Sensitivity of stomatal conductance to soil moisture: Implications for tropospheric ozone

Abstract. Soil moisture and water stress play a pivotal role in regulating stomatal behaviour of plants; however, in the last decade, the role of water availability has often been neglected in atmospheric chemistry modelling studies as well as in integrated risk assessments, despite the fact that plants remove a large amount of atmospheric compounds from the lower troposphere through stomata. The main aim of this study is to evaluate, within the chemistry transport model CHIMERE, the effect of soil water limitation on stomatal conductance and assess the resulting changes in atmospheric chemistry testing various hypotheses of water uptake by plants in the rooting zone. Results highlight how dry deposition significantly declines when soil moisture is used to regulate the stomatal opening, mainly in the semi-arid environments: in particular, over Europe the amount of ozone removed by dry deposition in one year without considering any soil water limitation to stomatal conductance is about 8.5 TgO3, while using a dynamic layer that ensures that plants maximize the water uptake from soil, we found a reduction of about 10 % in the amount of ozone removed by dry deposition (∼ 7.7 TgO3). Although dry deposition occurs from the top of canopy to ground level, it affects the concentration of gases remaining in the lower atmosphere, with a significant impact on ozone concentration (up to 4 ppb) extending from the surface to the upper troposphere (up to 650 hPa). Our results shed light on the importance of improving the parameterizations of processes occurring at plant level (i.e. from the soil to the canopy) as they have significant implications for concentration of gases in the lower troposphere and resulting risk assessments for vegetation or human health

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)

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)

Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks

Final published version of article.© 2014 American Meteorological SocietyIn the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO2 concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO2 emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations.When forced by RCP8.5 CO2 emissions, models simulate a large spread in atmospheric CO2; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO2 (on average by 44 ppm, 985 ± 97ppm by 2100) and hence higher radiative forcing (by 0.25Wm-2) when driven by CO2 emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO2, with the present-day biases reasonably well correlated with future atmospheric concentrations' departure from the prescribed concentration. The uncertainty in CO2 projections is mainly attributable to uncertainties in the response of the land carbon cycle. As a result of simulated higher CO2 concentrations than in the concentration-driven simulations, temperature projections are generally higher when ESMs are driven with CO2 emissions. Global surface temperature change by 2100 (relative to present day) increased by 3.9° ± 0.9°C for the emission-driven simulations compared to 3.7° ± 0.7°C in the concentration-driven simulations. Although the lower ends are comparable in both sets of simulations, the highest climate projections are significantly warmer in the emission-driven simulations because of stronger carbon cycle feedbacks. © 2014 American Meteorological Society.Department for Environment, Food and Rural Affairs (DEFRA)Department of Energy & Climate Change (DECC

Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models

PublishedJournal ArticleThe authors assess the ability of 18 Earth system models to simulate the land and ocean carbon cycle for the present climate. These models will be used in the next Intergovernmental Panel on Climate Change (IPCC) Fifth AssessmentReport (AR5) for climate projections, and such evaluation allows identification of the strengths and weaknesses of individual coupled carbon-climate models as well as identification of systematic biases of themodels. Results show thatmodels correctly reproduce the main climatic variables controlling the spatial and temporal characteristics of the carbon cycle. The seasonal evolution of the variables under examination is well captured. However, weaknesses appear when reproducing specific fields: in particular, considering the land carbon cycle, a general overestimation of photosynthesis and leaf area index is found for most of the models, while the ocean evaluation shows that quite a few models underestimate the primary production. The authors also propose climate and carbon cycle performance metrics in order to assess whether there is a set of consistently better models for reproducing the carbon cycle. Averaged seasonal cycles and probability density functions (PDFs) calculated from model simulations are compared with the corresponding seasonal cycles and PDFs from different observed datasets. Although the metrics used in this study allow identification of somemodels as better or worse than the average, the ranking of this study is partially subjective because of the choice of the variables under examination and also can be sensitive to the choice of reference data. In addition, it was found that the model performances show significant regional variations. © 2013 American Meteorological Society.This work was supported by the European Commission's 7th Framework Programme under Grant Agreements 238366 (GREENCYCLESII) and 282672 (EMBRACE), while Dr. Jones was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Program (GA01101)

Evaluation of land surface models in reproducing satellite-derived LAI over the high-latitude northern hemisphere. Part I: Uncoupled DGVMs

PublishedJournal ArticleLeaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN) from the latest version (third generation) of GIMMS AVHRR NDVI data over the period 1986-2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees. © 2013 by the authors.The corresponding author also thanks the CONACYT-CECTI and the University of Exeter for their funding during the PhD studies. The National Center for Atmospheric Research is sponsored by the National Science Foundation

Spatiotemporal patterns of terrestrial gross primary production: A review

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.There is another record for this publication in ORE at http://hdl.handle.net/10871/21007Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990-2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.European Commission's Seventh Framework Programme. Grant Numbers: 238366, 28267

Spatiotemporal patterns of terrestrial gross primary production: A review

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.There is another record for this publication in ORE at http://hdl.handle.net/10871/30934Great advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990-2009) from two observation-based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon-climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation-based data that show little IAV and trend, while the process-based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation-based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.European Commission's Seventh Framework Programme. Grant Numbers: 238366, 28267