Bridging the Gap Between the iLEAPS and GEWEX Land-Surface Modeling Communities

Models of Earth's weather and climate require fluxes of momentum, energy, and moisture across the land-atmosphere interface to solve the equations of atmospheric physics and dynamics. Just as atmospheric models can, and do, differ between weather and climate applications, mostly related to issues of scale, resolved or parameterised physics,and computational requirements, so too can the land models that provide the required surface fluxes differ between weather and climate models. Here, however, the issue is less one of scale-dependent parameterisations.Computational demands can influence other minor land model differences, especially with respect to initialisation, data assimilation, and forecast skill. However, the distinction among land models (and their development and application) is largely driven by the different science and research needs of the weather and climate communities

Effects of white roofs on urban temperature in a global climate model

(c) American Geophysical Union. This article can be found on the publisher's website at http://dx.doi.org/10.1029/2009GL042194Increasing the albedo of urban surfaces has received attention as a strategy to mitigate urban heat islands. Here, the effects of globally installing white roofs are assessed using an urban canyon model coupled to a global climate model. Averaged over all urban areas, the annual mean heat island decreased by 33%. Urban daily maximum temperature decreased by 0.6°C and daily minimum temperature by 0.3°C. Spatial variability in the heat island response is caused by changes in absorbed solar radiation and specification of roof thermal admittance. At high latitudes in winter, the increase in roof albedo is less effective at reducing the heat island due to low incoming solar radiation, the high albedo of snow intercepted by roofs, and an increase in space heating that compensates for reduced solar heating. Global space heating increased more than air conditioning decreased, suggesting that end-use energy costs must be considered in evaluating the benefits of white roofs

Evaluating the climate effects of mid-1800s deforestation in New England, USA, using a Weather, Research, and Forecasting (WRF) Model Multi-Physics Ensemble

The New England region of the northeastern United States has a land use history characterized by forest clearing for agriculture and other uses during European colonization and subsequent reforestation following widespread farm abandonment. Despite these broad changes, the potential influence on local and regional climate has received relatively little attention. This study investigated wintertime (December through March) climate impacts of reforestation in New England using a high-resolution (4 km) multiphysics ensemble of the Weather Research and Forecasting Model. In general, the conversion from mid-1800s cropland/grassland to forest led to warming, but results were sensitive to physics parameterizations. The 2-m maximum temperature (T2max) was most sensitive to choice of land surface model, 2-m minimum temperature (T2min) was sensitive to radiation scheme, and all ensemble members simulated precipitation poorly. Reforestation experiments suggest that conversion of mid-1800s cropland/grassland to present-day forest warmed T2max +0.5 to +3 K, with weaker warming during a warm, dry winter compared to a cold, snowy winter. Warmer T2max over forests was primarily the result of increased absorbed shortwave radiation and increased sensible heat flux compared to cropland/grassland. At night, T2min warmed +0.2 to +1.5 K where deciduous broadleaf forest replaced cropland/grassland, a result of decreased ground heat flux. By contrast, T2min of evergreen needleleaf forest cooled –0.5 to –2.1 K, primarily owing to increased ground heat flux and decreased sensible heat flux

Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0)

Land surface models used in climate models neglect the roughness sublayer and parameterize within-canopy turbulence in an ad hoc manner. We implemented a roughness sublayer turbulence parameterization in a multilayer canopy model (CLM-ml v0) to test if this theory provides a tractable parameterization extending from the ground through the canopy and the roughness sublayer. We compared the canopy model with the Community Land Model (CLM4.5) at seven forest, two grassland, and three cropland AmeriFlux sites over a range of canopy heights, leaf area indexes, and climates. CLM4.5 has pronounced biases during summer months at forest sites in midday latent heat flux, sensible heat flux, gross primary production, nighttime friction velocity, and the radiative temperature diurnal range. The new canopy model reduces these biases by introducing new physics. Advances in modeling stomatal conductance and canopy physiology beyond what is in CLM4.5 substantially improve model performance at the forest sites. The signature of the roughness sublayer is most evident in nighttime friction velocity and the diurnal cycle of radiative temperature, but is also seen in sensible heat flux. Within-canopy temperature profiles are markedly different compared with profiles obtained using Monin–Obukhov similarity theory, and the roughness sublayer produces cooler daytime and warmer nighttime temperatures. The herbaceous sites also show model improvements, but the improvements are related less systematically to the roughness sublayer parameterization in these canopies. The multilayer canopy with the roughness sublayer turbulence improves simulations compared with CLM4.5 while also advancing the theoretical basis for surface flux parameterizations

The role of surface roughness, albedo, and Bowen ratio on ecosystem energy balance in the Eastern United States

Land cover and land use influence surface climate through differences in biophysical surface properties, including partitioning of sensible and latent heat (e.g., Bowen ratio), surface roughness, and albedo. Clusters of closely spaced eddy covariance towers (e.g., \u3c10 \u3ekm) over a variety of land cover and land use types provide a unique opportunity to study the local effects of land cover and land use on surface temperature. We assess contributions albedo, energy redistribution due to differences in surface roughness and energy redistribution due to differences in the Bowen ratio using two eddy covariance tower clusters and the coupled (land-atmosphere) Variable-Resolution Community Earth System Model. Results suggest that surface roughness is the dominant biophysical factor contributing to differences in surface temperature between forested and deforested lands. Surface temperature of open land is cooler (−4.8 °C to −0.05 °C) than forest at night and warmer (+0.16 °C to +8.2 °C) during the day at northern and southern tower clusters throughout the year, consistent with modeled calculations. At annual timescales, the biophysical contributions of albedo and Bowen ratio have a negligible impact on surface temperature, however the higher albedo of snow-covered open land compared to forest leads to cooler winter surface temperatures over open lands (−0.4 °C to −0.8 °C). In both the models and observation, the difference in mid-day surface temperature calculated from the sum of the individual biophysical factors is greater than the difference in surface temperature calculated from radiative temperature and potential temperature. Differences in measured and modeled air temperature at the blending height, assumptions about independence of biophysical factors, and model biases in surface energy fluxes may contribute to daytime biases

An Urban Parameterization for a Global Climate Model. Part II: Sensitivity to Input Parameters and the Simulated Urban Heat Island in Offline Simulations

© 2008 American Meteorological SocietyIn a companion paper, the authors presented a formulation and evaluation of an urban parameterization designed to represent the urban energy balance in the Community Land Model. Here the robustness of the model is tested through sensitivity studies and the model’s ability to simulate urban heat islands in different environments is evaluated. Findings show that heat storage and sensible heat flux are most sensitive to uncertainties in the input parameters within the atmospheric and surface conditions considered here. The sensitivity studies suggest that attention should be paid not only to characterizing accurately the structure of the urban area (e.g., height-to-width ratio) but also to ensuring that the input data reflect the thermal admittance properties of each of the city surfaces. Simulations of the urban heat island show that the urban model is able to capture typical observed characteristics of urban climates qualitatively. In particular, the model produces a significant heat island that increases with height-to-width ratio. In urban areas, daily minimum temperatures increase more than daily maximum temperatures, resulting in a reduced diurnal temperature range relative to equivalent rural environments. The magnitude and timing of the heat island vary tremendously depending on the prevailing meteorological conditions and the characteristics of surrounding rural environments. The model also correctly increases the Bowen ratio and canopy air temperatures of urban systems as impervious fraction increases. In general, these findings are in agreement with those observed for real urban ecosystems. Thus, the model appears to be a useful tool for examining the nature of the urban climate within the framework of global climate models

An examination of urban heat island characteristics in a global climate model

This is the publisher's version, also available electronically from http://onlinelibrary.wiley.com/doi/10.1002/joc.2201/abstract;jsessionid=8D053A7D1E2894F4658DDA991ACAB056.f04t03.A parameterization for urban surfaces has been incorporated into the Community Land Model as part of the Community Climate System Model. The parameterization allows global simulation of the urban environment, in particular the temperature of cities and thus the urban heat island. Here, the results from climate simulations for the AR4 A2 emissions scenario are presented. Present-day annual mean urban air temperatures are up to 4 °C warmer than surrounding rural areas. Averaged over all urban areas resolved in the model, the heat island is 1.1 °C, which is 46% of the simulated mid-century warming over global land due to greenhouse gases. Heat islands are generally largest at night as evidenced by a larger urban warming in minimum than maximum temperature, resulting in a smaller diurnal temperature range compared to rural areas. Spatial and seasonal variability in the heat island is caused by urban to rural contrasts in energy balance and the different responses of these surfaces to the seasonal cycle of climate. Under simulation constraints of no urban growth and identical urban/rural atmospheric forcing, the urban to rural contrast decreases slightly by the end of the century. This is primarily a different response of rural and urban areas to increased long-wave radiation from a warmer atmosphere. The larger storage capacity of urban areas buffers the increase in long-wave radiation such that urban night-time temperatures warm less than rural. Space heating and air conditioning processes add about 0.01 W m−2 of heat distributed globally, which results in a small increase in the heat island. The significant differences between urban and rural surfaces demonstrated here imply that climate models need to account for urban surfaces to more realistically evaluate the impact of climate change on people in the environment where they live. Copyright © 2010 Royal Meteorological Societ

Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum

The Ball–Berry stomatal conductance model is commonly used in earth system models to simulate biotic regulation of evapotranspiration. However, the dependence of stomatal conductance (<i>g</i>_s) on vapor pressure deficit (<i>D</i>_s) and soil moisture must be empirically parameterized. We evaluated the Ball–Berry model used in the Community Land Model version 4.5 (CLM4.5) and an alternative stomatal conductance model that links leaf gas exchange, plant hydraulic constraints, and the soil–plant–atmosphere continuum (SPA). The SPA model simulates stomatal conductance numerically by (1) optimizing photosynthetic carbon gain per unit water loss while (2) constraining stomatal opening to prevent leaf water potential from dropping below a critical minimum. We evaluated two optimization algorithms: intrinsic water-use efficiency (&Delta;<i>A</i>_n /&Delta;<i>g</i>_s, the marginal carbon gain of stomatal opening) and water-use efficiency (&Delta;<i>A</i>_n /&Delta;<i>E</i>_l, the marginal carbon gain of transpiration water loss). We implemented the stomatal models in a multi-layer plant canopy model to resolve profiles of gas exchange, leaf water potential, and plant hydraulics within the canopy, and evaluated the simulations using leaf analyses, eddy covariance fluxes at six forest sites, and parameter sensitivity analyses. The primary differences among stomatal models relate to soil moisture stress and vapor pressure deficit responses. Without soil moisture stress, the performance of the SPA stomatal model was comparable to or slightly better than the CLM Ball–Berry model in flux tower simulations, but was significantly better than the CLM Ball–Berry model when there was soil moisture stress. Functional dependence of <i>g</i>_s on soil moisture emerged from water flow along the soil-to-leaf pathway rather than being imposed a priori, as in the CLM Ball–Berry model. Similar functional dependence of <i>g</i>_s on <i>D</i>_s emerged from the &Delta;<i>A</i>_n/&Delta;<i>E</i>_l optimization, but not the &Delta;<i>A</i>_n /<i>g</i>_s optimization. Two parameters (stomatal efficiency and root hydraulic conductivity) minimized errors with the SPA stomatal model. The critical stomatal efficiency for optimization (&iota;) gave results consistent with relationships between maximum <i>A</i>_n and <i>g</i>_s seen in leaf trait data sets and is related to the slope (<i>g</i>₁) of the Ball–Berry model. Root hydraulic conductivity (<i>R</i>_r^*) was consistent with estimates from literature surveys. The two central concepts embodied in the SPA stomatal model, that plants account for both water-use efficiency and for hydraulic safety in regulating stomatal conductance, imply a notion of optimal plant strategies and provide testable model hypotheses, rather than empirical descriptions of plant behavior

The signature of internal variability in the terrestrial carbon cycle

Uncertainty in model initial states produces uncertainty in climate simulations because of unforced variability internal to the climate system. Climate scientists use initial-condition ensembles to separate the forced signal of climate change from the unforced internal variability. Our analysis of an 11-member initial-condition ensemble from the Community Earth System Model Version 2 that spans the period 1850&ndash;2014 shows that a similar ensemble approach is needed to robustly assess trends in the terrestrial carbon cycle. Uncertainty in model initialization gives rise to internal variability that masks trends in carbon fluxes, and also creates spurious unforced trends, during the period 1960&ndash;2014 across North America, meaning that a single model realization can diverge from the observational record or from other models simply because of random behavior. The forced response is, however, evident in the ensemble mean and emerges from the noise of unforced variability at decadal timescales. Our results suggest that trends in the observational record must be interpreted with caution because of multiple possible histories that would have been observed if the sequence of internal variability had unfolded differently. Furthermore, internal variability produces irreducible uncertainty in the carbon cycle, leading to ambiguity in the magnitude and sign of carbon cycle trends, especially at small spatial scales and short timescales. The small spread in initial land carbon pools at 1850 suggests that internal climate variability arising from atmospheric and oceanic initialization, not the biogeochemical initialization, is the predominant cause of carbon cycle variability among ensemble members. Initial-condition ensembles with other Earth system models are needed to develop a multi-model understanding of internal variability in the terrestrial carbon cycle. &nbsp;</p

GLACE: The Global Land-Atmosphere Coupling Experiment

GLACE is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land-atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The twelve AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, enough similarity to pinpoint multi-model "hot spots" of land-atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model s coupling strength within the broad range of those documented here