ALBERT

Description: This study introduces the Systematic Correlation Matrix Evaluation (SCoMaE) method, a bottom-up approach which combines expert judgment and statistical information to systematically select transparent, non redundant indicators for a com- prehensive assessment of the state of the Earth system. The methods consists of three basic steps: 1) Calculation of a correlation matrix among variables relevant for a given research question, 2) Systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators, and 3) Interpretation of the identified clusters, enabling a learning effect from the selection of indicators. Optional further analysis steps include: 4) Testing the robustness of identified clusters with respect to changes in forcing or boundary conditions, 5) Enabling a comparative assessment of varying scenarios by constructing and evaluating a common correlation matrix, or 6) Inclusion of expert judgment such as to prescribe indicators, to allow for considerations other than statistical consistency. The exemplary application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of re-evaluating indicators identified in a historical scenario simulation for an accurate assessment of an intermediate-high, as well as a business-as-usual, climate change scenario simulation, which arises from changes in prevailing correlations in the Earth system under varying climate forcing. For a comparative assessment of the three climate change scenarios, we construct and evaluate a common correlation matrix, in which we identify robust correlations between variables across the three considered scenarios.

Description: The amount of additional future temperature change following a complete cessation of CO2 emissions is a measure of the unrealized warming to which we are committed due to CO2 already emitted to the atmosphere. This “zero emissions commitment” (ZEC) is also an important quantity when estimating the remaining carbon budget – a limit on the total amount of CO2 emissions consistent with limiting global mean temperature at a particular level. In the recent IPCC Special Report on Global Warming of 1.5 ∘C, the carbon budget framework used to calculate the remaining carbon budget for 1.5 ∘C included the assumption that the ZEC due to CO2 emissions is negligible and close to zero. Previous research has shown significant uncertainty even in the sign of the ZEC. To close this knowledge gap, we propose the Zero Emissions Commitment Model Intercomparison Project (ZECMIP), which will quantify the amount of unrealized temperature change that occurs after CO2 emissions cease and investigate the geophysical drivers behind this climate response. Quantitative information on ZEC is a key gap in our knowledge, and one that will not be addressed by currently planned CMIP6 simulations, yet it is crucial for verifying whether carbon budgets need to be adjusted to account for any unrealized temperature change resulting from past CO2 emissions. We request only one top-priority simulation from comprehensive general circulation Earth system models (ESMs) and Earth system models of intermediate complexity (EMICs) – a branch from the 1 % CO2 run with CO2 emissions set to zero at the point of 1000 PgC of total CO2 emissions in the simulation – with the possibility for additional simulations, if resources allow. ZECMIP is part of CMIP6, under joint sponsorship by C4MIP and CDRMIP, with associated experiment names to enable data submissions to the Earth System Grid Federation. All data will be published and made freely available.

Description: Cumulative CO2 emissions are a robust predictor of mean temperature increase. However, many societal impacts are driven by exposure to extreme weather conditions. Here, we show that cumulative emissions can be robustly linked to regional changes of a heat exposure indicator, as well as the resulting socioeconomic impacts associated with labour productivity loss in vulnerable economic sectors. We estimate historical and future increases in heat exposure using simulations from eight Earth System Models. Both the global intensity and spatial pattern of heat exposure evolve linearly with cumulative emissions across scenarios (1% CO2, RCP4.5 and RCP8.5). The pattern of heat exposure at a given level of global temperature increase is strongly affected by non-CO2 forcing. Global non-CO2 greenhouse gas emissions amplify heat exposure, while high local emissions of aerosols could moderate exposure. Considering CO2 forcing only, we commit ourselves to an additional annual loss of labour productivity of about 2% of total GDP per unit of trillion tonne of carbon emitted. This loss doubles when adding non-CO2 forcing of the RCP8.5 scenario. This represents an additional economic loss of about 4,400 G$ every year (i.e. 0.59 $/tCO2), varying across countries with generally higher impact in lower-income countries.

Description: This study introduces the Systematic Correlation Matrix Evaluation (SCoMaE) method, a bottom–up approach which combines expert judgment and statistical information to systematically select transparent, nonredundant indicators for a comprehensive assessment of the state of the Earth system. The methods consists of two basic steps: (1) the calculation of a correlation matrix among variables relevant for a given research question and (2) the systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators. Optional further analysis steps include (3) the interpretation of the identified clusters, enabling a learning effect from the selection of indicators, (4) testing the robustness of identified clusters with respect to changes in forcing or boundary conditions, (5) enabling a comparative assessment of varying scenarios by constructing and evaluating a common correlation matrix, and (6) the inclusion of expert judgment, for example, to prescribe indicators, to allow for considerations other than statistical consistency. The example application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of reevaluating indicators identified in a historical scenario simulation for an accurate assessment of an intermediate–high, as well as a business-as-usual, climate change scenario simulation. This necessity arises from changes in prevailing correlations in the Earth system under varying climate forcing. For a comparative assessment of the three climate change scenarios, we construct and evaluate a common correlation matrix, in which we identify robust correlations between variables across the three considered scenarios.

Description: The amount of additional future temperature change following a complete cessation of CO2 emissions is a measure of the unrealized warming to which we are committed due to CO2 already emitted to the atmosphere. This "Zero Emissions Commitment" (ZEC) is also an important quantity when estimating the remaining carbon budget – a limit on the total amount of CO2 emissions consistent with limiting global mean temperature at a particular level. In the recent IPCC Special Report on Global Warming of 1.5 °C, the carbon budget framework used to calculate the remaining carbon budget for 1.5 °C included the assumption that the ZEC due to CO2 emissions is negligible and close to zero. Previous research has shown significant uncertainty even in the sign of the ZEC. To close this knowledge gap, we propose the Zero Emissions Commitment Model Intercomparison Project (ZECMIP), which will quantify the amount of unrealized temperature change that occurs after CO2 emissions cease and investigate the geophysical drivers behind this climate response. Quantitative information on ZEC is a key gap in our knowledge, and one that will not be addressed by currently planned CMIP6 simulations, yet it is crucial for verifying whether carbon budgets need to be adjusted to account for any unrealized temperature change resulting from past CO2 emissions. We request only one top priority simulation from comprehensive general circulation Earth System Models (ESMs) and Earth System Models of Intermediate Complexity (EMICs) – a branch from the 1 % CO2 run with CO2 emissions set to zero at the point of 1000 PgC of total CO2 emissions in the simulation – with the possibility for additional simulations, if resources allow. ZECMIP is part of CMIP6, under joint sponsorship by C4MIP and CDRMIP, with associated experiment names to enable data submissions to Earth System Grid Federation. All data will be published and made freely available.

Description: Selecting appropriate indicators is essential to aggregate the information provided by climate model outputs into a manageable set of relevant metrics on which assessments of climate engineering (CE) can be based. From all the variables potentially available from climate models, indicators need to be selected that are able to inform scientists and society on the development of the Earth system under CE, as well as on possible impacts and side effects of various ways of deploying CE or not. However, the indicators used so far have been largely identical to those used in climate change assessments and do not visibly reflect the fact that indicators for assessing CE (and thus the metrics composed of these indicators) may be different from those used to assess global warming. Until now, there has been little dedicated effort to identifying specific indicators and metrics for assessing CE. We here propose that such an effort should be facilitated by a more decision-oriented approach and an iterative procedure in close interaction between academia, decision makers, and stakeholders. Specifically, synergies and trade-offs between social objectives reflected by individual indicators, as well as decision-relevant uncertainties should be considered in the development of metrics, so that society can take informed decisions about climate policy measures under the impression of the options available, their likely effects and side effects, and the quality of the underlying knowledge base.

Description: This study introduces the Systematic Correlation Matrix Evaluation (SCoMaE) method, a bottom–up approach which combines expert judgment and statistical information to systematically select transparent, nonredundant indicators for a comprehensive assessment of the state of the Earth system. The methods consists of two basic steps: (1) the calculation of a correlation matrix among variables relevant for a given research question and (2) the systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators. Optional further analysis steps include (3) the interpretation of the identified clusters, enabling a learning effect from the selection of indicators, (4) testing the robustness of identified clusters with respect to changes in forcing or boundary conditions, (5) enabling a comparative assessment of varying scenarios by constructing and evaluating a common correlation matrix, and (6) the inclusion of expert judgment, for example, to prescribe indicators, to allow for considerations other than statistical consistency. The example application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of reevaluating indicators identified in a historical scenario simulation for an accurate assessment of an intermediate–high, as well as a business-as-usual, climate change scenario simulation. This necessity arises from changes in prevailing correlations in the Earth system under varying climate forcing. For a comparative assessment of the three climate change scenarios, we construct and evaluate a common correlation matrix, in which we identify robust correlations between variables across the three considered scenarios.

Description: While terrestrial precipitation is a societally highly relevant climate variable, there is little consensus among climate models about its projected 21st century changes. An important source of precipitable water over land is plant transpiration. Plants control transpiration by opening and closing their stomata. The sensitivity of this process to increasing CO2 concentrations is uncertain. To assess the impact of this uncertainty on future climate, we perform experiments with an intermediate complexity Earth System Climate Model (UVic ESCM) for a range of model-imposed transpiration-sensitivities to CO2. Changing the sensitivity of transpiration to CO2 causes simulated terrestrial precipitation to change by −10% to +27% by 2100 under a high emission scenario. This study emphasises the importance of an improved assessment of the dynamics of environmental impact on vegetation to better predict future changes of the terrestrial hydrological and carbon cycles.