Yaqiong Lu

Crop growth and irrigation interact to influence surface fluxes in a regional climate-cropland model (WRF3.3-CLM4crop)

Abstract In this study, we coupled Version 4.0 of the Community Land Model that includes crop growth and management (CLM4crop) into the Weather Research and Forecasting (WRF) model Version 3.3 to better represent interactions between climate and agriculture. We evaluated the performance of the coupled model (WRF3.3-CLM4crop) by comparing simulated crop growth and surface climate to multiple observational datasets across the continental United States. The results showed that although the model with dynamic crop growth overestimated leaf area index (LAI) and growing season length, interannual variability in peak LAI was improved relative to a model with prescribed crop LAI and growth period, which has no environmental sensitivity. Adding irrigation largely improved daily minimum temperature but the RMSE is still higher over irrigated land than non-irrigated land. Improvements in climate variables were limited by an overall model dry bias. However, with addition of an irrigation scheme, soil moisture and surface energy flux partitioning were largely improved at irrigated sites. Irrigation effects were sensitive to crop growth: the case with prescribed crop growth underestimated irrigation water use and effects on temperature and overestimated soil evaporation relative to the case with dynamic crop growth in moderately irrigated regions. We conclude that studies examining irrigation effects on weather and climate using coupled climate–land surface models should include dynamic crop growth and realistic irrigation schemes to better capture land surface effects in agricultural regions.

Surface energy partitioning over four dominant vegetation types across the United States in a coupled regional climate model (Weather Research and Forecasting Model 3–Community Land Model 3.5)

[1] Accurate representation of surface energy partitioning is crucial for studying land surface processes and the climatic influence of land cover and land use change using coupled climate-land surface models. A critical question for these models, especially for newly coupled ones, is whether they can adequately distinguish differences in surface energy partitioning among different vegetation types. We evaluated 3 years (2004–2006) of surface energy partitioning and surface climate over four dominant vegetation types (cropland, grassland, needleleaf evergreen forest, and broadleaf deciduous forest) across the United States in a recently coupled regional climate model, Weather Research and Forecasting Model 3–Community Land Model 3.5 (WRF3-CLM3.5), by comparing model output to observations (AmeriFlux, Clouds and the Earth’s Radiant Energy System (CERES), and Parameter-elevation Regressions on Independent Slopes Model (PRISM) data) and to standard WRF model output. We found that WRF3-CLM3.5 can capture the seasonal pattern in energy partitioning for needleleaf evergreen forest but needs improvements in cropland, grassland, and broadleaf deciduous forest. Correcting the leaf area index representation for cropland and grassland could immediately improve the simulation of latent heat flux and hence the energy partitioning. Adding an irrigation scheme is especially important for cropland in the Midwest, where the strongly coupled soil moisture and precipitation can form a positive feedback that reduces latent heat flux and increases the warm bias. For deciduous forest, the simulated excess latent heat flux before leaf emergence is mainly from soil evaporation, requiring further improvement in the soil evaporation scheme. Finally, the domain-wide overestimated net radiation contributes to positive biases in sensible, latent, and ground heat flux, as well as surface temperature. The standard WRF simulation has a similar warm bias, implicating errors in modules other than the land surface code. A sensitivity test suggests that improved simulation of downward solar radiation could reduce the energy flux and temperature biases. After adding irrigation process and correcting the leaf area index, WRF3-CLM3.5 appears reliable for studying conversions between natural grassland and irrigated cropland and between needleleaf evergreen forest and grassland.

Effects of Dynamic Crop Growth on the Simulated Precipitation Response to Irrigation

AbstractThe rapid expansion of irrigation since the 1950s has significantly depleted the Ogallala Aquifer. This study examines the warm-season climate impacts of irrigation over the Ogallala using high-resolution (6.33 km) simulations of a version of the Weather Research and Forecasting (WRF) Model that has been coupled to the Community Land Model with dynamic crop growth (WRF-CLM4crop). To examine how dynamic crops influence the simulated impact of irrigation, the authors compare simulations with dynamic crops to simulations with a fixed annual cycle of crop leaf area index (static crops). For each crop scheme, simulations were completed with and without irrigation for 9 years that represent the range of observed precipitation. Reduced temperature and precipitation biases occur with dynamic versus static crops. Fundamental differences in the precipitation response to irrigation occur with dynamic crops, as enhanced surface roughness weakens low-level winds, enabling more water from irrigation to remain o...

Increased heat waves with loss of irrigation in the United States

A potential decline in irrigation due to groundwater depletion or insufficient surface water would not only directly affect agriculture, but also could alter surface climate. In this study we investigated how loss of irrigation affects heat wave frequency, duration, and intensity across fifteen heat wave indices (HINs) using a regional climate model that incorporated dynamic crop growth. Averaged across all indices, loss of irrigation increased heat wave frequency, duration, and intensity. In the United States, irrigation effects on heat waves were statistically significant over irrigated cropland for the majority of HINs, but in non-irrigated regions, the effects were significant only for a few HINs. The heat index temperature metrics that include humidity were less sensitive to loss of irrigation due to the trade-off between increased temperature and decreased humidity. Using the same temperature metric but different temperature thresholds resulted in qualitatively similar effects on heat waves. Regions experiencing strong groundwater depletion, such as the southern high plains, may suffer more and longer heat waves with reduced irrigation.

A Cost of Tractability? Estimating Climate Change Impacts Using a Single Crop Market Understates Impacts on Market Conditions and Variability

Scientists estimate that U.S. Corn Belt crop yields will increase or decrease, on average, and become more variable with climate change. Corn and soybean farming dominates this region, but studies typically do not assess the joint impact of new distributions of corn and soybean yields on markets. We use a structural economic model with projections of climate‐driven yield changes to simulate these effects. Our findings suggest that a narrow focus on a single crop in this key growing region risks underestimating the impact on price distributions and average crop receipts, and can lead to incorrect signs on estimated impacts.

Plant Uptake of Atmospheric Carbonyl Sulfide in Coast Redwood Forests

Author(s): Campbell, JE; Whelan, ME; Berry, JA; Hilton, TW; Zumkehr, A; Stinecipher, J; Lu, Y; Kornfeld, A; Seibt, U; Dawson, TE; Montzka, SA; Baker, IT; Kulkarni, S; Wang, Y; Herndon, SC; Zahniser, MS; Commane, R; Loik, ME | Abstract: ©2017. American Geophysical Union. All Rights Reserved. The future resilience of coast redwoods (Sequoia sempervirens) is now of critical concern due to the detection of a 33% decline in California coastal fog over the 20th century. However, ecosystem-scale measurements of photosynthesis and stomatal conductance are challenging in coast redwood forests, making it difficult to anticipate the impacts of future changes in fog. To address this methodological problem, we explore coastal variations in atmospheric carbonyl sulfide (COS or OCS), which could potentially be used as a tracer of these ecosystem processes. We conducted atmospheric flask campaigns in coast redwood sites, sampling at surface heights and in the canopy (~70 m), at the University of California Landels-Hill Big Creek Reserve and Big Basin State Park. We simulated COS atmosphere-biosphere exchange with a high-resolution 3-D model to interpret these data. Flask measurements indicated a persistent daytime drawdown between the coast and the downwind forest (45 ± 6 ppt COS) that is consistent with the expected relationship between COS plant uptake, stomatal conductance, and gross primary production. Other sources and sinks of COS that could introduce noise to the COS tracer technique (soils, anthropogenic activity, nocturnal plant uptake, and surface hydrolysis on leaves) are likely to be small relative to daytime COS plant uptake. These results suggest that COS measurements may be useful for making ecosystem-scale estimates of carbon, water, and energy exchange in coast redwood forests.

Future crop yields and water productivity changes for Nebraska rainfed and irrigated crops

ABSTRACT We assessed future rainfed and irrigated crop yield and water productivity changes in Nebraska across multiple climate and emission scenarios using an empirical modeling approach. We found rainfed crops showed slightly increased crop water productivity while irrigated crops showed no change or decreased water productivity. Contrary to U.S.-wide studies reporting declines in crop yields, we projected Nebraska crop yields to increase overall with greatest increases in current rainfed fields due to combined effects from maximum and minimum temperatures. However, the increased rainfed yields are not sufficient to fully close the gap between rainfed and irrigated yields. Abbreviations: USDA: U.S. Department of Agriculture; RegCM4.3: ICTP Regional Climate Model version 4.3; NCEP: National Centers for Environmental prediction; DOE: U.S. Department of Energy; CGCM: Canadian Climate Centre general circulation model; GFDL: Geophysical Fluid Dynamics Laboratory general circulation model; CRCM: Canadian Climate Centre regional climate model; CCSM: National Center for Atmospheric Research general circulation model; HRM3: Hadley Centre’s Regional Model 3; HADCM3: Hadley Centre’s general circulation model; WRFG: the NCAR Weather Research and Forecasting model; CCCma: Canadian Centre for Climate Modelling and Analysis; CanESM2: Canadian Centre Earth System Model 2; ICHEC-EC: A European community Earth-System Model; IPCC: Intergovernmental Panel on Climate Change; RMSE: Root Mean Square Error

Irrigation Effects on Land–Atmosphere Coupling Strength in the United States

AbstractLand–atmosphere coupling strength describes the degree to which the atmosphere responds (e.g., via changes in precipitation) to changes in the land surface state (e.g., soil moisture). The Midwest and Great Plains of the United States have been shown to be “hot spots” of coupling by many climate models and some observations. However, very few of the modeling studies have reported whether the climate models applied irrigation in the Midwest and Great Plains, where 24%–27% of farmland is irrigated, leaving open the question of whether irrigation affects current estimates of coupling strength. This study used a regional climate model that incorporated dynamic crop growth and precision irrigation (WRF3.3–CLM4crop) to investigate irrigation effects on land–atmosphere coupling strength. Coupling strength was quantified using multiple indices and the irrigated land-induced precipitation was tracked using a back trajectory method. The indices showed a consistent and significant decline in local coupling s...