Determination of the Earth's pole tide Love number k₂ from observations of polar motion using an adaptive Kalman filter approach

The geophysical interpretation of observed time series of Earth rotation parameters (ERP) is commonly based on numerical models that describe and balance variations of angular momentum in various subsystems of the Earth. Naturally, models are dependent on geometrical, rheological and physical parameters. Many of these are weakly determined from other models or observations. In our study we present an adaptive Kalman filter approach for the improvement of parameters of the dynamic Earth system model DyMEG which acts as a simulator of ERP. In particular we focus on the improvement of the pole tide Love number k(2). In the frame of a sensitivity analysis k(2) has been identified as one of the most crucial parameters of DyMEG since it directly influences the modeled Chandler oscillation. At the same time k(2) is one of the most uncertain parameters in the model. Our simulations with DyMEG cover a period of 60 years after which a steady state of k(2) is reached. The estimate for k(2), accounting for the anelastic response of the Earth's mantle and the ocean, is 0.3531 + 0.0030i. We demonstrate that the application of the improved parameter k(2) in DyMEG leads to significantly better results for polar motion than the original value taken from the Conventions of the International Earth Rotation and Reference Systems Service (IERS)

Operationalizing climate targets under learning: An application of cost-risk analysis

Cost-Effectiveness Analysis (CEA) determines climate policies that reach a given climate target at minimum welfare losses. However, when applied to temperature targets under climate sensitivity uncertainty, decision-makers might be confronted with normatively unappealing negative expected values of future climate information or even infeasible solutions. To tackle these issues, Cost-Risk Analysis (CRA), that trades-off the costs for mitigating climate change against the risk of exceeding climate targets, has been proposed as an extension of CEA under uncertainty. Here we build on this proposition and develop an axiomatically sound CRA for the context of uncertainty and future learning. The main contributions of this paper are: (i) we show, that a risk-penalty function has to be non-concave to avoid counter-intuitive preferences, (ii) we introduce a universally applicable calibration of the cost-risk trade-off, and (iii) we implement the first application of CRA to a numerical integrated assessment model. We find that for a 2°-target in combination with a 66 % compliance level, the expected value of information in 2015 vs. 2075 is between 0.15 % and 0.66 % of consumption every year, and can reduce expected mitigation costs by about one third. (iv) Finally, we find that the relative importance of the economic over the risk-related contribution increases with the target probability of compliance

Moving targets—cost-effective climate policy under scientific uncertainty

The IPCC’s fifth assessment report of Working Group III has just come out. It pays special attention to the 2 °C temperature target and tells us that the window of opportunity to prevent such climate change is rapidly closing. Yet, the report also presents a portfolio of stabilization targets, reflecting a fundamental ambiguity: there is no unique “dangerous” climate threshold. Here, we describe a framework for the evaluation of optimal climate policy given an uncertain formal climate threshold. We find that uncertainty leads to moving targets: even when the available information does not change, future regulators will tend to relax current climate plans. We develop a reduced form integrated assessment model to assess the quantitative significance of our findings. We calibrate preferences such that in 2000 a stabilization target of 450 ppmv maintains the optimal balance between climate risks and abatement costs. The naïve equilibrium ultimately reaches a peak of 570 ppmv, missing the 2000 stabilizations targets by a wide margin. Our results offer an explanation for the inertia in mitigation efforts over the past decades: policies often delay the majority of abatement efforts. Yet, believing that subsequent regulators will uphold the planned future efforts is self-defeating

Determination of the Earth's pole tide Love number k

The geophysical interpretation of observed time series of Earth rotation parameters (ERP) is commonly based on numerical models that describe and balance variations of angular momentum in various subsystems of the Earth. Naturally, models are dependent on geometrical, rheological and physical parameters. Many of these are weakly determined from other models or observations. In our study we present an adaptive Kalman filter approach for the improvement of parameters of the dynamic Earth system model DyMEG which acts as a simulator of ERP. In particular we focus on the improvement of the pole tide Love number k(2). In the frame of a sensitivity analysis k(2) has been identified as one of the most crucial parameters of DyMEG since it directly influences the modeled Chandler oscillation. At the same time k(2) is one of the most uncertain parameters in the model. Our simulations with DyMEG cover a period of 60 years after which a steady state of k(2) is reached. The estimate for k(2), accounting for the anelastic response of the Earth's mantle and the ocean, is 0.3531 + 0.0030i. We demonstrate that the application of the improved parameter k(2) in DyMEG leads to significantly better results for polar motion than the original value taken from the Conventions of the International Earth Rotation and Reference Systems Service (IERS)

Predicting future uncertainty constraints on global warming projections

Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by “current knowledge” of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change