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Summary of achievements

Sam Levis continues to work with, develop, and support the Community Land Model (CLM). In model development, Sam upgraded CLM's CN module (Peter Thornton's Carbon-Nitrogen cycling model) to Dynamic Global Vegetation Model (dgvm) status. This means that, if requested, CN can now simulate the movement of plant types across model grid cells (the way that the CLM-dgvm could before), instead of using satellite-derived plant type distributions. Furthermore, Sam introduced to CN the "agro" part of the IBIS dgvm. As a result, now CLM-CN can simulate annual planting-to-harvest cycles for maize, wheat, and soybean that respond to environmental conditions. Preliminary results suggest that these dynamic crops appear more realistic than CLM-CN's generic crop. In December, Sam convened and chaired an AGU session on land use and land management in Earth System Models. Regarding CLM support, Sam continues to respond to hundreds of community (NCAR and external) requests per year.

Publications

Robock, A., C.M. Ammann, L. Oman, D. Shindell, S. Levis and G. Stenchikov. 2009: Did the Toba volcanic eruption of ~74 kBP produce widespread glaciation? J. Geophys. Res., 114, D10107, doi:10.1029/2008JD011652.

Figure 1: High resolution figure

Abstract: It has been suggested that the Toba volcanic eruption, approximately 74 ka B.P., was responsible for the extended cooling period and ice sheet advance immediately following it, but previous climate model simulations, using 100 times the amount of aerosols produced by the 1991 Mount Pinatubo eruption, have been unable to produce such a prolonged climate response. Here we conduct six additional climate model simulations with two different climate models, the National Center for Atmospheric Research Community Climate System Model 3.0 (CCSM3.0) and National Aeronautics and Space Administration Goddard Institute for Space Studies ModelE, in two different versions, to investigate additional mechanisms that may have enhanced and extended the forcing and response from such a large supervolcanic eruption. With CCSM3.0 we include a dynamic vegetation model to explicitly calculate the feedback of vegetation death on surface fluxes in response to the large initial reduction in transmitted light, precipitation, and temperature. With ModelE we explicitly calculate the effects of an eruption on stratospheric water vapor and model stratospheric chemistry feedbacks that might delay the conversion of SO2 into sulfate aerosols and prolong the lifetime and radiative forcing of the stratospheric aerosol cloud. To span the uncertainty in the amount of stratospheric injection of SO2, with CCSM3.0 we used 100 times the Pinatubo injection, and with ModelE we used 33, 100, 300, and 900 times the Pinatubo injection without interactive chemistry, and 300 times Pinatubo with interactive chemistry. Starting from a roughly present-day seasonal cycle of insolation, CO2 concentration, and vegetation, or with 6 ka B.P. conditions for CCSM3.0, none of the runs initiates glaciation. The CCSM3.0 run produced a maximum global cooling of 10 K and ModelE runs produced 8–17 K of cooling within the first years of the simulation, depending on the injection, but in all cases, the climate recovers over a few decades. Nevertheless, the "volcanic winter" following a supervolcano eruption of the size of Toba today would have devastating consequences for humanity and global ecosystems. These simulations support the theory that the Toba eruption indeed may have contributed to a genetic bottleneck.

Figure caption: For the CCSM3.0 run, (top) percent coverage of four major vegetation types for the year before the eruption. (bottom) As above but averaged for the 4 years just after the eruption. Broadleaf evergreen trees virtually disappear, as do tropical deciduous trees.

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Levis, S., P. Thornton, G. Bonan and C. Kucharik. 2009: Modeling land use and land management with the Community Land Model. iLEAPs Newsletter, in press.

Figure 2: High resolution figure

Figure caption: 25-year average monthly leaf area index (LAI) in Mead, Nebraska, USA, from a CN-crop point simulation driven with observed weather. The grass, crop, corn, wheat, and soy plant functional types occupy different parts of the single model grid cell in this simulation. The grass and generic crop phenologies (i.e. their periods of greenness and dormancy) are simulated by CLM-CN, while the phenologies of corn, wheat, and soy are simulated by CN-crop.

 

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Wang, Y., M. Notaro, Z. Liu, R. Gallimore, S. Levis and J.E. Kutzbach. 2008: Detecting vegetation-precipitation feedbacks in mid-Holocene North Africa from two climate models Climate of the Past, 89, 59-67. [article]

Figure 3: High resolution figure

Abstract: Using two climate-vegetation model simulations from the Fast Ocean Atmosphere Model (FOAM) and the Community Climate System Model (CCSM, version 2), we investigate vegetation-precipitation feedbacks across North Africa during the mid-Holocene. From mid-Holocene snapshot runs of FOAM and CCSM2, we detect a negative feedback at the annual timescale with our statistical analysis. Using the Monte-Carlo bootstrap method, the annual negative feedback is further confirmed to be significant in both simulations. Additional analysis shows that this negative interaction is partially caused by the competition between evaporation and transpiration in North African grasslands. Furthermore, we find the feedbacks decrease with increasing timescales, and change signs from positive to negative at increasing timescales in FOAM. The proposed mechanism for this sign switch is associated with the different persistent timescales of upper and lower soil water contents, and their interactions with vegetation and atmospheric precipitation.

Figure caption: Annual rainfall (mm/day) for CCSM2 (A) 6K control, (C) 0K control, and (E) 6K minus 0K. Top soil moisture content (mm³/mm³) for CCSM2 (B) 6K control, (D) 0K control, and (F) 6K minus 0K from two sets of 350-year control simulations.

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Gent, P.R., S.G. Yeager, R.B. Neale, S. Levis and D.A. Bailey. 2009: Improvements in a half degree atmosphere/land version of the CCSM. Climate Dynamics, 79, 25-58, doi:10.1007/s00382-009-0614-8.

Figure 4: High resolution figure

Abstract: A decadal climate projection between 1980 and 2030 using a nominal 0.5° resolution in the atmosphere and land components has been performed using the Community Climate System Model, version 3.5. The mean climate is compared to a companion simulation using a nominal 2° resolution in the atmosphere and land components. The increased atmosphere resolution has several benefits, and produces a significantly better mean climate. The maximum sea surface temperature biases in the major upwelling regions, including the West Coast of the USA, are reduced by more than 60%. Precipitation patterns are improved in the summer Asian monsoon, mostly due to the better resolved orography, and in the eastern tropical Pacific Ocean south of the equator. The improved precipitation patterns lead to better river flows in many rivers worldwide. The atmospheric circulation in the Arctic also improves, which leads to a better regional sea ice thickness distribution in the Arctic Ocean.

Figure caption: Annual mean Arctic sea ice thickness in meter from (a) 0.5° run, and (b) 2° run.

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Sacks, W.J., B.I. Cook, N. Buenning, S. Levis and J.H. Helkowski. 2009: Effects of global irrigation on the near-surface climate. Climate Dynamics, 33, 159-175, doi:10.1007/s00382-008-0445-z.

Figure 5: High resolution figure

Abstract: Irrigation delivers about 2,600 km³ of water to the land surface each year, or about 2% of annual precipitation over land. We investigated how this redistribution of water affects the global climate, focusing on its effects on near-surface temperatures. Using the Community Atmosphere Model (CAM) coupled to the Community Land Model (CLM), we compared global simulations with and without irrigation. To approximate actual irrigation amounts and locations as closely as possible, we used national-level census data of agricultural water withdrawals, disaggregated with maps of croplands, areas equipped for irrigation, and climatic water deficits. We further investigated the sensitivity of our results to the timing and spatial extent of irrigation. We found that irrigation alters climate significantly in some regions, but has a negligible effect on global-average near-surface temperatures. Irrigation cooled the northern mid-latitudes; the central and southeast United States, portions of southeast China and portions of southern and southeast Asia cooled by ~0.5 K averaged over the year. Much of northern Canada, on the other hand, warmed by ~1 K. The cooling effect of irrigation seemed to be dominated by indirect effects like an increase in cloud cover, rather than by direct evaporative cooling. The regional effects of irrigation were as large as those seen in previous studies of land cover change, showing that changes in land management can be as important as changes in land cover in terms of their climatic effects. Our results were sensitive to the area of irrigation, but were insensitive to the details of irrigation timing and delivery.

Figure caption: a Annual irrigation amounts used to force our model (mm year-1). This map disaggregates national-level census data (sub-national for the United States and China) using the weighting maps shown in Fig. 1. b–e Seasonal distribution of irrigation in our model, based on when crop LAI exceeded 80% of its annual maximum for a grid cell: b DJF, c MAM, d JJA, e SON.

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Lebel, T., B. Cappelaere, S. Galle, N. Hanan, L. Kergoat, S. Levis, B. Vieux, L. Descroix, M. Gosset, E. Mougin, C. Peugeot, L. Seguis. 2009: AMMA-CATCH studies in the Sahelian region of West-Africa: an overview. J. Hydrol., 375, 3-13, doi:10.1016/j.jhydrol.2009.03.020.

Figure 6: High resolution figure

Abstract: The African Monsoon Multidisciplinary Analysis (AMMA) is an international and interdisciplinary experiment designed to investigate the interactions between atmospheric, oceanic and terrestrial systems and their joint controls on tropical monsoon dynamics in West Africa. This special issue reports results from a group of AMMA studies regrouped in the component “Couplage de l’Atmosphère Tropicale et du Cycle Hydrologique” (CATCH). AMMA-CATCH studies focus on measuring and understanding land surface properties and processes in West Africa, the role of terrestrial systems in altering boundary layer dynamics, and thus the potential that surface hydrology and biology, and human land use practices, may directly or indirectly affect monsoon dynamics and rainfall in the region. AMMA-CATCH studies focus on three intensively instrumented mesoscale sites in Mali, Niger and Benin that sample across the 100–1300 mm/annum rainfall gradient of the Sahel, Sudan and North-Guinean bioclimatic zones. Studies report on: (i) surface–boundary layer interactions that may influence atmospheric convergence and convective processes and thus rainfall type, timing and amount; (ii) vegetation dynamics at seasonal to decadal time-scales that may respond to, and alter, atmospheric processes; (iii) surface–atmosphere fluxes of heat, water and carbon dioxide that directly influence the atmosphere; (iv) soil moisture variability in space and time that provide the proximate control on vegetation activity, evapotranspiration and energy balance; and (v) local and mesoscale modeling of hydrology and land surface–atmosphere exchanges to assess their role in the hydrological, atmospheric and rainfall dynamics of West Africa. The AMMA-CATCH research reported in this issue will be extended in future years as measurements and analysis continue and are concluded within the context of both CATCH and the wider AMMA study. This body of research will contribute to an improved understanding of the functioning of the coupled West African system, and enhance our ability to model and predict rainfall, vegetation and biogeochemical dynamics across time-scales (day, year, decade, and century), and in response to changing climate and land use. Such information is vital for policy makers and managers in planning for future economic development, sustainability and livelihoods of the growing populations of the region.

Figure caption: Distribution of population in West Africa (2003). The most populated zones are the coastal region and the cultivated Sahel. Red-orange and blue-green colors indicate densely versus sparsely populated areas, respectively.