Projections of leaf area index in earth system models

Mahowald, N. M.; Lo, Fiona; Zheng, Yang; Harrison, L.; Funk, Chris; Lombardozzi, Danica; Goodale, Christine L.

doi:10.5194/esd-7-211-2016

Cited by 119 publications

(122 citation statements)

References 68 publications

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“…It closely modulates the energy balance, as well as the hydrological and carbon cycles of the coupled land-atmosphere system at different spatiotemporal scales (Mahowald et al, 2016). For atmosphere-ocean GCMs, including those of HighResMIP, the mean seasonal cycle of LAI is commonly prescribed to improve the physical and biophysical simulations of the land-atmosphere system (Taylor et al, 2011).…”

Section: Appendix C: Targeted Additional Experiments C1 Leaf Area Indmentioning

confidence: 99%

High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

et al. 2016

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Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes.However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25 • in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs).Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability.The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, "what are the origins and consequences of systematic model biases?", but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.

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Section: Appendix C: Targeted Additional Experiments C1 Leaf Area Indmentioning

confidence: 99%

High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

et al. 2016

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“…2018, 10, 148 2 of 26 models [2,3]. Global LAI products have been derived from satellites, which have the advantage of large spatial coverage and serve as inputs for many numerical models.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties

et al. 2018

Remote Sensing

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Leaf area index (LAI) is a key input for many land surface models, ecological models, and yield prediction models. In order to make the model simulation and/or prediction more reliable and applicable, it is crucial to know the characteristics and uncertainties of remotely sensed LAI products before they are input into models. In this study, we conducted a comparison of four global remotely sensed LAI products-Global Land Surface Satellite (GLASS), Global LAI Product of Beijing Normal University (GLOBALBNU), Global LAI Map of Chinese Academy of Sciences (GLOBMAP), and Moderate-resolution Imaging Spectrometer (MODIS) LAI, while the former three products are newly developed by three Chinese research groups on the basis of the MODIS land reflectance product over China between 2001 and 2011. Direct validation by comparing the four products to ground LAI observations both globally and over China demonstrates that GLASS LAI shows the best performance, with R 2 = 0.70 and RMSE = 0.96 globally and R 2 = 0.94 and RMSE = 0.61 over China; MODIS performs worst (R 2 = 0.55, RMSE = 1.23 globally and R 2 = 0.03, RMSE = 2.12 over China), and GLOBALBNU and GLOBMAP performs moderately. Comparison of the four products shows that they are generally consistent with each other, giving the smallest spatial correlation coefficient of 0.7 and the relative standard deviation around the order of 0.3. Compared with MODIS LAI, GLOBALBNU LAI is the most similar, followed by GLASS LAI and GLOBMAP. Large differences mainly occur in southern regions of China. LAI difference analysis indicates that evergreen needleleaf forest (ENF), woody savannas (SAV) biome types and temperate dry hot summer, temperate warm summer dry winter and temperate hot summer no dry season climate types correspond to high standard deviation, while ENF and grassland (GRA) biome types and temperate warm summer dry winter and cold dry winter warm summer climate types are responsible for the large relative standard deviation of the four products. Our results indicate that although the three newly developed products have improved the accuracy of LAI estimates, much work remains to improve the LAI products especially in ENF, SAV, and GRA regions and temperate climate zones. Findings from our study can provide guidance to communities regarding the performance of different LAI products over mainland China.

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“…On a global scale, Earth system models tend to grow more leaf area under future climate conditions but with significant variation across models [56]. However, modeled leaf area increases are not sufficient to compensate for decreased stomatal conductance under large increases in CO 2 [12,57], although increases in leaf area could be of sufficient magnitude regionally [58].…”

Section: Carbon Dioxidementioning

confidence: 99%

“…Because the calculation of PET can't account for any plant-driven responses to CO 2 , it shows much higher demand for water under a warmer climate, and when included as the "demand" calculation in drought indices, it suggests more severe and more lengthy droughts. However, Swann et al [12] find that rates of ET increase only slightly in simulations of future climate because stomatal closure more than compensates for increases in leaf area [56] and increases in demand from the atmosphere. Thus, our estimate for future water balance on land depends strongly on our highly uncertain estimate of changes in leaf area.…”

Section: Carbon Dioxidementioning

confidence: 99%

“…However, it is primarily asserted the Earth system models are more likely overestimating the leaf area response of plants to CO 2 , rather that underestimating it. While Earth system models tend to predict increases in photosynthesis and leaf area under future climate [56], many of these models do not represent moderations on increases in photosynthesis and growth that may arise from nutrient limitation [59], plant acclimation and adaptation [60], etc.…”

Section: Carbon Dioxidementioning

confidence: 99%

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Plants and Drought in a Changing Climate

Swann¹

2018

Preprint

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Purpose of review Climate is changing in response to rising concentrations of atmospheric CO 2 and it is commonly asserted that this will cause droughts to become more frequent and severe. However, different metrics of drought give diverging estimates of future impacts. I present a summary of the significant yet underappreciated influence that plant stomatal and growth responses to CO 2 have on drought, and highlight new insights into the impacts of drought on plants in a warmer world. Recent findings Plants influence the water availability on land, and thus reduce the duration and intensity of droughts under higher CO 2 conditions. Plants are concurrently more vulnerable to mortality when droughts occur under hotter conditions. Summary The frequency and severity of drought in the future depends on the response of plants to a changing climate-ignoring plant responses leads to over-prediction of drought. Nonetheless, the impact of current frequencies of drought on plants could lead to higher mortality rates in the future as plants must withstand drought stress simultaneously with hotter temperatures.

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Projections of leaf area index in earth system models

Cited by 119 publications

References 68 publications

High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

Comparison of the Spatial Characteristics of Four Remotely Sensed Leaf Area Index Products over China: Direct Validation and Relative Uncertainties

Plants and Drought in a Changing Climate

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