been used extensively for climate change research. The model is under continuous development with components being added and updated. To provide transparency in the model development, we perform a baseline evaluation by comparing model behavior and properties in the newest version to the previous model version. In particular, changes resulting from updates to the land surface model component and the input forcings used in historical simulations of climate change are investigated. We run an 1800-member ensemble of MESM historical climate simulations where the model parameters that set climate sensitivity, the rate of ocean heat uptake, and the net anthropogenic aerosol forcing are systematically varied. By comparing model output to observed patterns of surface temperature changes and the linear trend in the increase in ocean heat content, we derive probability distributions for the three model parameters. Furthermore, we run a 372-member ensemble of transient climate simulations where all model forcings are fixed and carbon dioxide concentrations are increased at the rate of 1 % yearĝ'1. From these runs, we derive response surfaces for transient climate response and thermosteric sea level rise as a function of climate sensitivity and ocean heat uptake. We show that the probability distributions shift towards higher climate sensitivities and weaker aerosol forcing when using the new model and that the climate response surfaces are relatively unchanged between model versions. Because the response surfaces are independent of the changes to the model forcings and similar between model versions with different land surface models, we suggest that the change in land surface model has limited impact on the temperature evolution in the model. Thus, we attribute the shifts in parameter estimates to the updated model forcings.

Northern Eurasia is made up of a complex and diverse set of physical, ecological, climatic and human systems, which provide important ecosystem services including the storage of substantial stocks of carbon in its terrestrial ecosystems. At the same time, the region has experienced dramatic climate change, natural disturbances and changes in land management practices over the past century. For these reasons, Northern Eurasia is both a critical region to understand and a complex system with substantial challenges for the modeling community. This review is designed to highlight the state of past and ongoing efforts of the research community to understand and model these environmental, socioeconomic, and climatic changes. We further aim to provide perspectives on the future direction of global change modeling to improve our understanding of the role of Northern Eurasia in the coupled human-Earth system. Modeling efforts have shown that environmental and socioeconomic changes in Northern Eurasia can have major impacts on biodiversity, ecosystems services, environmental sustainability, and the carbon cycle of the region, and beyond. These impacts have the potential to feedback onto and alter the global Earth system. We find that past and ongoing studies have largely focused on specific components of Earth system dynamics and have not systematically examined their feedbacks to the global Earth system and to society. We identify the crucial role of Earth system models in advancing our understanding of feedbacks within the region and with the global system. We further argue for the need for integrated assessment models (IAMs), a suite of models that couple human activity models to Earth system models, which are key to address many emerging issues that require a representation of the coupled human-Earth system.

We designed scenarios for impact assessment that explicitly address policy choices and uncertainty in climate response. Economic projections and the resulting greenhouse gas emissions for the “no climate policy” scenario and two stabilization scenarios: at 4.5 W/m2 and 3.7 W/m2 by 2100 are provided. They can be used for a broader climate impact assessment for the US and other regions, with the goal of making it possible to provide a more consistent picture of climate impacts, and how those impacts depend on uncertainty in climate system response and policy choices. The long-term risks, beyond 2050, of climate change can be strongly influenced by policy choices. In the nearer term, the climate we will observe is hard to influence with policy, and what we actually see will be strongly influenced by natural variability and the earth system response to existing greenhouse gases. In the end, the nature of the system is that a strong effect of policy, especially directed toward long-lived GHGs, will lag by 30 to 40 years its implementation.

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US.

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia. © 2013 IOP Publishing Ltd.

This paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The focus is on long-term climate projections designed to 1) quantify the climate change commitment of different radiative forcing trajectories and 2) explore the extent to which climate change is reversible on human time scales. All commitment simulations follow the four representative concentration pathways (RCPs) and their extensions to year 2300. MostEMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year-2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near-preindustrial values in most models for RCPs 2.6-6.0. The MOC weakening is more persistent for RCP8.5. Elimination of anthropogenic CO2 emissions after 2300 results in slowly decreasing atmospheric CO2 concentrations. At year 3000 atmospheric CO2 is still at more than half its year-2300 level in all EMICs forRCPs 4.5-8.5. Surface air temperature remains constant or decreases slightly and thermosteric sea level rise continues for centuries after elimination ofCO2 emissions in allEMICs.Restoration of atmosphericCO2 fromRCPto preindustrial levels over 100-1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to preindustrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2. © 2013 American Meteorological Society.

The article studies the economic essence of depreciation accounting. Various points of view are examined by the authors on the issue of the economic nature of depreciation deductions and the order of their reflection in accounting and reporting. Special attention is paid to the method of cost allocation for the acquisition of assets on the basis of their contribution to net income. It is established that in accounting, the compliance of two financial flows – income and expenditure of the company, and the comparison within specific reporting periods should be ensured. It is proposed to apply a method for reserving amounts of depreciation depending on the result of the financial activities of the company. The mechanism of redundancy is described in detail, the essence of which lies in the emergence of two streams of depreciation. It is proved that the proposed method of redundancy of depreciation deductions will take into account the trends in the income of the company’s activities and fully implement the principle of matching revenues expenses incurred.

A global biofuels program will lead to intense pressures on land supply and can increase greenhouse gas emissions from land-use changes. Using linked economic and terrestrial biogeochemistry models, we examined direct and indirect effects of possible land-use changes from an expanded global cellulosic bioenergy program on greenhouse gas emissions over the 21st century. Our model predicts that indirect land use will be responsible for substantially more carbon loss (up to twice as much) than direct land use; however, because of predicted increases in fertilizer use, nitrous oxide emissions will be more important than carbon losses themselves in terms of warming potential A global greenhouse gas emissions policy that protects forests and encourages best practices for nitrogen fertilizer use can dramatically reduce emissions associated with biofuels production. doi: 10.1126/science.1180251