Dev Niyogi

The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements

[1] This first paper of the two‐part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah‐MP). The Noah‐MP’s performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long‐term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture‐groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local‐scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah‐MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah‐MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah‐MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah‐MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.

An overview of regional land-use and land-cover impacts on rainfall

This paper documents the diverse role of land-use/land-cover change on precipitation. Since land conversion continues at a rapid pace, this type of human disturbance of the climate system will continue and become even more significant in the coming decades.

Description and Evaluation of the Characteristics of the NCAR High-Resolution Land Data Assimilation System

Abstract This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized...

Impacts of land use/land cover change on climate and future research priorities.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Changes in moisture and energy fluxes due to agricultural land use and irrigation in the Indian Monsoon Belt

[1] We present a conceptual synthesis of the impact that agricultural activity in India can have on land-atmosphere interactions through irrigation. We illustrate a ‘‘bottom up’’ approach to evaluate the effects of land use change on both physical processes and human vulnerability. We compared vapor fluxes (estimated evaporation and transpiration) from a pre-agricultural and a contemporary land cover and found that mean annual vapor fluxes have increased by 17% (340 km 3 ) with a 7% increase (117 km 3 ) in the wet season and a 55% increase (223 km 3 ) in the dry season. Two thirds of this increase was attributed to irrigation, with groundwater-based irrigation contributing 14% and 35% of the vapor fluxes in the wet and dry seasons, respectively. The area averaged change in latent heat flux across India was estimated to be 9 Wm 2 . The largest increases occurred where both cropland and irrigated lands were the predominant contemporary land uses. Citation: Douglas, E. M., D. Niyogi, S. Frolking, J. B. Yeluripati, R. A. Pielke Sr.,

Effect of Land–Atmosphere Interactions on the IHOP 24–25 May 2002 Convection Case

Abstract Numerical simulations are conducted using the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to investigate the impact of land–vegetation processes on the prediction of mesoscale convection observed on 24–25 May 2002 during the International H2O Project (IHOP_2002). The control COAMPS configuration uses the Weather Research and Forecasting (WRF) model version of the Noah land surface model (LSM) initialized using a high-resolution land surface data assimilation system (HRLDAS). Physically consistent surface fields are ensured by an 18-month spinup time for HRLDAS, and physically consistent mesoscale fields are ensured by a 2-day data assimilation spinup for COAMPS. Sensitivity simulations are performed to assess the impact of land–vegetative processes by 1) replacing the Noah LSM with a simple slab soil model (SLAB), 2) adding a photosynthesis, canopy resistance/transpiration scheme [the gas exchange/photosynthesis-based evapotranspiration model (GEM)] to the Noah LSM, and 3) repla...

Urban Modification of Thunderstorms: An Observational Storm Climatology and Model Case Study for the Indianapolis Urban Region*

AbstractA radar-based climatology of 91 unique summertime (May 2000–August 2009) thunderstorm cases was examined over the Indianapolis, Indiana, urban area. The study hypothesis is that urban regions alter the intensity and composition/structure of approaching thunderstorms because of land surface heterogeneity. Storm characteristics were studied over the Indianapolis region and four peripheral rural counties approximately 120 km away from the urban center. Using radar imagery, the time of event, changes in storm structure (splitting, initiation, intensification, and dissipation), synoptic setting, orientation, and motion were studied. It was found that more than 60% of storms changed structure over the Indianapolis area as compared with only 25% over the rural regions. Furthermore, daytime convection was most likely to be affected, with 71% of storms changing structure as compared with only 42% at night. Analysis of radar imagery indicated that storms split closer to the upwind urban region and merge aga...

Evaluation of Temperature and Precipitation Trends and Long-Term Persistence in CMIP5 Twentieth-Century Climate Simulations

AbstractThe authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-ter...

Documentation of Uncertainties and Biases Associated with Surface Temperature Measurement Sites for Climate Change Assessment

The objective of this research is to determine whether poorly sited long-term surface temperature monitoring sites have been adjusted in order to provide spatially representative independent data for use in regional and global surface temperature analyses. We present detailed analyses that demonstrate the lack of independence of the poorly sited data when they are adjusted using the homogenization procedures employed in past studies, as well as discuss the uncertainties associated with undocumented station moves. We use simulation and mathematics to determine the effect of trend on station adjustments and the associated effect of trend in the reference series on the trend of the adjusted station. We also compare data before and after adjustment to the reanalysis data, and we discuss the effect of land use changes on the uncertainty of measurement. A major conclusion of our analysis is that there are large uncertainties associated with the surface temperature trends from the poorly sited stations. Moreover...

2012 Midwest Drought in the United States

The 2012 North American drought may be the costliest and one of the most widespread natural disasters in U.S. history [USDA Economic Research Service (USDA-ERS) 2012]. While several states across the United States were experiencing drought conditions to varying degrees of severity, the Midwest and Northern Plains were perhaps the most affected. The drought severely impacted agricultural activities across the United States, particularly corn and soybean crops, prompting federal agencies including U.S. Department of Agriculture to declare disaster areas (USDA 2012b) and to provide assistance to those affected by this calamity. This paper utilizes existing and new techniques to provide insights into the severity of the 2012 Midwest drought and its impacts over the region.