The impact of structural error on parameter constraint in a climate model

Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections of future climate change. We use observations of forest fraction to constrain carbon cycle and land surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error. Using an ensemble of climate model runs to build a computationally cheap statistical proxy (emulator) of the climate model, we use history matching to rule out input parameter settings where the corresponding climate model output is judged sufficiently different from observations, even allowing for uncertainty. Regions of parameter space where FAMOUS best simulates the Amazon forest fraction are incompatible with the regions where FAMOUS best simulates other forests, indicating a structural error in the model. We use the emulator to simulate the forest fraction at the best set of parameters implied by matching the model to the Amazon, Central African, South East Asian, and North American forests in turn. We can find parameters that lead to a realistic forest fraction in the Amazon, but that using the Amazon alone to tune the simulator would result in a significant overestimate of forest fraction in the other forests. Conversely, using the other forests to tune the simulator leads to a larger underestimate of the Amazon forest fraction. We use sensitivity analysis to find the parameters which have the most impact on simulator output and perform a history-matching exercise using credible estimates for simulator discrepancy and observational uncertainty terms. We are unable to constrain the parameters individually, but we rule out just under half of joint parameter space as being incompatible with forest observations. We discuss the possible sources of the discrepancy in the simulated Amazon, including missing processes in the land surface component and a bias in the climatology of the Amazon.This work was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). Doug McNeall was supported on secondment to Exeter University by the Met Office Academic Partnership (MOAP) for part of the work. Jonny Williams was supported by funding from Statoil ASA, Norwa

Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models

We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission-driven rather than concentration-driven perturbed parameter ensemble of a global climate model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration-driven simulations (with 10–90th percentile ranges of 1.7 K for the aggressive mitigation scenario, up to 3.9 K for the high-end, business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9 K (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission-driven experiments, they do not change existing expectations (based on previous concentration-driven experiments) on the timescales over which different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in the case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration scenarios used to drive GCM ensembles, lies towards the lower end of our simulated distribution. This design decision (a legacy of previous assessments) is likely to lead concentration-driven experiments to under-sample strong feedback responses in future projections. Our ensemble of emission-driven simulations span the global temperature response of the CMIP5 emission-driven simulations, except at the low end. Combinations of low climate sensitivity and low carbon cycle feedbacks lead to a number of CMIP5 responses to lie below our ensemble range. The ensemble simulates a number of high-end responses which lie above the CMIP5 carbon cycle range. These high-end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real-world climate-sensitivity constraints which, if achieved, would lead to reductions on the upper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present-day observables and future changes, while the large spread of future-projected changes highlights the ongoing need for such work

Correcting a bias in a climate model with an augmented emulator

This is the final version. Available from Copernicus Publications via the DOI in this record. A key challenge in developing flagship climate model configurations is the process of setting uncertain input parameters at values that lead to credible climate simulations. Setting these parameters traditionally relies heavily on insights from those involved in parameterisation of the underlying climate processes. Given the many degrees of freedom and computational expense involved in evaluating such a selection, this can be imperfect leaving open questions about whether any subsequent simulated biases result from mis-set parameters or wider structural model errors (such as missing or partially parameterised processes). Here, we present a complementary approach to identifying plausible climate model parameters, with a method of bias correcting subcomponents of a climate model using a Gaussian process emulator that allows credible values of model input parameters to be found even in the presence of a significant model bias. A previous study (McNeall et al., 2016) found that a climate model had to be run using land surface input parameter values from very different, almost non-overlapping, parts of parameter space to satisfactorily simulate the Amazon and other forests respectively. As the forest fraction of modelled non-Amazon forests was broadly correct at the default parameter settings and the Amazon too low, that study suggested that the problem most likely lay in the model's treatment of non-plant processes in the Amazon region. This might be due to modelling errors such as missing deep rooting in the Amazon in the land surface component of the climate model, to a warm-dry bias in the Amazon climate of the model or a combination of both. In this study, we bias correct the climate of the Amazon in the climate model from McNeall et al. (2016) using an "augmented" Gaussian process emulator, where temperature and precipitation, variables usually regarded as model outputs, are treated as model inputs alongside land surface input parameters. A sensitivity analysis finds that the forest fraction is nearly as sensitive to climate variables as it is to changes in its land surface parameter values. Bias correcting the climate in the Amazon region using the emulator corrects the forest fraction to tolerable levels in the Amazon at many candidates for land surface input parameter values, including the default ones, and increases the valid input space shared with the other forests. We need not invoke a structural model error in the land surface model, beyond having too dry and hot a climate in the Amazon region. The augmented emulator allows bias correction of an ensemble of climate model runs and reduces the risk of choosing poor parameter values because of an error in a subcomponent of the model. We discuss the potential of the augmented emulator to act as a translational layer between model subcomponents, simplifying the process of model tuning when there are compensating errors and helping model developers discover and prioritise model errors to target.Alan Turing Institut

Predicting the targeting of tail-anchored proteins to subcellular compartments in mammalian cells

This is the author accepted manuscript. The final version is available from Company of Biologists via the DOI in this record.Tail-anchored (TA) proteins contain a single transmembrane domain (TMD) at the Cterminus, anchoring them to organelle membranes where they mediate a variety of critical cellular processes. Mutations in individual TA proteins cause a number of severe inherited disorders. However, the molecular mechanisms and signals facilitating proper TA protein targeting are not fully understood, in particular in mammals. Here, we identify additional TA proteins at peroxisomes or shared by multiple organelles in mammals and reveal that a combination of TMD hydrophobicity and tail charge determines targeting to distinct organelles. Specifically, an increase in tail charge can override a hydrophobic TMD signal and re-direct a protein from the ER to peroxisomes or mitochondria and vice versa. We demonstrate that subtle alterations in those physicochemical parameters can shift TA protein targeting between organelles, explaining why peroxisomes and mitochondria share many TA proteins. Our analyses enabled us to allocate characteristic physicochemical parameters to different organelle groups. This classification allows for the first time, successful prediction of the location of uncharacterized TA proteins.We thank colleagues who provided materials (see Tables S1-S4) and acknowledge support from A. C. Magalhães, M. Almeida, D. Tuerker, S. Kuehl and C. Davies. This work was supported by the Biotechnology and Biological Sciences Research Council (BB/K006231/1 to M.S.), a Wellcome Trust Institutional Strategic Support Award (WT097835MF, WT105618MA to M.S.), the Portuguese Foundation for Science and Technology and FEDER/COMPETE (PTDC/BIA-BCM/118605/2010 to M.S.; SFRH/BD/37647/2007 to N.B.; SFRH/BPD/77619/2011 and UID/BIM/04501/2013 to D.R.). M.W., E.A.G., and M.S. are supported by Marie Curie Initial Training Network (ITN) action PerFuMe (316723)

Continuous Structural Parameterization: A Proposed Method for Representing Different Model Parameterizations Within One Structure Demonstrated for Atmospheric Convection

Continuous structural parameterization (CSP) is a proposed method for approximating different numerical model parameterizations of the same process as functions of the same grid‐scale variables. This allows systematic comparison of parameterizations with each other and observations or resolved simulations of the same process. Using the example of two convection schemes running in the Met Office Unified Model (UM), we show that a CSP is able to capture concisely the broad behavior of the two schemes, and differences between the parameterizations and resolved convection simulated by a high resolution simulation. When the original convection schemes are replaced with their CSP emulators within the UM, basic features of the original model climate and some features of climate change are reproduced, demonstrating that CSP can capture much of the important behavior of the schemes. Our results open the possibility that future work will estimate uncertainty in model projections of climate change from estimates of uncertainty in simulation of the relevant physical processes. Plain Language Summary Numerical models are used to provide estimates of future weather and climate change. The models contain “parameterizations,” which are algorithms that simulate the effect of processes too small or poorly understood to represent using physical equations. Although they are based as much as possible on physics, parameterizations are a large source of modeling uncertainty because there can be large disagreements on how to best represent a given process. The method and even the variables used by two different parameterizations may differ. It is therefore very difficult to know how different parameterizations cause numerical models to produce different results and whether the parameterizations we have are adequate and span the range of uncertainty concerning our knowledge of the processes they represent. Using the example of small‐scale atmospheric convection linked to rain and thunderstorms, this paper describes a mathematical method for expressing different parameterizations within the same framework. This allows easy but formal mathematical comparison of different parameterizations and gives future work the potential to understand whether our parameterizations perform as they should in conjunction with future observations

Internal variability of Earth’s energy budget simulated by CMIP5 climate models

We analyse a large number of multi-century pre-industrial control simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) to investigate relationships between: net top-of-atmosphere radiation (TOA), globally averaged surface temperature (GST), and globally integrated ocean heat content (OHC) on decadal timescales. Consistent with previous studies, we find that large trends (∼0.3 K dec ^−1 ) in GST can arise from internal climate variability and that these trends are generally an unreliable indicator of TOA over the same period. In contrast, trends in total OHC explain 95% or more of the variance in TOA for two-thirds of the models analysed; emphasizing the oceans’ role as Earth’s primary energy store. Correlation of trends in total system energy (TE ≡ time integrated TOA) against trends in OHC suggests that for most models the ocean becomes the dominant term in the planetary energy budget on a timescale of about 12 months. In the context of the recent pause in global surface temperature rise, we investigate the potential importance of internal climate variability in both TOA and ocean heat rearrangement. The model simulations suggest that both factors can account for O (0.1 W m ^−2 ) on decadal timescales and may play an important role in the recently observed trends in GST and 0–700 m (and 0–1800 m) ocean heat uptake

Quantifying the likelihood of a continued hiatus in global warming

Since the end of the twentieth century, global mean surface temperature has not risen as rapidly as predicted by global climate models (GCMs). This discrepancy has become known as the global warming a hiatus'and a variety of mechanisms have been proposed to explain the observed slowdown in warming. Focusing on internally generated variability, we use pre-industrial control simulations from an observationally constrained ensemble of GCMs and a statistical approach to evaluate the expected frequency and characteristics of variability-driven hiatus periods and their likelihood of future continuation. Given an expected forced warming trend of â 1/40.2 K per decade, our constrained ensemble of GCMs implies that the probability of a variability-driven 10-year hiatus is â 1/410%, but less than 1% for a 20-year hiatus. Although the absolute probability of a 20-year hiatus is small, the probability that an existing 15-year hiatus will continue another five years is much higher (up to 25%). Therefore, given the recognized contribution of internal climate variability to the reduced rate of global warming during the past 15 years, we should not be surprised if the current hiatus continues until the end of the decade. Following the termination of a variability-driven hiatus, we also show that there is an increased likelihood of accelerated global warming associated with release of heat from the sub-surface ocean and a reversal of the phase of decadal variability in the Pacific Ocean.Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101)Natural Environment Research Council DEEP-C project NE/K005480/

The mechanisms of North Atlantic CO₂ uptake in a large Earth System Model ensemble

The oceans currently take up around a quarter of the carbon dioxide (CO2) emitted by human activity. While stored in the ocean, this CO2 is not influencing Earth's radiation budget; the ocean CO2 sink therefore plays an important role in mitigating global warming. CO2 uptake by the oceans is heterogeneous, with the subpolar North Atlantic being the strongest CO2 sink region. Observations over the last 2 decades have indicated that CO2 uptake by the subpolar North Atlantic sink can vary rapidly. Given the importance of this sink and its apparent variability, it is critical that we understand the mechanisms behind its operation. Here we explore the combined natural and anthropogenic subpolar North Atlantic CO2 uptake across a large ensemble of Earth System Model simulations, and find that models show a peak in sink strength around the middle of the century after which CO2 uptake begins to decline. We identify different drivers of change on interannual and multidecadal timescales. Short-term variability appears to be driven by fluctuations in regional seawater temperature and alkalinity, whereas the longer-term evolution throughout the coming century is largely occurring through a counterintuitive response to rising atmospheric CO2 concentrations. At high atmospheric CO2 concentrations the contrasting Revelle factors between the low latitude water and the subpolar gyre, combined with the transport of surface waters from the low latitudes to the subpolar gyre, means that the subpolar CO2 uptake capacity is largely satisfied from its southern boundary rather than through air–sea CO2 flux. Our findings indicate that: (i) we can explain the mechanisms of subpolar North Atlantic CO2 uptake variability across a broad range of Earth System Models; (ii) a focus on understanding the mechanisms behind contemporary variability may not directly tell us about how the sink will change in the future; (iii) to identify long-term change in the North Atlantic CO2 sink we should focus observational resources on monitoring lower latitude as well as the subpolar seawater CO2; (iv) recent observations of a weakening subpolar North Atlantic CO2 sink may suggest that the sink strength has peaked and is in long-term decline