Uncertainties in the response of vegetation to rising atmospheric CO concentrations contribute to the large spread in projections of future climate change. Climate-carbon cycle models generally agree that elevated atmospheric CO concentrations will enhance terrestrial gross primary productivity (GPP). However, the magnitude of this CO fertilization effect varies from a 20 per cent to a 60 per cent increase in GPP for a doubling of atmospheric CO concentrations in model studies. Here we demonstrate emergent constraints on large-scale CO fertilization using observed changes in the amplitude of the atmospheric CO seasonal cycle that are thought to be the result of increasing terrestrial GPP. Our comparison of atmospheric CO measurements from Point Barrow in Alaska and Cape Kumukahi in Hawaii with historical simulations of the latest climate-carbon cycle models demonstrates that the increase in the amplitude of the CO seasonal cycle at both measurement sites is consistent with increasing annual mean GPP, driven in part by climate warming, but with differences in CO fertilization controlling the spread among the model trends. As a result, the relationship between the amplitude of the CO seasonal cycle and the magnitude of CO fertilization of GPP is almost linear across the entire ensemble of models. When combined with the observed trends in the seasonal CO amplitude, these relationships lead to consistent emergent constraints on the CO fertilization of GPP. Overall, we estimate a GPP increase of 37 ± 9 per cent for high-latitude ecosystems and 32 ± 9 per cent for extratropical ecosystems under a doubling of atmospheric CO concentrations on the basis of the Point Barrow and Cape Kumukahi records, respectively.
An emergent linear relationship between the long-term sensitivity of tropical land carbon storage to climate warming (γ LT ) and the short-term sensitivity of atmospheric carbon dioxide (CO 2 ) to interannual temperature variability (γ IAV ) has previously been identified by Cox et al. (2013) across an ensemble of Earth system models (ESMs) participating in the Coupled Climate-Carbon Cycle Model Intercomparison Project (C 4 MIP). Here we examine whether such a constraint also holds for a new set of eight ESMs participating in Phase 5 of the Coupled Model Intercomparison Project. A wide spread in tropical land carbon storage is found for the quadrupling of atmospheric CO 2 , which is of the order of 252 ± 112 GtC when carbon-climate feedbacks are enabled. Correspondingly, the spread in γ LT is wide (À49 ± 40 GtC/K) and thus remains one of the key uncertainties in climate projections. A tight correlation is found between the long-term sensitivity of tropical land carbon and the short-term sensitivity of atmospheric CO 2 (γ LT versus γ IAV ), which enables the projections to be constrained with observations. The observed short-term sensitivity of CO 2 (À4.4 ± 0.9 GtC/yr/K) sharpens the range of γ LT to À44 ± 14 GtC/K, which overlaps with the probability density function derived from the C 4 MIP models (À53 ± 17 GtC/K) by Cox et al. (2013), even though the lines relating γ LT and γ IAV differ in the two cases. Emergent constraints of this type provide a means to focus ESM evaluation against observations on the metrics most relevant to projections of future climate change.
Abstract.A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology-climate interactions, as well as atmospheric CO 2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the stateof-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user.
Abstract. A community diagnostics and performance metrics tool for the evaluation of Earth System Models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected Essential Climate Variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases and soil hydrology-climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the CMIP ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within the Coupled Model Intercomparison Project (CMIP) and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user.
Stratospheric ozone recovery and increasing greenhouse gases are anticipated to have a large impact on the Southern Hemisphere extratropical circulation, shifting the jet stream and associated storm tracks. Models participating in phase 5 of the Coupled Model Intercomparison Project poorly simulate the austral jet, with a mean equatorward bias and 10° latitude spread in their historical climatologies, and project a wide range of future trends in response to anthropogenic forcing in the representative concentration pathway (RCP) scenarios. Here, the question is addressed whether the unweighted multimodel mean (uMMM) austral jet projection of the RCP4.5 scenario can be improved by applying a process-oriented multiple diagnostic ensemble regression (MDER). MDER links future projections of the jet position to processes relevant to its simulation under present-day conditions. MDER is first targeted to constrain near-term (2015–34) projections of the austral jet position and selects the historical jet position as the most important of 20 diagnostics. The method essentially recognizes the equatorward bias in the past jet position and provides a bias correction of about 1.5° latitude southward to future projections. When the target horizon is extended to midcentury (2040–59), the method also recognizes that lower-stratospheric temperature trends over Antarctica, a proxy for the intensity of ozone depletion, provide additional information that can be used to reduce uncertainty in the ensemble mean projection. MDER does not substantially alter the uMMM long-term position in jet position but reduces the uncertainty in the ensemble mean projection. This result suggests that accurate observational constraints on upper-tropospheric and lower-stratospheric temperature trends are needed to constrain projections of the austral jet position.
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