Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models

How can we use MODIS land surface temperature to validate long-term urban model simulations?

This is the authors accepted manuscript. The published version is available here: http://dx.doi.org/10.1002/2013JD021101.High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.Funded by National Aeronautics and Space Administration. Grant Number: (NNX10AK79G

Year in review in Intensive Care Medicine 2009: I. Pneumonia and infections, sepsis, outcome, acute renal failure and acid base, nutrition and glycaemic control

Journal ArticleReviewSCOPUS: re.jinfo:eu-repo/semantics/publishe

The effects of land use change on the numerical modeling of regional climate and watershed runoff in the Great Lakes region.

Between 1895 and 1920 average air temperatures in the Midwest and United States declined by an amount similar to the increase observed from 1920 to 2000. The period 1895 to 1920 was also marked by land use changes from native forest and grassland to cropland. The investigation of a link between land use changes and climatic variability is the focus of this study. A mesoscale atmospheric model (MM5) was coupled with a puddle-modified Biosphere-Atmosphere Transfer Scheme (BATS) to model the regional climate. Verification simulations showed the BATS-modified MM5 was superior to the original MM5 in the prediction of surface water runoff and precipitation magnitude and distribution. To assess the effect of land use change on the summer climate of the Great Lakes region, four-month numerical experiments were completed using two land use scenarios, pre-settlement (1850) and current (1995). The results showed a significant modeled surface air temperature decrease in the latter period due to a repartitioning of the upward surface energy flux from sensible heat to evaporation. The modeled precipitation change was complex and appeared to be influenced very little by local land use change. However, there appeared to be a consistent modification of synoptic systems throughout the pre-settlement domain, with cold fronts and associated closed lows moving faster and isolated cold fronts moving more slowly. Clear, coherent mesoscale circulations formed over regions of abrupt, altered land use as expected due to surface heating but appear to have little effect on local precipitation. To assess the effects of future land use and climate change on the Huron River watershed, the BATS was further modified to include urban land use. The output from a global climate model was used to drive this version of the BATS. The simulations showed that the ten-year surface runoff would increase 28.9 inches from a current land use and climate scenario to a future land use and climate scenario. Factor separation analysis showed that 58% of this runoff increase would occur because of changing climate induced by higher intensity precipitation. The remainder of the increase was predominately due to forecasted land use changes.Ph.D.Physics, Atmospheric SciencePure SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124735/2/3016797.pd

Evaluating and Enhancing Snow Compaction Process in the Noah‐MP Land Surface Model

Abstract The accuracy of snow density in land surface model (LSM) simulations impacts the accuracy of simulated terrestrial water and energy budgets. However, there has been little research that has focused on enhancing snow compaction in operationally used LSMs. A baseline snow simulation with the widely used Noah‐MP LSM systematically overestimates snow depth by 55 mm even after removing daily snow water equivalent (SWE) biases. To reduce uncertainties associated with snow compaction, we enhance the most sensitive Noah‐MP snow compaction parameter—the empirical parameter for compaction due to overburden (Cbd)—such that Cbd is calculated as a function of surface air temperature as opposed to a fixed value in the baseline simulation. This enhancement improves accuracy in simulated snow compaction across the majority of western U.S. (WUS) SNOTEL test sites (biases reduced at 88% of test sites), with modest bias reductions in cooler accumulation periods (biases reduced at 70% of test sites) and substantial improvements during warmer ablation periods (biases reduced at 99% of test sites). Relatively larger improvements during warm conditions are attributable to the default Cbd value being reasonable for cold temperatures (≤−5°C). Improvements in simulated snow depth and density with observations outside of the training sites and optimization periods support that the snow compaction enhancement is transferable in space and time. Differences between enhanced and baseline gridded simulations across the total WUS support that the enhancement can have important impacts on snowpack evolution, snow albedo feedback, and snow hydrology

A Trial to Improve Surface Heat Exchange Simulation through Sensitivity Experiments over a Desert Steppe Site

It is still a daunting challenge for land surface models (LSMs) to correctly represent surface heat exchange for water-limited desert steppe ecosystems. This study aims to improve the ability of the Noah LSM to simulate surface heat fluxes through addressing uncertainties in precipitation forcing conditions, rapidly evolving vegetation properties, soil hydraulic properties (SHPs), and key parameterization schemes. Three years (2008-10) of observed surface heat fluxes and soil temperature over a desert steppe site in Inner Mongolia, China, are used to verify model simulations. The proper seasonal distribution of precipitation, along with more realistic vegetation parameters, can improve the simulation of sensible heat flux (SH) and the seasonal variability of latent heat flux. Correctly representing the low-surface exchange coefficient is crucial for improving SH for short vegetation like this desert steppe site. Relating C-zil, the coefficient in the Noah surface exchange coefficient calculation, with canopy height h improves the simulated SH and the diurnal range of soil temperature over the simulation compared with using the default constant C-zil. The exponential water stress formulation proposed here for the Jarvis scheme improves the partitioning between soil evaporation and transpiration. It is found that the surface energy fluxes are very sensitive to SHPs. This study highlights the important role of the proper parameter values and appropriate parameterizations for the surface exchange coefficient and water stress function in canopy resistance in capturing the observed surface energy fluxes and soil temperature variations for this desert steppe site

The Influence of Urbanization on the Development of a Convective Storm—A Study for the Belém Metropolitan Region, Brazil

One of the main problems faced by the Belém Metropolitan Region (BMR) inhabitants is flash floods caused by precarious infrastructure and extreme rainfall events. The objective of this article is to investigate whether and how the local urban characteristics may influence the development of thunderstorms. The Weather Research and Forecasting (WRF) model was used with three distinct configurations of land use/cover to represent urbanization scenarios in 2017 and 1986 and the forest-only scenario. The WRF model simulated reasonably well the event. The results showed that the urban characteristics of the BMR may have an impact on storm systems in the urban areas close to the Northern Coast of South America. In particular, for the urban characteristics in the BMR in 2017, the intensification of the storm may be linked to a higher value of energy available for convection (over 1000 J kg−1) and favorable wind convergence and vertical shear in the urban area (where the wind speed at the surface was more than 3 m s−1 slower than in the forest-only scenario). Meanwhile, the other land cover scenarios could not produce a similar storm due to lack of moisture, wind convergence/shear, or convective energy

Impacts of Land Cover and Soil Texture Uncertainty on Land Model Simulations Over the Central Tibetan Plateau

Land surface processes and their coupling to the atmosphere over the Tibetan Plateau (TP) play an important role in modulating the regional and global climate. Therefore, identifying and quantifying uncertainty in these land surface model (LSM) processes are essential for improving climate models. The specifications of land cover and soil texture types, intertwined with the uncertainties in associated vegetation and soil parameters in LSMs, are significant sources of uncertainty due to the lack of detailed land survey in the TP. To differentiate the effects of land cover or soil texture specifications in the Noah with Multiple Parameterizations (Noah-MP) LSM from the effects of uncertainties in the model parameters, this study first identified the most sensitive vegetation and soil parameters through global sensitivity analysis and then conducted parametric ensemble simulations using two land cover data sets and two soil texture data sets over the central TP to estimate their corresponding impacts on the overall model responses. The distinction level and the Kolmogorov-Smirnov test were then applied to assess the differences between the results from parametric ensemble simulations using different land cover or soil texture data sets. The results show that the simulated energy and water fluxes over the central TP are dominated by soil parameters. The canopy height is the most sensitive vegetation parameter, and the Clapp-Hornberger b parameter (the exponent in the function that relates soil water potential and water content) is the most sensitive soil parameter. Relative to the background parametric uncertainties, the Noah-MP LSM could not sufficiently distinguish the effects of changes between forested types or soil texture types, which highlight the need for further quantifying and reducing the parametric uncertainties in LSMs. Further analysis shows significant sensitivities of the distinction level and changes in model response to annual precipitation and vegetation fraction. This work provides a scientific reference for assessing the impacts of land cover or soil texture changes on Noah-MP simulations under future climate change conditions