Forests and Emissions: A contribution to the Eliasch Review

This report discusses the impacts of deforestation and reforestation on carbon emissions and carbon storage, and how change in land cover will affect future trends in climate change and carbon levels

Uncertainties in the timing of unprecedented climates

The question of when the signal of climate change will emerge from the background noise of climate variability—the ‘time of emergence’—is potentially important for adaptation planning. Mora et al.1 presented precise projections of the time of emergence of unprecedented regional climates. However, their methodology produces artificially early dates at which specific regions will permanently experience unprecedented climates and artificially low uncertainty in those dates everywhere. This overconfidence could impair the effectiveness of climate risk management decisions 2. There is a Reply to this Brief Communication Arising by Mora, C. et al. Nature 511, http://dx.doi.org/10.1038/nature13524 (2014)

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

Tail-anchored (TA) proteins contain a single transmembrane domain (TMD) at the C-terminus that anchors them to the membranes of organelles where they mediate critical cellular processes. Accordingly, mutations in genes encoding TA proteins have been identified in a number of severe inherited disorders. Despite the importance of correctly targeting a TA protein to its appropriate membrane, the mechanisms and signals involved are not fully understood. In this study, we identify additional peroxisomal TA proteins, discover more proteins that are present on multiple organelles, and reveal that a combination of TMD hydrophobicity and tail charge determines targeting to distinct organelle locations in mammals. 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 show that subtle changes in those parameters can shift TA proteins between organelles, explaining why peroxisomes and mitochondria have many of the same TA proteins. This enabled us to associate characteristic physicochemical parameters in TA proteins with particular organelle groups. Using this classification allowed successful prediction of the location of uncharacterized TA proteins for the first time

Forest fraction in an ensemble of the climate model FAMOUS

<div>Data for “The impact of structural error on parameter constraint in a climate model” by Doug McNeall, Jonny Williams, Ben Booth, Richard Betts, Peter Challenor, Andy Wiltshire, and David Sexton. Published in Earth System Dynamics, 2016</div><div>contact [email protected]</div><div><br></div><div>The R data file “famous_forest_fraction.RData” contains a number of R objects:</div><div><br></div><div>full_frac         Data frame containing 100 ensemble members of FAMOUS. Unique model run ID, input parameters and aggregated forest fraction</div><div>bl.frac.ens       Global broadleaf tree grid box forest fraction, rows match the rows of full_frac, columns are lats x longs of the model grid (by lats)</div><div>nl.frac.ens       Global needleleaf tree grid box forest fraction, rows match the rows of full_frac, columns are lats x longs of the model grid (by lats)</div><div>lats              Latitudes for FAMOUS model grid</div><div>longs             Longitudes for FAMOUS model grid</div><div>obs               Aggregated “Observations” of forest fraction</div><div>X.standard        Standard or default parameters for FAMOUS land surface</div><div><br></div><div><br></div><div>Useful functions:</div><div><br></div><div>remap.famous = function(dat,longs,lats, shift = FALSE){</div><div>  # reshape a map in vector form so that fields() package function image.plot() </div><div>  #  (for example) will plot it correctly</div><div>  mat = matrix(dat, nrow=length(longs), ncol=length(lats))[ ,length(lats):1]</div><div>  if(shift){</div><div>    block1.ix = which(longs < shift)</div><div>    block2.ix = which(longs > shift)</div><div>    mat.shift = rbind(mat[ block2.ix, ], mat[block1.ix, ]) </div><div>    out = mat.shift</div><div>  }</div><div>  else{</div><div>    out = mat</div><div>  }</div><div>  out</div><div>}</div

The potential of an observational data set for calibration of a computationally expensive computer model

We measure the potential of an observational data set to constrain a set of inputs to a complex and computationally expensive computer model. We use each member in turn of an ensemble of output from a computationally expensive model, corresponding to an observable part of a modelled system, as a proxy for an observational data set. We argue that, given some assumptions, our ability to constrain uncertain parameter inputs to a model using its own output as data, provides a maximum bound for our ability to constrain the model inputs using observations of the real system. The ensemble provides a set of known parameter input and model output pairs, which we use to build a computationally efficient statistical proxy for the full computer model, termed an emulator. We use the emulator to find and rule out "implausible" values for the inputs of held-out ensemble members, given the computer model output. As we know the true values of the inputs for the ensemble, we can compare our constraint of the model inputs with the true value of the input for any ensemble member. Measures of the quality of constraint have the potential to inform strategy for data collection campaigns, before any real-world data is collected, as well as acting as an effective sensitivity analysis. We use an ensemble of the ice sheet model Glimmer to demonstrate our measures of quality of constraint. The ensemble has 250 model runs with 5 uncertain input parameters, and an output variable representing the pattern of the thickness of ice over Greenland. We have an observation of historical ice sheet thickness that directly matches the output variable, and offers an opportunity to constrain the model. We show that different ways of summarising our output variable (ice volume, ice surface area and maximum ice thickness) offer different potential constraints on individual input parameters. We show that combining the observational data gives increased power to constrain the model. We investigate the impact of uncertainty in observations or in model biases on our measures, showing that even a modest uncertainty can seriously degrade the potential of the observational data to constrain the model

Validation of River Flows in HadGEM1 and HadCM3 with the TRIP River Flow Model

The Total Runoff Integrating Pathways (TRIP) global river-routing scheme in the third climate configuration of the Met Office Unified Model (HadCM3) and the newer Hadley Centre Global Environmental Model version 1 (HadGEM1) general circulation models (GCMs) have been validated against long-term average measured river discharge data from 40 stations on 24 major river basins from the Global Runoff Data Centre (GRDC). TRIP was driven by runoff produced directly by the two GCMs in order to assess both the skill of river flows produced within GCMs in general and to test this as a method for validating large-scale hydrology in GCMs. TRIP predictions of long-term-averaged annual discharge were improved at 28 out of 40 gauging stations on 24 of the world's major rivers in HadGEM1 compared to HadCM3, particularly for lowand high-latitude basins, with predictions ranging from "good" (within 20% of observed values) to "poor" (biases exceeding 50%). For most regions, the modeled annual average river flows tended to be exaggerated in both models, largely reflecting inflated estimates of precipitation, although lack of human interventions in this modeling setup may have been an additional source of error. Within individual river basins, there were no clear trends in the accuracy of HadGEM1 versus HadCM3 predictions at up- or downstream gauging stations. Relative root-mean-square error (RRMSE) scores for the annual cycle of river flow ranged from poor (.50%) to "fair" (20%-50%) with an overall range of 20.7%-1023.5%, comparable to that found in similar global-scale studies. In both models, simulations of the annual cycle of river flow were generally better for high-latitude basins than in low or midlatitudes. There was a relatively small improvement in the annual cycle of river flow in HadGEM1 compared to HadCM3, mostly in the low-latitude rivers. The findings suggest that there is still substantial work to be done to enable GCMs to simulate monthly discharge consistently well over the majority of basins, including improvements to both (i) GCM simulation of basin-scale precipitation and evaporation and (ii) hydrological processes (e.g., representation of dry land hydrology, floodplain inundation, lakes, snowmelt, and human intervention)

Reconciling Observation and Model Trends in North Atlantic Surface CO2

The North Atlantic Ocean is a region of intense uptake of atmospheric CO2. To assess how this CO2 sink has evolved over recent decades, various approaches have been used to estimate basin-wide uptake from the irregularly sampled in situ CO2 observations. Until now, the lack of robust uncertainties associated with observation-based gap-filling methods required to produce these estimates has limited the capacity to validate climate model simulated surface ocean CO2 concentrations. After robustly quantifying basin-wide and annually varying interpolation uncertainties using both observational and model data, we show that the North Atlantic surface ocean fugacity of CO2 (fCO(2-ocean)) increased at a significantly slower rate than that simulated by the latest generation of Earth System Models during the period 1992-2014. We further show, with initialized model simulations, that the inability of these models to capture the observed trend in surface fCO(2-ocean) is primarily due to biases in the models' ocean biogeochemistry. Our results imply that current projections may underestimate the contribution of the North Atlantic to mitigating increasing future atmospheric CO2 concentrations