Displays

We propose a statistical model of the global carbon budget as represented in the annual data set made available by the Global Carbon Project (Friedlingsstein et al., 2019, Earth System Science Data 11, 1783-1838). The model connects four main objects of interest: atmospheric CO2 concentrations, anthropogenic CO2 emissions, the absorption of CO2 by the terrestrial biosphere (land sink) and by the ocean (ocean sink).  The model captures the global carbon budget equation, which states that emissions not absorbed by either land or ocean sinks must remain in the atmosphere and constitute a flow to the stock of atmospheric concentrations. Emissions depend on global economic activity as measured by World gross domestic product (GDP), and sink activity depends on the level of atmospheric concentrations (fertilization). The model is cast in a state-space system, which facilitates estimation of the parameters of the model using the Kalman filter and the method of maximum likelihood. We illustrate the usefulness of the model in two applications: (i) short-horizon forecasts of all variables in the model, which is an output of the Kalman filter; and (ii) long-horizon projections of climate variables, implied by certain assumptions on future World GDP, are constructed from the model and compared with those coming from the Representative Concentration Pathway scenarios. The statistical nature of the model allows or an assessment of parameter estimation uncertainty in the forecast and projection exercises.

How to cite: Bennedsen, M., Hillebrand, E., and Koopman, S. J.: A statistical model of the global carbon budget, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18986, https://doi.org/10.5194/egusphere-egu2020-18986, 2020

How to cite: Rising, J., Dietz, S., Stoerk, T., and Wagner, G.: Tipping Points in the Climate System and the Economics of Climate Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2959, https://doi.org/10.5194/egusphere-egu2020-2959, 2020

Estimates of physical, social and economic impacts of climate change are less accurate than usually thought because the impacts literature has largely neglected the internal variability of the climate system. Climate change scenarios are highly sensitive to the initial conditions of the climate system due the chaotic dynamics of weather. As the initial conditions of the climate system are unknown with a sufficiently high level of precision, each future climate scenario – for any given model parameterization and level of exogenous forcing – is only one of the many possible future realizations of climate. The impacts literature usually relies on only one realization randomly taken out of the full distribution of future climates. Here we use one of the few available large scale ensembles produced to study internal variability and an econometric model of climate change impacts on United States (US) agricultural productivity to show that the range of impacts is much larger than previously thought. Different ensemble members lead to significantly different impacts. Significant sign reversals are frequent. Relying only on one ensemble member leads to incorrect conclusions on the effect of climate change on agriculture in most of the US counties. Impacts studies should start using large scale ensembles of future climate change to predict damages. Climatologists should ramp-up efforts to run large ensembles for all GCMs, for at least the most frequently used scenarios of exogenous forcing.

How to cite: Massetti, E. and Di Lorenzo, E.: Chaos in Estimates of Climate Change Impacts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21065, https://doi.org/10.5194/egusphere-egu2020-21065, 2020

Here we demonstrate how the same aerosol emissions, released from different locations, lead to different regional and global changes in the physical environment, in turn resulting in divergent magnitudes and spatial distributions of societal impacts. Atmospheric chemistry and the general circulation do not evenly distribute aerosols around the globe, so aerosol impacts -- both direct and via interactions with the general circulation -- vary spatially. Our repeat-cycle perturbation experiment shows that the same emissions, when released from one of 8 different regions, result in significantly different steady-state distributions of surface particulate matter (PM_2.5), total column aerosol optical depth (AOD), surface temperature, and precipitation. We link these changes in the physical environment to established temperature, precipitation, AOD, and PM_2.5 damage functions to estimate both local and global impacts on infant mortality, crop yields, and economic growth. Because the damages associated with these aerosol and aerosol precursor emissions are strongly emission-location dependent, the marginal dollar spent on mitigation would have very different returns in different locations, both locally and globally. This has important implications for calculating a realistic social cost of carbon, since these aerosol-mediated effects are ultimately inseparable from the processes producing CO₂ emissions.

How to cite: Burney, J., Persad, G., Proctor, J., Burke, M., Bendavid, E., Heft-Neal, S., and Caldeira, K.: Aerosol - Climate Interactions, the Distribution of Aerosol Impacts, and Implications for the Social Cost of Carbon, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8332, https://doi.org/10.5194/egusphere-egu2020-8332, 2020

Integrated Assessment Models are a key tool to search and evaluate climate policies - i.e. a set of measures best suited to avoid the worst of climate change without “harming the economy” too much. 
Climate action µ(t) is typically portrayed as coming at a cost (relative to a no-policy case) C(µ), where C is a positive, monotonously increasing function. 
However, this representation ignores economic dynamics. For instance, it assumes that CO2 abatement costs today are independent from efforts done last year, whereas in reality, previous investments in infrastructure or knowledge will have effects on abatement and abatement costs in the future. More generally speaking, the economy is a complex system of interacting players, capable of path-dependent behaviour, multiple equilibria or out-of-equilibrium dynamics, and transitions between states, and climate policy measures (or climate impacts) targeting some actors can affect the whole system. 

Agent-based modelling has in recent years emerged as a tool to break the constraints imposed by generalised equilibrium models underlying most IAMs. Agent-based models directly simulate the activities of diverse interacting agents, rather than making assumptions of the aggregate behaviour of groups of agents. 

Here, we present an agent-based Integrating Assessment Model, the Dystopian Schumpter-Keynes (DSK) model. It contains an industrial sector with interacting machine and consumption good firms, a banking sector, a government, and an electricity supplier, coupled to a climate module. The model has been used, among other things, to investigate how different types of climate impacts propagate through the economy. In this presentation, we focus on climate policy. In particular, we investigate
1.  which policy tools, or combination of tools, are effective at bringing about a sufficiently rapid decarbonisation. Is a uniform carbon tax really sufficient to cause a green transition? 
2.  what will be the side effects on the economy. Will there be ongoing strain on the economy, or will costs be transitional - potentially even with long-term benefits? 

How to cite: Wieners, C., Lamperti, F., Roventini, A., and Buizza, R.: Embracing dynamic complexity in climate economics: The DSK Agent-based Integrated Assessment Modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18055, https://doi.org/10.5194/egusphere-egu2020-18055, 2020

To understand the evolution of climate time series, it is essential to take account of their non-stationary nature with both stochastic trends and distributional shifts: see e.g., . Using the novel approach of saturation estimation, explained in the presentation, we model observational records on evolving climate processes that also shift, undertaking empirical studies that are complementary to analyses based on laws of conservation of energy and physical process-based models. Despite saturation estimation creating more candidate variables than observations in the initial general formulation, our machine learning model selection algorithm has seen many successful applications, illustrated here by modelling the highly non-stationary data on UK CO2 emissions annually 1860-2018 with strong upward then downward trends, punctuated by large outliers from world wars, national coal strikes and stringent legislation.

How to cite: Hendry, D. and Castle, J.: Econometric methods for empirical climate modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3449, https://doi.org/10.5194/egusphere-egu2020-3449, 2020

Existing studies have demonstrated substantial and robust effects of temperature shocks on economic growth, agricultural output, labor productivity, conflict, and health. These studies help clarify the impacts of climate change on social and economic systems, yet the relationship between climate shocks and political outcomes are less well identified. What effect do climate shocks have on states' climate policies? In this paper, I estimate the relationship between national-level temperature and rainfall shocks and the supply and demand for international climate governance. Temperature shocks may increase the salience of climate change in national politics and lead political leaders to adjust policies to match. Similarly, temperature shocks may have material consequences that induce adaptation---one avenue being to use international institutions to coordinate a global response to climate impacts. I argue that the responsiveness of national governments to climate shocks is conditioned by the political and natural context in which governments operate. Specifically, I expect that democratic governments will be more responsive to climate shocks, as will countries that are more vulnerable to the impacts of climate change. I assess whether countries that experience more frequent and more severe climate shocks participate more in international climate politics and adjust their climate policies. I examine four sets of outcomes at the national level: (1) membership in international institutions that govern climate change, (2) the provision and receipt of climate finance, (3) representation at the UN climate conferences, and (4) national climate policies. As the climate changes, we are developing stronger evidence about the underlying natural relationships, but the heterogenous effects across socio-political contexts are less well understood. This paper contributes to our understanding of how climate change shapes national policy and with it the ability of countries to manage and adapt to climate change.

How to cite: Rowan, S.: Climate shocks and the supply and demand for climate governance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20049, https://doi.org/10.5194/egusphere-egu2020-20049, 2020

Integrated Assessment Models (IAMs) are widely used to evaluate the economic costs of climate change, the social cost of carbon and the value of mitigation policies. These IAMs include simple energy balance models (EBMs) to represent the physical climate system and to calculate the timeseries of global mean temperature in response to changing radiative forcing[1]. The EBMs are deterministic in nature which leads to smoothly varying GMT trajectories so for simple monotonically increasing forcing scenarios (e.g. representative concentration pathways (RCPs) 8.5, 6.0 and 4.5) the GMT trajectories are also monotonically increasing. By contrast real world, and global-climate-model-derived, timeseries show substantial inter-annual and inter-decadal variability. Here we present an analysis of the implications of this intrinsic variability for the economic consequences of climate change.

We use a simple stochastic EBM to generate large ensembles of GMT trajectories under each of the RCP forcing scenarios. The damages implied by each trajectory are calculated using the Weitzman damage function. This provides a conditional estimate of the unavoidable uncertainty in implied damages. It turns out to be large and positively skewed due to the shape of the damage function. Under RCP2.6 we calculate a 5-95% range of -30% to +52% of the deterministic value; -13% to +16% under RCP 8.5. The risk premia associated with such unavoidable uncertainty are also significant. Under our economic assumptions a social planner would be willing to pay 32 trillion dollars to avoid just the intrinsic uncertainty in RCP8.5. This figure rises further when allowance is made for epistemic uncertainty in relation to climate sensitivity. We conclude that appropriate representation of stochastic variability in the climate system is important to include in future economic assessments of climate change.

[1] Calel, R. and Stainforth D.A., “On the Physics of Three Integrated Assessment Models”, Bulletin of the American Meteorological Society, 2017.

How to cite: Stainforth, D., Calel, R., Chapman, S., and Watkins, N.: Implications of intrinsic variability for economic assessments of climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9464, https://doi.org/10.5194/egusphere-egu2020-9464, 2020

The social discount rate is a crucial element required for valuing future damages from climate change. A consensus has emerged that discount rates should be declining with horizon, i.e., that the term structure of discount rates should have a negative slope. However, much controversy remains about the appropriate the overall level of discount rates.

We contribute to this debate from a macro-finance perspective, based on the insight that the equilibrium real interest rate, commonly known as r*, is the crucial determinant of the level of discount rates. First, we show theoretically how r* anchors the term structure of discount rates, using the modern macro-finance theory of the term structure of interest rates to provide a new perspective on classic results about social discount rates. Second, we show empirically that new macro-finance estimates of r* have fallen substantially over the past quarter century---consistent with a broader literature that documents such a secular decline. Bayesian estimation of a state-space model for Treasury yields, inflation and the real interest rate allows us to quantify both the decline in r* and the resulting downward shift of the term structure of social discount rates. Third, we document that this decline in r* and the social discount rate boosts the social cost of carbon and has quantitatively important implications for assessing the economic consequences of climate change. In essence, we demonstrate that the lower new normal for interest rates implies a higher new normal for the present value of climate change damages.

How to cite: Bauer, M. and Rudebusch, G.: Discounting Future Climate Change and the Equilibrium Real Interest Rate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6454, https://doi.org/10.5194/egusphere-egu2020-6454, 2020

Anthropogenic climate change has been attributed mainly to the excessive burning of fossil fuels and the release of carbon compounds. On average, 75% of the primary energy is still being produced by means of fossil fuels. In order to mitigate the global effects of climate change, a transition towards low-carbon economies is thus necessary. However, given current technology, this transition requires investments to shift away from high-carbon assets and so the effectiveness of changes in investment decisions depends highly on the expectations about policy change (e.g. regarding carbon pricing). The systemic implications of disruptive technological progress on the prices of carbon-intensive assets are thus compounded by the geopolitical nature of transition risk. If investors are pricing transition risk, this implies prices of high-carbon assets should all be responsive to climate-related policy news. For modelling the dynamics of volatility co-movements at the global scale, we propose an extension to the global volatility factor model of Engle and Martins (\textit{in preparation}). To allow for richer structures of the global volatility process, including dynamics, structural changes, outliers or time-varying parameters, we adapt the indicator saturation approach introduced by Hendry (1999) to the second moment and high-frequency data. In the model, climate change is interpreted as a source of structural change affecting the financial system. The new global volatility model is applied to the daily share prices of major Oil and Gas companies from different countries traded in the NYSE to avoid asynchronicity. As a proxy for climate change risk, we use the climate change news index of Engle et al. (2019). This index is a time series that captures news about long-run climate risk. In particular, we use the innovations in their negative (or bad) news index which is based on sentiment analysis.

How to cite: Martins, S.: Co-movements of financial volatilities in a changing environment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10570, https://doi.org/10.5194/egusphere-egu2020-10570, 2020

A key statistic describing climate change impacts is the “social cost of carbon” (SCC), the total market and non-market costs to society incurred by releasing a ton of CO₂. Estimates of the SCC have risen in recent years, with improved understanding of the risk of climate change to various sectors, including agriculture [1], mortality [2], and economic growth [3].

The total risks of climate impacts also depend on the representation of human-climate feedbacks such as the effect of climate impacts on GDP growth and extremes (rather than a focus only on means), but this relationship has not been extensively studied [4-7]. In this paper, we update the widely used PAGE IAM to investigate how SCC distributions change with the inclusion of climate-economy feedbacks and temperature variability. The PAGE model has recently been improved with representations of permafrost thawing and surface albedo feedback, CMIP6 scenarios, and empirical market damage estimates [8]. We study how changes from PAGE09 to PAGE-ICE affected the SCC, increasing it up to 75%, with a SCC distribution with a mean around $300 for the central SSP2-4.5 scenario. Then we model the effects of different levels of the persistence of damages, for which the persistence parameter is shown to have enormous effects. Adding stochastic interannual regional temperature variations based on an analysis of observational temperature data [9] can increase the hazard rate of economic catastrophes changes the form of the distribution of SCC values. Both the effects of temperature variability and climate-economy feedbacks are region-dependent. Our results highlight the importance of feedbacks and extremes for the understanding of the expected value, distribution, and heterogeneity of climate impacts.

[1] Moore, F. C., Baldos, U., Hertel, T., & Diaz, D. (2017). New science of climate change impacts on agriculture implies higher social cost of carbon. Nature communications, 8(1), 1607.

[2] Carleton, et al. (2018). Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits.

[3] Ricke, K., Drouet, L., Caldeira, K., & Tavoni, M. (2018). Country-level social cost of carbon. Nature Climate Change, 8(10), 895.

[4] Burke, M., et al. (2016). Opportunities for advances in climate change economics. Science, 352(6283), 292–293. https://doi.org/10.1126/science.aad9634

[5] National Academies of Sciences Engineering and Medicine. (2017). Valuing climate damages: updating estimation of the social cost of carbon dioxide. National Academies Press.

[6] Stiglitz, J. E., et al.. (2017). Report of the high-level commission on carbon prices.

[7] Field, C. B., Barros, V., Stocker, T. F., & Dahe, Q. (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change (Vol. 9781107025). https://doi.org/10.1017/CBO9781139177245.009

[8] Yumashev, D., et al. (2019). Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements. Nature Communications, 10(1). https://doi.org/10.1038/s41467-019-09863-x

[9] Brierley, C. M., Koch, A., Ilyas, M., Wennyk, N., & Kikstra, J. S. (2019, March 12). Half the world's population already experiences years 1.5°C warmer than preindustrial. https://doi.org/10.31223/osf.io/sbc3f

How to cite: Kikstra, J., Waidelich, P., Rising, J., Yumashev, D., Hope, C., and Brierley, C.: Climate-economy feedbacks, temperature variability, and the social cost of carbon, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13230, https://doi.org/10.5194/egusphere-egu2020-13230, 2020

We show that several of the most important economic models of climate change produce climate dynamics inconsistent with the current crop of models in climate science. First, most economic models exhibit far too long a delay between an impulse of CO2 emissions and warming. Second, few economic models incorporate positive feedbacks in the carbon cycle, whereby CO2 uptake by carbon sinks diminishes at the margin with increasing cumulative CO2 uptake and temperature. These inconsistencies affect economic prescriptions to abate CO2 emissions. Controlling for how the economy is represented, different climate models result in significantly different optimal CO2 emissions. A long delay between emissions and warming leads to optimal carbon prices that are too low and too much sensitivity of optimal carbon prices to the discount rate. Omitting positive carbon cycle feedbacks also leads to optimal carbon prices that are too low. We conclude it is important for policy purposes to bring economic models in line with the state of the art in climate science.

How to cite: Rezai, A., Dietz, S., van der Ploeg, F., and Venmans, F.: Are economists getting climate dynamics right and does it matter?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20039, https://doi.org/10.5194/egusphere-egu2020-20039, 2020

Integrated assessment models are often criticized because of: i) the simplified treatment of the severe uncertainties involved, ii) the strong dependency on the difficult quantification of future climate damages and iii) their implicit description of adaptation strategies.
We propose a novel approach to tackle these three issues by coupling a closed loop control strategy and an updated AD-DICE (ADaptation - Dynamic Integrated Climate-Economy) model. First, we model explicitly uncertain parametrization and stochastic processes for climate sensitivity, atmospheric temperature, population, productivity, and carbon intensity. We then ensure an adaptive response to the uncertainties by implementing a closed-loop control system where we condition the decision variables on state observation. This leads to an improvement with respect to the traditional static optimization approach. Second, we propose a multi-objective formulation of the optimization problem traditionally solved by DICE in order to separate temperature targets from economic objectives. This allows us to be less dependent on the climate damages quantification while studying the tradeoffs to find compromise solutions. Third, we include an explicit description of adaptation strategies introducing stock and flow adaptation investments as additional decision variables. Thanks to this last modification, we can also thoroughly analyze the tradeoffs between mitigation and adaptation.
Results show that the proposed method outperforms traditional static optimization both in single-objective and multi-objectives contexts. Moreover, we confirm the absolute need for fast and strong mitigation since we observe that the tradeoff between temperature and economic objectives is strongly reduced under uncertainty and when considering adaptation. On the other hand, different adaptation strategies correspond to a different balance of present value damages and economic objectives. By making explicit this tradeoff between two socio-economic objectives, results reveal the political nature of the choice over climate adaptation strategies.

How to cite: Carlino, A., Marangoni, G., Tavoni, M., and Castelletti, A.: Addressing uncertainty, multiple objectives, and adaptation in DICE: Can dynamic planning shed new light on the decision-making process?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14954, https://doi.org/10.5194/egusphere-egu2020-14954, 2020

A major tool that supports climate policy decisions, integrated assessment models are highly vulnerable to their initial assumptions and calibrations. Despite the broad literature rich in both single-model and multi-model sensitivity analyses, universal, well-established practices are still missing in this field. This paper endorses structured global sensitivity analysis (GSA) as an indispensable routine in climate–economic modeling. An application of a high-efficiency GSA method based on polynomial chaos expansions to DICE provides two insights. First, only global and comprehensive—as opposed to local or selective—sensitivity analysis delivers a trustworthy picture of the uncertainty propagated through the model. Second, careful treatment of the model’s structure throughout the analysis reconciles the results with established analytical insights—enhancing these insights with more details. The efficient GSA method provides a comprehensive decomposition of the uncertainty in a model’s output while minimizing computational costs, and is hence potentially applicable to models of higher complexity.

How to cite: Miftakhova, A.: Global Sensitivity Analysis of Optimal Climate Policies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1339, https://doi.org/10.5194/egusphere-egu2020-1339, 2019

To understand how global warming can be kept well-below 2°C and even 1.5°C, climate policy uses scenarios that describe how society could transform in order to reduce its greenhouse gas emissions. Such scenario are typically created with integrated assessment models that include a representation of the economy, and the energy, land-use, and industrial system. However, current climate change scenarios have a key weakness in that they typically focus on reaching specific climate goals in 2100 only.

This choice results in risky pathways that delay action and seemingly inevitably rely on large quantities of carbon-dioxide removal after mid-century. Here we propose a framework that more closely reflects the intentions of the UN Paris Agreement. It focusses on reaching a peak in global warming with either stabilisation or reversal thereafter. This approach provides a critical extension of the widely used Shared Socioecononomic Pathways (SSP) framework and reveals a more diverse picture: an inevitable transition period of aggressive near-term climate action to reach carbon neutrality can be followed by a variety of long-term states. It allows policymakers to explicitly consider near-term climate strategies in the context of intergenerational equity and long-term sustainability.

How to cite: Rogelj, J., Huppmann, D., Krey, V., Riahi, K., Clarke, L., Gidden, M., Nicholls, Z., and Meinshausen, M.: A new scenario logic for the Paris Agreement long-term temperature goal, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10262, https://doi.org/10.5194/egusphere-egu2020-10262, 2020

The assessment of long-term greenhouse gas emissions scenarios and societal transformation pathways is a key component of the IPCC Working Group 3 (WG3) on the Mitigation of Climate Change. A large scientific community, typically using integrated assessment models and econometric frameworks, supports this assessment in understanding both near-term actions and long-term policy responses and goals related to mitigating global warming. WG3 must systematically assess hundreds of scenarios from the literature to gain an in-depth understanding of long-term emissions pathways, across all sectors, leading to various levels of global warming. Systematic assessment and understanding the climate outcomes of each emissions scenario, requires coordinated processes which have developed over consecutive IPCC assessments. Here, we give an overview of the processes involved in the systematic assessment of long-term mitigation pathways as used in recent IPCC Assessments¹ and being further developed for the IPCC 6^th Assessment Report (AR6). The presentation will explain how modelling teams can submit scenarios to AR6 and invite feedback to the process.

Following discussions amongst IPCC Lead Authors to define the scope of scenarios desired and variables requested, a call for scenarios to support AR6 was launched in September 2019. Modelling teams have registered and submitted scenarios through Autumn 2019 using a new and secure online submission portal, from which authorised Lead Authors can interrogate the scenarios interactively.

This analysis is underpinned by the open-source software pyam, a Python package specifically designed for analysis and visualisation of integrated assessment scenarios². Submitted scenarios are automatically checked for errors and processed using a new climate assessment pipeline. The climate assessment involves infilling and harmonization³ of emissions data, then the scenarios are processed through Simple Climate Models, using the OpenSCM framework⁴, to give probabilistic climate implications for each scenario – atmospheric concentrations, radiative forcing and global mean temperature. The climate assessment accounts for updated climate sensitivity estimates from CMIP6 and WG1,s scenarios are categorized according to climate outcomes and distinguish between timing and levels of net-negative emissions, emissions peak and temperature overshoot. Scenarios are also categorized by other indicators, for consistent use across WG3 chapters, such as: population and GDP; Primary and Final energy use; and shares of renewables, bioenergy and fossil fuels.

The automated framework also facilitates bolt-on analyses, such as estimating the population impacted by biophysical climate impacts⁵, and estimates of avoided damages with the social cost of carbon⁶.

Upon publication of the WG3 AR6 report, all scenario data used in the WG3 Assessment will be publicly available on a Scenario Explorer, an online tool for interrogating and visualizing the data that supports the report. In combination, this framework brings new levels of consistency, transparency and reproducibility to the assessment of scenarios in IPCC WG3 and will be a key resource for the climate community in understanding the main drivers of different transformation pathways.

1. Huppmman et al 2018, Nature Climate Change
2. Gidden and Huppmann, 2019, Journal of Open Source Software
3. Gidden et al 2018 Environ. Model. Softw
4. Nicholls et al 2020
5. Byers et al 2018 Environmental Research Letters
6. Ricke et al 2018 Nature Climate Change

How to cite: Byers, E. A., Riahi, K., Kriegler, E., Krey, V., Schaeffer, R., van Vuuren, D., Gidden, M., Huppmann, D., Kikstra, J., Lamboll, R., Meinshausen, M., Nicholls, Z., and Rogelj, J.: Systematic scenario process to support analysis of long-term emissions scenarios and transformation pathways for the IPCC WG3 6th Assessment Report, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20564, https://doi.org/10.5194/egusphere-egu2020-20564, 2020

Quantifying the climate impacts onto economic outcomes is crucial to inform mitigation and adaptation policy decisions in the context of anthropogenic climate change. Existing macro-level economic impact projections are often derived using calibrated Integrated Assessment Models (IAMs) or empirically-estimated econometric models. Both approaches, however, rarely consider how such impacts would change under macro-level adaptation interventions. Here, we present approaches to econometrically test climate impact estimates for their historical stability to approximate empirical macro-adaptation rates. By modelling deterministic trends and structural breaks as well as socio-economic drivers of adaptation, our approach could provide the basis for a new set of macro-economic impact projections that control for adaptation measures. Ultimately, adaptation-explicit impact projections could be used to inform both mitigation and adaptation decisions and further allow benchmarking of non-empirical modelling approaches.

How to cite: Schwarz, M. and Pretis, F.: Modelling Historical Adaptation Rates to Inform Future Adaptation Pathways, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21004, https://doi.org/10.5194/egusphere-egu2020-21004, 2020

How to cite: van Maanen, N., Dasgupta, S., Gosling, S. N., Piontek, F., Otto, C., and Schleussner, C.-F.: Projections of global labour productivity under climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21342, https://doi.org/10.5194/egusphere-egu2020-21342, 2020

British pre-industrial economic growth has traditionally been analysed from the Malthusian point of view and other more optimistic approaches, but in many cases, ignoring environmental factors. This article explores the inclusion of the climate in this general debate, focusing on one of the colder periods of the last 500 years, known as the Maunder Minimum. The provisional results suggest that climate change and the resulting adaptations may have influenced the start of the English Agricultural Revolution, the Energy Transition and the European Divergence. However, from an econometric point of view these results are not fully conclusive, making it necessary to continue working with better primary sources and other alternative methodologies.

How to cite: Martinez-Gonzalez, J. L.: Assessing climate impacts on English economic growth (1645–1740): an econometric approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21512, https://doi.org/10.5194/egusphere-egu2020-21512, 2020

Exploiting a national-level panel of per capita CO2 emissions and GDP data, we investigate the GDP-CO2 relationship, using a data-driven approach. We conduct an in-sample analysis in which we investigate the shape of the GDP-CO2 relationship. Utilizing the shape of the GDP-CO2 relationship learned, we project CO2 emissions through 2100, using the same set of GDP and population growth scenarios as used by the Intergovernmental Panel of Climate Change (IPCC) for their sixth assessment report due for release in 2021-22. Our analysis is carried out at two levels: at a global, and at the level of five large regions of the world. We consider a semiparametric model specification which places no restrictions on the functional relationship between GDP and CO2, but which allows for country and time specific fixed effects. The nonparametric component of our model is specified as a feedforward neural network, ensuring universal approximation capabilities, theoretically. In a simulation study, we show that our model is able to capture various complex relationships in finite samples of realistic sizes.

How to cite: Jensen, S., Hillebrand, E., and Bennedsen, M.: Investigating the GDP-CO2 relationship using a neural network approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22104, https://doi.org/10.5194/egusphere-egu2020-22104, 2020

CMIP6 (Coupled Model Intercomparison Project Version 6) is currently publishing updates on simulations for Global Climate Models (GCMs). In this paper, we focus on analyzing surface temperature and downward solar radiation (SDSR), which are two essential variables in estimating the transient climate sensitivity (TCS). We carry out the analysis for five GCMs that have published data at the moment. More GCMs will be included in the analysis when data is available. The research period dates from 1960 to 2014, providing the latest available projection for climate forcings. Temperature projections accord reasonably well with observations. This is no surprise, as data for CMIP5 was also aligned with observations.  On the other hand, a striking improvement has been observed with respect to SDSR. According to Storelvmo et al. (2018), CMIP5 models showed no statistically significant trend over time and revealed egregious mismatch with observations, casting major concerns about their fidelity. The data from CMIP6 models, however, this mismatch between simulations and observations is substantially alleviated. Not only is a negative trend recorded, but the significant fall around the beginning of the 1990s, due to the Mount Pinatubo eruption, is also reproduced, though with a slightly smaller scale compared to the observations in that period.
Based on the econometric framework from Phillips et al. (2019), we estimate the TCS for five GCMs. We find that the TCS estimates range from 2.03K to 2.65K. Each reported TCS for the five GCM’s are within it’s corresponding 95% confidence interval for the estimated TCS. It is worth noticing that a 25-year rolling window estimation indicates that average TCS for the GCMs varies greatly along time, though it has a significant upward trend from the beginning of the 1990s until 2009, and flattens, or even decreases, afterward.
We also compute the sample average of the TCS estimates. We find that for the period 1964-2005, which is used in Phillips et al. (2019), the average TCS is 1.82 for the CMIP5 models, and 2.07 for CMIP6. The difference is not significant. For the 1964-2014 period, however, the average TCS estimate for CMIP6 is 2.38, which is significantly higher than the average CMIP5 estimates. Since we find that the CMIP6 simulations reproduce observed trends in RSDS much better than the CMIP5 simulations, when compared to observations, this indicates both that the econometric framework of Phillips et al.(2019) is working very well and captures key drivers of the climate, and that the true TCS is most likely closer to the estimated TCS for observations.

How to cite: Yuan, M. and Leirvik, T.: Trend analysis and transient climate sensitivity revealed by CMIP6, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9697, https://doi.org/10.5194/egusphere-egu2020-9697, 2020

The greatest sources of uncertainty for future sea-level rise are the Greenland and Antarctic ice sheets. An important aspect of this uncertainty is the potential interconnectivity between them, which may amplify underlying instabilities in individual ice sheets. We explore these connections empirically by modelling the ice sheets as a cointegrated system. We consider two specications which allow the ice sheets to follow either an I(1) or an I(2) process in order to disentangle the long-run theory consistent relationships in the data. We examine the stability of these relationships over time both in and out of sample and eximine how a sudden loss of ice in Greenland propagates through the system. We show that a 1 Gigatonne loss of ice leads to a large and persistent loss of ice in West Arctica which is partially offset by an accumulation of ice in East Antarctica. Accounting for the long-run interactions between the ice sheets helps to improve our understanding of future instabilities and provides useful projections of the future paths of the ice sheets.

How to cite: Martinez, A., Jackson, L., Pretis, F., and Juselius, K.: Statistical Approaches for Modeling Ice Sheet Interconnectivity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12487, https://doi.org/10.5194/egusphere-egu2020-12487, 2020

New Zealand’s public insurer, the Earthquake Commission (EQC), provides residential insurance for some weather-related damage. Climate change and the expected increase in intensity and frequency of weather-related events are likely to translate into higher damages and thus an additional financial liability for the EQC. We project future insured damages from extreme precipitation events associated with future projected climatic change. We first estimate the empirical relationship between extreme precipitation events and the EQC’s weather-related insurance claims based on a complete dataset of all claims from 2000 to 2017. We then use this estimated relationship, together with climate projections based on future GHG concentration scenarios from six different dynamically downscaled Regional Climate Models, to predict the impact of future extreme precipitation events on EQC liabilities for different time horizons up to the year 2100. Our results show predicted adverse impacts vary over time and space. The percent change between projected and past damages—the climate change signal—ranges between an increase of 7% and 26% by the end of the century. We also give detailed caveats as to why these quantities might be mis-estimated. The projected increase in the public insurer’s liabilities could also be used to inform private insurers, regulators, and policymakers who are assessing the future performance of both the public and private insurers that cover weather-related risks in the face of climatic change.

How to cite: Pastor, J., Noy, I., Sin, I., Sood, A., Fleming-Munoz, D., and Owen, S.: Projecting the effect of climate-change-induced increases in extreme rainfall on residential property damages, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2789, https://doi.org/10.5194/egusphere-egu2020-2789, 2020

Forest has great value both in storing carbon and timber production. Afforestation has been widely undertaken across countries to achieve their goals in poverty alleviation and environment protection, specifically in mitigating the atmosphere carbon concentration. This study determines the optimal rotations of different forest types in China’s afforestation projects considering the costs of benefit of afforestation and the carbon value under two different carbon accounting rules, tCER and lCER accounting. The optimal rotation periods of three tree species, Eucalyptus, Chinese fir and Poplar, were estimated using data from various Chinese regions. We apply a modified Hartman rotation model to calculate the optimal rotation period. Results show that at carbon price of 15 USD per t CO₂ for a 5-year validation period, the optimal rotation period are all extended with the highest increase (5 years or 29%) found for Chinese fir (E, N, NE) under tCER accounting after considering the value of carbon sequestration. However, the optimal decision for Eucalyptus is extended to 3 years or 60% under lCER accounting. Poplar plantation is less influenced by either tCER or lCER accounting. We further examine the sensitivity of the optimal decision to carbon price and interest rate. Results show the optimal decision of Chinese fir is highly sensitive to the changes of carbon price or interest rate under tCER accounting, while that of Eucalyptus is the most sensitive under lCER accounting. We demonstrate the significant effects of carbon accounting methods and plantation species on the determination of optimal rotation period for afforestation projects. The findings can contribute to the sustainable management of carbon sequestration projects. The methodology can also be applied to other regions in the developing world.

How to cite: Hou, G., Delang, C. O., Lu, X., and Olschewski, R.: Optimizing rotation management of forest plantations: the effects of carbon accounting methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22094, https://doi.org/10.5194/egusphere-egu2020-22094, 2020

In the Tropical Andes, glaciers play a fundamental role for sustaining human livelihoods and ecosystems in headwater areas and further downstream. However, current rates of glacier shrinkage driven by climate change as well as increasing water demand levels bear a threat to long-term water supply. While a growing number of research has covered impacts of climate change and glacier shrinkage on the terrestrial water cycle and potential disaster risks, the associated potential economic losses have barely been assessed.

Here we present an integrated surface-groundwater assessment model for multiple water sectors under current conditions (1981-2016) and future scenarios (2050) of glacier shrinkage and growing water demand. As a case, the lumped model has been applied to the Santa river basin (including the Cordillera Blanca, Andes of Peru) within three subcatchments and considers effects from evapotranspiration, environmental flows and backflows of water use. Therefore, coupled greenhouse gas concentration (RCP2.6 and RCP8.5) and socioeconomic scenarios are used, which provide a broad range of the magnitude of glacier and water volume changes and associated economic impacts. Finally, net water volume released on the long term due to deglaciation effects is quantified and by multiple metrics converted into potential economic costs and losses for the agriculture, household and hydropower sectors. Additionally, the potential damages from outburst floods from current and future lakes have been included. Results for the entire Santa river basin show that water availability would diminish by about 11-16% (57-78 10⁶ m³) in the dry season (June-August) and by some 7-10% (103-155 10⁶ m³) during the wet season (December-February) under selected glacier shrinkage scenarios until 2050. This is a consequence of diminishing glacier contribution to streamflow which until 2050 would reduce from about 45% to 33% for June-August and from 6% to 4% for December-February. A first rough estimate suggests associated economic losses for main water demand sectors (agriculture, hydropower, drinking water) on the order of about 300 10⁶ USD/year by 2050. Additionally, with ongoing glacier shrinkage and the formation of new lakes, about 45,000 inhabitants and 30,000 buildings are expected to be exposed to the risk of outburst floods in the 21^st century.

The pressure on water resources and interconnected socio-eonvironmental systems in the basin is already challenging and expected to further exacerbate within the next decades. Currently, water demand levels are considerably increasing driven by growing irrigated (export) agriculture, population and energy demand which is in a large part sustained by hydropower. A coupling of potential water scarcity driven by climate change with a lack of water governance and high human vulnerabilities, bears strong conflict potentials with negative feedbacks for socio-economic development in the Santa basin and beyond. In this context, our coupled hydro-glacial economic impact model provides important support for future decision-making and long-term water management planning. However, uncertainties are relatively high (uncertainty range to be estimated) due to a lack of (good) hydro-climatic and socio-economic information at appropriate spatiotemporal scales. The presented model framework is potentially transferable to other high mountain catchments in the Tropical Andean region and beyond.

How to cite: Drenkhan, F., Muñoz, R., Huggel, C., Frey, H., Valenzuela, F., and Motschmann, A.: Economic losses from changing hydrology under future climate change, glacier shrinkage and growing water demand in the Tropical Andes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12448, https://doi.org/10.5194/egusphere-egu2020-12448, 2020

The influence of natural conditions on human settlements are immense. While a friendly and calm environment can lead to prosperity and growth, a hostile one with frequent natural disasters can result in stagnation, collapse, and even death. Tropical cyclones, as an unpredictable and recurring disastrous events, pose a considerable threat to prosperous development of human societies. The IPCC estimates that globally around 250 million people are vulnerable to storm surge events every year. If the threat is too large, a natural adaptation strategy would seem to move away to less dangerous places. It thus can be considered puzzling that there is a positive trend of moving to coastal flooding zones in Sub-Saharan Africa, North America and Asia, and this is projected to continue in the future. Additionally, climate change may increase the local exposure to storm surge by rising sea levels and changing intensity of tropical cyclones.

Given this worrisome development, a systematic analysis of the relationship between settlement structures and tropical cyclones is called for. In this paper we analyze whether people relocate from hazardous areas impacted by tropical cyclones. Importantly, the greatest threat from a tropical cyclone is generally due to the accompanying storm surge. But, because storm surge levels are hard to model, as of date no global (economic) impact study has attempted to model or used historic storm surge data to estimate the economic impact of tropical storms. Rather most studies only focus on wind damages, while other also include rain damages. Within this paper, we are closing this gap by explicitly modeling historic storm surge data worldwide from 1850-2015 and linking this to local population settlement. 

By combining data on bathymetry, tidal cycles, weather conditions, and  pressure drop models for the tropical cyclones we are able to estimate spatial storm surge data at a resolution of 5 arc minutes. This data then allows us, in a first step, to analyze its systematic impact on historical geo-referenced population and settlement structure data at a spatial scale of 5 arc minutes. We are able to show some interesting population patterns in response to tropical cyclones. Contrary to many empirical studies, we find that people do settle away from hazardous areas. This effect is especially large for low elevation coastal zones, while for non low elevation coastal areas we find no effect. The same pattern can be found for developing and developed countries, but the shrinking of the population is 39 percent larger in developing countries. 

How to cite: Kunze, S.: The Global Long-term Effects of Storm Surge Damages on Human Settlements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21564, https://doi.org/10.5194/egusphere-egu2020-21564, 2020

As an important means regulating the relationship between human and natural ecosystem, ecological restoration program plays a key role in restoring ecosystem functions. The Grain-for-Green Program (GFGP, One of the world’s most ambitious ecosystem conservation set-aside programs aims to transfer farmland on steep slopes to forestland or grassland to increase vegetation coverage) has been widely implemented from 1999 to 2015 and exerted significant influence on land use and ecosystem services (ESs). In this study, three ecological models (InVEST, RUSLE, and CASA) were used to accurately calculate the three key types of ESs, water yield (WY), soil conservation (SC), and net primary production (NPP) in Karst area of southwestern China from 1982 to 2015. The impact of GFGP on ESs and trade-offs was analyzed. It provides practical guidance in carrying out ecological regulation in Karst area of China under global climate change. Results showed that ESs and trade-offs had changed dramatically driven by GFGP . In detail, temporally, SC and NPP exhibited an increasing trend, while WY exhibited a decreasing trend. Spatially, SC basically decreased from west to east; NPP basically increased from north to south; WY basically increased from west to east; NPP and SC, SC and WY developed in the direction of trade-offs driven by the GFGP, while NPP and WY developed in the direction of synergy. Therefore, future ecosystem management and restoration policy-making should consider trade-offs of ESs so as to achieve sustainable provision of ESs.

How to cite: Wang, X.: Trade-offs and Synergies of Ecosystem Services in Karst Area of China Driven by Grain-for-Green Program, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2259, https://doi.org/10.5194/egusphere-egu2020-2259, 2020

Digital economy is becoming a new engine of China's economic transformation, leading a new path of green and low-carbon development. However, the positive and negative effects of the digital economy on the environment have also been widely debated. The energy consumption of China's digital economy industry is still increasing, but it has received little attention. This paper studies the emerging links between digital economy and low-carbon sustainable development. Understanding the impact of the digital economy on carbon emissions is critical to addressing the challenges of climate change in the digital age.

By integrating input-output methods, this paper establishes a comprehensive framework to evaluate China's digital economy and environmental sustainable development. It can not only evaluate the carbon emissions in various sub-industries of the digital economy, but also reveal its formation and change mechanism by determining its source industries, transfer paths and economic drivers. Using STIRPAT model and provincial panel data from 2001 to 2016, this paper investigates the impact of the digital economy industry on carbon emissions at the national and regional levels. In addition, assess the carbon footprint of the entire digital industry, including the relative contribution of major infrastructure, core and integration components of the digital economy to carbon emissions. The results show that the digital economy helps reduce China's carbon emissions. The digital economy in the central region has a greater impact on carbon emissions than the eastern region, while the western region has unconspicuous impact. With the emergence of the digital economy in the energy system, energy consumption can be reduced and energy efficiency can be improved, which can help reduce carbon emissions in the energy sector, and contribute to the sector's carbon emission reduction goal of about 3%. The positive and negative impacts of the digital economy on the environment have resulted in an inverted U-shaped relationship between the digital economy and carbon emissions. The inflection point of the digital economy is slightly higher than the medium level, which means that carbon emissions may increase further with the development of the digital economy at this stage. Without control, the relative contribution of the digital economy to carbon emissions may exceed 10% by 2030. These findings not only help to advance the existing literature, but also deserve special attention from policy makers.

How to cite: Piao, X. and Cui, X.: Assessing China's Digital Economy and Environmental Sustainability: A Regional Low-Carbon Perspective, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12740, https://doi.org/10.5194/egusphere-egu2020-12740, 2020