Land-atmosphere interactions, ecosystem impacts and feedbacks
Research leaders: J. Ramirez, Denning, and L. Poff
Student programs in this theme area will equip scientists to interpret and inform science and policy about climate issues and their direct impact on water supplies, management, and ecosystems. For example, climate change is expected to alter water systems management and harm rich but sensitive ecosystems in water-stressed areas (arid West, densely populated Southeast).
Concern about global climate change is increasingly important as a driver of policy decisions at the regional, state, and local levels. Impacts of climate change on ecosystems and water resources in the western United States will depend on changes in the distribution of temperature and precipitation spatially (e.g., mountains vs. basins vs. agriculturally-intensive plains) and seasonally (e.g., summer rains vs. winter snow, timing of spring runoff).
Unfortunately, the current generation of climate models used for global change research cannot accurately predict decadal trends at resolutions approaching those required to understand impacts on western water resources and ecosystems (IPCC, 2007). Climate model grid spacing is typically on the order of 200 km, yet in the western US, such an area may include high terrain that develops abundant snowpack feeding a reservoir system, a network of stream and river channels, and lowland cities and farms that draw from the stored water derived from snowmelt. High-resolution models can simulate interactions between climate and regional ecosystems, but must be “driven” from coarse-resolution global models, without the potential for local and regional changes to feed back on the larger scale. The scale mismatch between predictive global climate models and analyses of hydrologic and ecological impacts and feedbacks has been a pernicious problem, and will be a major focus of WATER-IGERT research and teaching.
A dramatically new approach to the representation of clouds and precipitation in global models of weather and climate is being pioneered by the Center for Multiscale Modeling of Atmospheric Processes (CMMAP, http://www.cmmap.org), an NSF Science and Technology Center headquartered at CSU. Global models cannot resolve cloud-scale processes, and therefore use empirical representations to estimate the effects of clouds over hundreds of km using only large-scale information (Arakawa, 2004). Ideally, global models would be run with grid spacing of at most a few km, to capture updrafts, cloud formation, and organized circulations that lead to rain and snow. Such calculations have been performed (Tomita et al, 2005), but the computational expense is so extreme that only a few weeks can be simulated on the world’s fastest computers.
At CMMAP, we have developed a Multiscale Modeling Framework (MMF, Randall et al, 2003b) in which cloud-resolving submodels are run inside a small fraction of every grid column in a global atmospheric model (Grabowski, 2001, 2004; Khairoutdinov and Randall, 2001; Jung and Arakawa, 2005). This approach is analogous to public opinion polling, because it is unnecessary to simulate every cloud in a large-scale grid column, just a representative sub-population. Global simulation with the MMF is hundreds of times more expensive than with traditional coarse-resolution models, but produces a much more realistic climate (Khairoutdinov et al, 2005; Tao et al, 2007). A critical advantage of the MMF is that it predicts variations of precipitation and cloud processes on scales commensurate with observations in the field and from satellites, which allows direct evaluation against data that is impossible for traditional climate models (Randall et al, 2003a; Demott and Randall, 2007).
Research by teams of WATER-IGERT scholars and faculty will study regional coupling among atmospheric, hydrologic, and ecological systems under current and future climate regimes. With the advent of km-scale estimates of precipitation and other climate properties from the global MMF, WATER-IGERT students will have a unique opportunity to study land-atmosphere interactions in response to global climate change. Up to now, cloud-scale predictions from the MMF have been aggregated and passed to the land-surface submodel at the large scale (hundreds of km). WATER-IGERT students will develop and evaluate a coupled land-atmosphere MMF in which a separate instance of the land component model is run in each cloud-resolving cell of the atmospheric component, rather than only at the large scale. This will include treatment of topographic effects on precipitation, heterogeneous vegetation, high-resolution spatio-temporal patterns of soil moisture, ecosystem water stress, photosynthesis, and respiration. The Simple Biosphere Model (SiB) is a land-surface parameterization used to compute biophysical exchanges in climate models and later adapted to include ecosystem metabolism (Denning et al, 1996a,b), stable isotopes (Suits et al, 2005), and agriculture (Hanan et al, 2004; Lokupitiya et al, 2009). WATER-IGERT students will use the coupled model to investigate changes in regional snowpack under various climate change scenarios and consequent water availability for agriculture and economic development in the western US. They will evaluate potential changes in atmospheric moisture and circulation in response to regional land-use, urban development, and irrigation of croplands. They will consider changing irrigation demand and agricultural production as a result of projected changes in physical climate as well as atmospheric CO2 (including physiological responses of crop cultivars) during the next several decades.
Spatial variations in cloud-scale prediction of precipitation in SiB-MMF simulations of the current climate will be evaluated against observations made over Northern Colorado by the CSU-CHILL radar. Spatial patterns will be analyzed by season and compared with statistical downscaling from the predictions of the large-scale global grid (Luca et al, 2001). This analysis will be performed separately with the land model run only on the large-scale grid and with the land model run interactively on the cloud-scale of the MMF. We will evaluate the degree to which interactions between the vegetation, soil moisture, and overlying atmosphere affect the organization of the precipitation patterns (Kochendorfer and Ramírez, 2005) and persistence of anomalous moisture regimes.
Much of the water that supports agriculture in the western US is derived from melting mountain snowpacks. The accumulation of this snow, and the timing and rate of the spring runoff when it melts, are critical to the ability to capture this water in reservoirs, and to the structure and function of aquatic and riparian ecosystems in the region. Mountain snowpack and the hydrologic dynamics of spring runoff are very likely to change in coming decades in response to global climate change. Teams of WATER-IGERT students and faculty will be uniquely positioned to analyze these changes and their impacts on ecosystems and water resources using the coupled multiscale climate modeling system (SiB-MMF). We will use both the high-resolution output of the coupled MMF and statistical downscaling along topographic gradients to analyze potential changes in mountain snowpacks under climate change. Students will analyze spring runoff patterns and project changes in reservoir storage. They will evaluate the likely impacts of these changes on riparian and aquatic ecosystems, water resource management, and agricultural production in the region. They will work with regional stakeholders to develop scenarios for adapting to impacts of a changing climate on regional hydrology, water resources, and the agricultural economy.
The western United States is particularly sensitive to inter-annual variability of summer and winter climate. In the most arid regions of the southwest United States, for example, the inter-annual variability of summer precipitation can be larger than the mean summer rainfall itself (e.g., Higgins et al., 1998; Castro et al., 2001). Sustained flood or drought conditions over broad areas result from shifts in the large-scale circulation pattern (e.g., Trenberth and Guillemot, 1996). Short- or long-term departures from average conditions may adversely affect infrastructure, agricultural production, water supply, and hydroelectric power generation (e.g., Meehl et al., 2000). There is a critical need to understand the causes of inter-annual variability so seasonal forecasts can be improved.
Changes in vegetation and soil processes, both hydrodynamic and thermodynamic, directly affect the surface energy and moisture fluxes into the atmosphere [e.g., Pielke, 2001; Kochendorfer and Ramírez, 2005]. Hoffman and Jackson  propose that as a result of atmospheric-vegetation interactions, anthropogenic impacts can exacerbate declines in precipitation (e.g., drought). In the context of climate, soil and vegetation dynamics are as much a part of the climate system as are atmospheric variables [Hayden, 1998; Pielke, 1998; Wang and Eltahir, 2000a, 2000b]. Eastman et al., [2001a,b] showed that land-use change, grazing, and increased CO2 can significantly alter the regional climate system in the central Great Plains of the United States. For example, the effects of enhanced atmospheric concentrations of CO2 on plant growth on a seasonal time scale are shown to amplify the radiative effect of enhanced atmospheric CO2 on the region. The non-linear effect of vegetation-atmospheric feedback on this scale results in a complex spatial and temporal pattern of response. Not only is there a teleconnection of atmospheric conditions to locations distant from where the land feedback occurs, but the landscape at distant locations itself is influenced by the altered weather.
Pan et al.  concluded that soil moisture significantly affects summer rainfall in both drought and flood years in the midwest of the United States. Pan et al.,  concluded that the non-linear soil moisture-atmosphere feedback manifested in such manner that increases in soil moisture enhanced local rainfall when the lower atmosphere was thermodynamically unstable and relatively dry but decreased rainfall when the atmosphere was humid and lacked sufficient thermal forcing to initiate deep cumulus convection. Segal et al.  concluded that average rainfall in North America is increased as a result of irrigation. Kiang and Eltahir , Eastman et al. , Lu et al. , and Wang and Eltahir [2000a, 2000b] used coupled regional atmospheric-vegetation dynamics models to demonstrate the importance of two-way interaction between the atmosphere and vegetation response. Many other studies support the result that there is a significant effect on the regional- and large-scale climate due to land-surface processes. This research theme will examine the impact of the nonlinear dynamics of land surface feedbacks (e.g., soil moisture, vegetation, etc.) to precipitation on the variability of precipitation and soil moisture at the local and regional-scale. We will use coupled land surface-atmosphere models and conceptual, physically based models (i.e., Kochendorfer and Ramirez, 2005), to examine land surface-atmosphere feedbacks in the context of their influence on the intensity, magnitude, duration and spatial extent of floods and droughts. These models will be implemented in a fully coupled manner and the analyses will be performed at a range of spatial and temporal scales such that large-scale forcings (i.e., ENSO, PDO, and others), as well as regional- and local-scale forcings (i.e., land use changes, irrigation, urbanization, etc.) are explicitly accounted for. Particular emphasis will be given to determining the impact of the non-linear feedbacks on the characteristics of the probability distribution of precipitation so that risk-based management and adaptation decisions can be implemented.
The primary mission of I-WATER is to prepare Ph.D. students to work in an interdisciplinary team-based activity. Our research themes involve interacting teams of hydrologists, meteorologists, ecologists, and management experts. I-WATER features problem-focused research to bridge basic and applied science by combining fundamental research on scientific problems with application of scientific knowledge to actual resource issues.
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