Climate-carbon cycle uncertainties and the Paris Agreement

The Paris Agreement aims to address the gap between existing climate policies and policies consistent with ‘holding the increase in global average temperature to well below 2C’. The feasibility of meeting the target has been questioned both in terms of the possible requirement for negative emissions, and ongoing debate on the sensitivity of the climate-carbon cycle system. Using a sequence of ensembles of a fully dynamic three-dimensional climate-carbon cycle model, forced by emissions from an integrated assessment model of regional-level climate policy, economy, and technological transformation, we show that a reasonable interpretation of the Paris Agreement is still technically achievable. Specifically, limiting peak (decadal) warming to less than 1.7°C, or end-century warming to less than 1.54°C, occurs in 50% of our simulations in a policy scenario without net negative emissions or excessive stringency in any policy domain. We evaluate two mitigation scenarios, with 200 GTC and 307 GTC post-2017 emissions, quantifying spatio-temporal variability of warming, precipitation, ocean acidification and marine productivity. Under rapid decarbonisation decadal variability dominates the mean response in critical regions, with significant implications for decision making, demanding impact methodologies that address non-linear spatio-temporal responses. Ignoring carbon-cycle feedback uncertainties (explaining 47% of peak warming uncertainty) becomes unreasonable under strong mitigation conditions.We acknowledge C-EERNG and Cambridge Econometrics for support, and funding from EPSRC (to J.-F.M., fellowship number EP/ K007254/1); the Newton Fund (to J.-F.M., P.S. and J.E.V., EPSRC grant number EP/N002504/1 and ESRC grant number ES/N013174/1), NERC (to N.R.E., P.H. and H.P., grant number NE/P015093/1), CONICYT (to P.S.), the Philomathia Foundation (to J.E.V.) and Horizon 2020 (to H.E.P. and J.-F.M., the Sim4Nexus project)

Information Markets and Nonmarkets

As large amounts of data become available and can be communicated more easily and processed more e¤ectively, information has come to play a central role for economic activity and welfare in our age. This essay overviews contributions to the industrial organization of information markets and nonmarkets, while attempting to maintain a balance between foundational frameworks and more recent developments. We start by reviewing mechanism-design approaches to modeling the trade of information. We then cover ratings, predictions, and recommender systems. We turn to forecasting contests, prediction markets, and other institutions designed for collecting and aggregating information from decentralized participants. Finally, we discuss science as a prototypical information nonmarket with participants who interact in a non-anonymous way to produce and disseminate information. We aim to make the reader familiar with the central notions and insights in this burgeoning literature and also point to some open critical questions that future research will have to address

PLASIM-ENTSem v1.0: a spatio-temporal emulator of future climate change for impacts assessment

Many applications in the evaluation of climate impacts and environmental policy require detailed spatio-temporal projections of future climate. To capture feedbacks from impacted natural or socio-economic systems requires interactive two-way coupling, but this is generally computationally infeasible with even moderately complex general circulation models (GCMs). Dimension reduction using emulation is one solution to this problem, demonstrated here with the GCM PLASIM-ENTS (Planet Simulator coupled with the efficient numerical terrestrial scheme). Our approach generates temporally evolving spatial patterns of climate variables, considering multiple modes of variability in order to capture non-linear feedbacks. The emulator provides a 188-member ensemble of decadally and spatially resolved (~ 5◦ resolution) seasonal climate data in response to an arbitrary future CO2 concentration and non-CO2 radiative forcing scenario. We present the PLASIM-ENTS coupled model, the construction of its emulator from an ensemble of transient future simulations, an application of the emulator methodology to produce heating and cooling degree-day projections, the validation of the simulator (with respect to empirical data) and the validation of the emulator (with respect to high-complexity models). We also demonstrate the application to estimates of sea-level rise and associated uncertainty

Probabilistic Lane Association

Lane association is the problem of determining in which lane a vehicle is currently driving, which is of interest for automated driving where the vehicle must understand its surroundings. Limited to highway scenarios, a method combining data from different sensors to extract information about the currently associated lane is presented. The suggested method splits the problem in two main parts, lane change identification and road edge detection. The lane change identification mainly uses information from the camera to model the lateral movement on the road and identifies the lane changes as a relative position on the road. This part is implemented with a particle filter. The road edge detection enters radar detections to an iterated Kalman filter and estimates the distances to the road edges. Finally, a combination of the filter outputs makes it possible to compute an absolute position on the road. Comparing the relative and absolute positioning then leads to the desired lane association estimate. The results produced are reliable and encourages to continue approaching this problem in a similar manner, but the current implementation is computationally heavy