INFERNO: a fire and emissions scheme for the UK Met Office's Unified Model

Warm and dry climatological conditions favour the occurrence of forest fires. These fires then become a significant emission source to the atmosphere. Despite this global importance, fires are a local phenomenon and are difficult to represent in a large-scale Earth System Model (ESM). To address this, the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO) was developed. INFERNO follows a reduced complexity approach and is intended for decadal to centennial scale climate simulations and assessment models for policy making. Fuel flammability is simulated using temperature, relative humidity, fuel density as well as precipitation and soil moisture. Combining flammability with ignitions and vegetation, burnt area is diagnosed. Emissions of carbon and key species are estimated using the carbon scheme in the JULES land surface model. JULES also possesses fire index diagnostics which we document and compare with our fire scheme. Two meteorology datasets and three ignition modes are used to validate the model. INFERNO is shown to effectively diagnose global fire occurrence (R = 0.66) and emissions (R = 0.59) through an approach appropriate to the complexity of an ESM, although regional biases remain

Conversion from forests to pastures in the Colombian Amazon leads to differences in dead wood dynamics depending on land management practices

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordDead wood, composed of coarse standing and fallen woody debris (CWD), is an important carbon (C) pool in tropical forests and its accounting is needed to reduce uncertainties within the strategies to mitigate climate change by reducing deforestation and forest degradation (REDD+). To date, information on CWD stocks in tropical forests is scarce and effects of land-cover conversion and land management practices on CWD dynamics remain largely unexplored. Here we present estimates on CWD stocks in primary forests in the Colombian Amazon and their dynamics along 20 years of forest-to-pasture conversion in two sub-regions with different management practices during pasture establishment: high-grazing intensity (HG) and low-grazing intensity (LG) sub-regions. Two 20-year-old chronosequences describing the forest-to-pasture conversion were identified in both sub-regions. The line-intersect and the plot-based methods were used to estimate fallen and standing CWD stocks, respectively. Total necromass in primary forests was similar between both sub-regions (35.6 ± 5.8 Mg ha(-1) in HG and 37.0 ± 7.4 Mg ha(-1) in LG). An increase of ∼124% in CWD stocks followed by a reduction to values close to those at the intact forests were registered after slash-and-burn practice was implemented in both sub-regions during the first two years of forest-to-pasture conversion. Implementation of machinery after using fire in HG pastures led to a reduction of 82% in CWD stocks during the second and fifth years of pasture establishment, compared to a decrease of 41% during the same period in LG where mechanization is not implemented. Finally, average necromass 20 years after forest-to-pasture conversion decreased to 3.5 ± 1.4 Mg ha(-1) in HG and 9.3 ± 3.5 Mg ha(-1) in LG, representing a total reduction of between 90% and 75% in each sub-region, respectively. These results highlight the importance of low-grazing intensity management practices during ranching activities in the Colombian Amazon to reduce C emissions associated with land-cover change from forest to pasture.This study was funded by AXA Research Fund (2012-Doc-University-of-Exeter-NAVARRETE-D)

The carbon cycle in Mexico: past, present and future of C stocks and fluxes

PublishedThe Supplement related to this article is available online at doi:10.5194/bg-13-223-2016-supplement.We modeled the carbon (C) cycle in Mexico with a process-based approach. We used different available products (satellite data, field measurements, models and flux towers) to estimate C stocks and fluxes in the country at three different time frames: present (defined as the period 2000–2005), the past century (1901–2000) and the remainder of this century (2010–2100). Our estimate of the gross primary productivity (GPP) for the country was 2137 ± 1023 TgC yr−1 and a total C stock of 34 506 ± 7483 TgC, with 20 347 ± 4622 TgC in vegetation and 14 159 ± 3861 in the soil. Contrary to other current estimates for recent decades, our results showed that Mexico was a C sink over the period 1990–2009 (+31 TgC yr−1) and that C accumulation over the last century amounted to 1210 ± 1040 TgC. We attributed this sink to the CO2 fertilization effect on GPP, which led to an increase of 3408 ± 1060 TgC, while both climate and land use reduced the country C stocks by −458 ± 1001 and −1740 ± 878 TgC, respectively. Under different future scenarios, the C sink will likely continue over the 21st century, with decreasing C uptake as the climate forcing becomes more extreme. Our work provides valuable insights on relevant driving processes of the C cycle such as the role of drought in drylands (e.g., grasslands and shrublands) and the impact of climate change on the mean residence time of soil C in tropical ecosystems.The lead author (G. Murray-Tortarolo) thanks CONACYT-CECTI, the University of Exeter and Secretaría de Educación Pública (SEP) for their funding of this project. The authors extend their thanks to Carlos Ortiz Solorio and to the Colegio de Posgraduados for the field soil data and to the Alianza Redd+ Mexico for the field biomass data. This project would not have been possible without the valuable data from the CMIP5 models. A. Arneth, G. Murray-Tortarolo, A. Wiltshire and S. Sitch acknowledge the support of the European Commission-funded project LULCC4C (grant no. 603542). A. Wiltshire was partsupported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101)

Accuracy of Vitalograph lung monitor as a screening test for COPD in primary care.

Microspirometry may be useful as the second stage of a screening pathway among patients reporting respiratory symptoms. We assessed sensitivity and specificity of the Vitalograph® lung monitor compared with post-bronchodilator confirmatory spirometry (ndd Easy on-PC) among primary care chronic obstructive pulmonary disease (COPD) patients within the Birmingham COPD cohort. We report a case-control analysis within 71 general practices in the UK. Eligible patients were aged ≥40 years who were either on a clinical COPD register or reported chronic respiratory symptoms on a questionnaire. Participants performed pre- and post-bronchodilator microspirometry, prior to confirmatory spirometry. Out of the 544 participants, COPD was confirmed in 337 according to post-bronchodilator confirmatory spirometry. Pre-bronchodilator, using the LLN as a cut-point, the lung monitor had a sensitivity of 50.5% (95% CI 45.0%, 55.9%) and a specificity of 99.0% (95% CI 96.6%, 99.9%) in our sample. Using a fixed ratio of FEV1/FEV6 < 0.7 to define obstruction in the lung monitor, sensitivity increased (58.8%; 95% CI 53.0, 63.8) while specificity was virtually identical (98.6%; 95% CI 95.8, 99.7). Within our sample, the optimal cut-point for the lung monitor was FEV1/FEV6 < 0.78, with sensitivity of 82.8% (95% CI 78.3%, 86.7%) and specificity of 85.0% (95% CI 79.4%, 89.6%). Test performance of the lung monitor was unaffected by bronchodilation. The lung monitor could be used in primary care without a bronchodilator using a simple ratio of FEV1/FEV6 as part of a screening pathway for COPD among patients reporting respiratory symptoms

Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude northern hemisphere. Part II: Earth system models

PublishedJournal ArticleLeaf Area Index (LAI) is a key parameter in the Earth System Models (ESMs) since it strongly affects land-surface boundary conditions and the exchange of matter and energy with the atmosphere. Observations and data products derived from satellite remote sensing are important for the validation and evaluation of ESMs from regional to global scales. Several decades' worth of satellite data products are now available at global scale which represents a unique opportunity to contrast observations against model results. The objective of this study is to assess whether ESMs correctly reproduce the spatial variability of LAI when compared with satellite data and to compare the length of the growing season in the different models with the satellite data. To achieve this goal we analyse outputs from 11 coupled carbon-climate models that are based on the set of new global model simulations planned in support of the IPCC Fifth Assessment Report. We focus on the average LAI and the length of the growing season on Northern Hemisphere over the period 1986-2005. Additionally we compare the results with previous analyses (Part I) of uncoupled land surface models (LSMs) to assess the relative contribution of vegetation and climatic drivers on the correct representation of LAI. Our results show that models tend to overestimate the average values of LAI and have a longer growing season due to the later dormancy. The similarities with the uncoupled models suggest that representing the correct vegetation fraction with the associated parameterizations; is more important in controlling the distribution and value of LAI than the climatic variables. © 2013 by the authors.This work was funded by the European Commission’s 7th Framework Programme under Grant Agreements number 238366 (GREENCYCLESII project) and 282672 (EMBRACE project)

Current challenges of implementing anthropogenic land-use and land-cover change in models contributing to climate change assessments

This is the author accepted manuscript. The final version is available from European Geosciences Union (EGU) via the DOI in this record.Land-use and land-cover change (LULCC) represents one of the key drivers of global environmental change. However, the processes and drivers of anthropogenic land-use activity are still overly simplistically implemented in terrestrial biosphere models (TBMs). The published results of these models are used in major assessments of processes and impacts of global environmental change, such as the reports of the Intergovernmental Panel on Climate Change (IPCC). Fully coupled models of climate, land use and biogeochemical cycles to explore land use-climate interactions across spatial scales are currently not available. Instead, information on land use is provided as exogenous data from the land-use change modules of integrated assessment models (IAMs) to TBMs. In this article, we discuss, based on literature review and illustrative analysis of empirical and modeled LULCC data, three major challenges of this current LULCC representation and their implications for land use-climate interaction studies: (I) provision of consistent, harmonized, land-use time series spanning from historical reconstructions to future projections while accounting for uncertainties associated with different land-use modeling approaches, (II) accounting for sub-grid processes and bidirectional changes (gross changes) across spatial scales, and (III) the allocation strategy of independent land-use data at the grid cell level in TBMs. We discuss the factors that hamper the development of improved land-use representation, which sufficiently accounts for uncertainties in the land-use modeling process. We propose that LULCC data-provider and user communities should engage in the joint development and evaluation of enhanced LULCC time series, which account for the diversity of LULCC modeling and increasingly include empirically based information about sub-grid processes and land-use transition trajectories, to improve the representation of land use in TBMs. Moreover, we suggest concentrating on the development of integrated modeling frameworks that may provide further understanding of possible land-climate-society feedbacks.The research in this paper has been supported by the European Research Council under the European Union’s Seventh Framework Programme project LUC4C (Grant No. 603542), ERC grant GLOLAND (No. 311819) and BiodivERsA project TALE (No. 832.14.006) funded by the Dutch National Science Foundation (NWO). This research contributes to the Global Land Project (www.globallandproject.org). This is paper number 26 of the Birmingham Institute of Forest Research

Impact of merging of historical and future climate data sets on land carbon cycle projections for South America

Earth System Models (ESMs) project climate change, but they often contain biases in their estimates of contemporary climate that propagate into simulated futures. Land models translate climate projections into surface impacts, but these will be inaccurate if ESMs have substantial errors. Bias concerns are relevant for terrestrial physiological processes which often respond non-linearly (i.e. contain threshold responses) and are therefore sensitive to absolute environmental conditions as well as changes. We bias-correct the UK Met Office ESM, HadGEM2-ES, against the CRU–JRA observation-based gridded estimates of recent climate. We apply the derived bias corrections to future projections by HadGEM2-ES for the RCP8.5 scenario of future greenhouse gas concentrations. Focusing on South America, the bias correction includes adjusting for ESM estimates that, annually, are approximately 1 degree too cold, for comparison against 21st Century warming of around 4 degrees. Locally, these values can be much higher. The ESM is also too wet on average, by approximately 1 mm·day−1, which is substantially larger than the mean predicted change. The corrected climate fields force the Joint UK Land Environment Simulator (JULES) dynamic global vegetation model to estimate land surface changes, with an emphasis on the carbon cycle. Results show land carbon sink reductions across South America, and in some locations, the net land–atmosphere CO2 flux becomes a source to the atmosphere by the end of this century. Transitions to a CO2 source is where increases in plant net primary productivity are offset by larger enhancements in soil respiration. Bias-corrected simulations estimate the rise in South American land carbon stocks between pre-industrial times and the end of the 2080s is ∼12 GtC lower than that without climate bias removal, demonstrating the importance of merging historical observational meteorological forcing with ESM diagnostics. We present evidence for a substantial climate-induced role of greater soil decomposition in the fate of the Amazon carbon sink

Importance of soil thermal regime in terrestrial ecosystem carbon dynamics in the circumpolar north

© The Author(s), 2016. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Global and Planetary Change 142 (2016): 28-40, doi:10.1016/j.gloplacha.2016.04.011.In the circumpolar north (45-90°N), permafrost plays an important role in vegetation and carbon (C) dynamics. Permafrost thawing has been accelerated by the warming climate and exerts a positive feedback to climate through increasing soil C release to the atmosphere. To evaluate the influence of permafrost on C dynamics, changes in soil temperature profiles should be considered in global C models. This study incorporates a sophisticated soil thermal model (STM) into a dynamic global vegetation model (LPJ-DGVM) to improve simulations of changes in soil temperature profiles from the ground surface to 3 m depth, and its impacts on C pools and fluxes during the 20th and 21st centuries.With cooler simulated soil temperatures during the summer, LPJ-STM estimates ~0.4 Pg C yr-1 lower present-day heterotrophic respiration but ~0.5 Pg C yr-1 higher net primary production than the original LPJ model resulting in an additional 0.8 to 1.0 Pg C yr-1 being sequestered in circumpolar ecosystems. Under a suite of projected warming scenarios, we show that the increasing active layer thickness results in the mobilization of permafrost C, which contributes to a more rapid increase in heterotrophic respiration in LPJ-STM compared to the stand-alone LPJ model. Except under the extreme warming conditions, increases in plant production due to warming and rising CO2, overwhelm the enhanced ecosystem respiration so that both boreal forest and arctic tundra ecosystems remain a net C sink over the 21st century. This study highlights the importance of considering changes in the soil thermal regime when quantifying the C budget in the circumpolar north.This research is supported by funded projects to Q. Z. National Science Foundation (NSF- 1028291 and NSF- 0919331), the NSF Carbon and Water in the Earth Program (NSF-0630319), the NASA Land Use and Land Cover Change program (NASA- NNX09AI26G), and Department of Energy (DE-FG02-08ER64599).2017-05-0

Sample size calculations for cluster randomised controlled trials with a fixed number of clusters

Background\ud Cluster randomised controlled trials (CRCTs) are frequently used in health service evaluation. Assuming an average cluster size, required sample sizes are readily computed for both binary and continuous outcomes, by estimating a design effect or inflation factor. However, where the number of clusters are fixed in advance, but where it is possible to increase the number of individuals within each cluster, as is frequently the case in health service evaluation, sample size formulae have been less well studied. \ud \ud Methods\ud We systematically outline sample size formulae (including required number of randomisation units, detectable difference and power) for CRCTs with a fixed number of clusters, to provide a concise summary for both binary and continuous outcomes. Extensions to the case of unequal cluster sizes are provided. \ud \ud Results\ud For trials with a fixed number of equal sized clusters (k), the trial will be feasible provided the number of clusters is greater than the product of the number of individuals required under individual randomisation ($n_i$) and the estimated intra-cluster correlation ($\rho$). So, a simple rule is that the number of clusters ($\kappa$) will be sufficient provided: \ud \ud $\kappa$ > $n_i$ x $\rho$\ud \ud Where this is not the case, investigators can determine the maximum available power to detect the pre-specified difference, or the minimum detectable difference under the pre-specified value for power. \ud \ud Conclusions\ud Designing a CRCT with a fixed number of clusters might mean that the study will not be feasible, leading to the notion of a minimum detectable difference (or a maximum achievable power), irrespective of how many individuals are included within each cluster. \ud \u