Incorporating Urban Systems in Global Climate Models: The Role of GIScience

Abstract is speaker biography.Johannes' primary interest is in understanding the human impact on the Earth's surface, and the consequences of these actions on the environment. More specifically he wishes to understand anthropogenic impacts on climate, and how climate change affects the environment and society. Johannes is currently with researchers at the National Center for Atmospheric Research to implement these ideas into the Community Climate System model.# KU Department of Geography # Kansas Biological Survey # State of Kansas Data Access and Support Center (DASC) # KU Center for Remote Sensing of Ice Sheets (CReSIS) # KU Transportation Research Institute # KU Biodiversity Institute # KU Institute for Policy & Social Research # Kansas View Consortium # Western Air Maps # KU Libraries # The Coca-Cola Compan

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

Seasonal trends in air temperature and precipitation in IPCC AR4 GCM output for Kansas, USA: evaluation and implications

Understanding the impacts of future climate change in Kansas is important for agricultural and other socioeconomic sectors in the region. To quantify these impacts, seasonal trends in air temperature and precipitation patterns from decadally averaged monthly output of 21 global climate models under the Special Report on Emissions Scenarios A1B scenario used in the Intergovernmental Panel of Climate Change Assessment Report 4 are examined for six grid cells representing Kansas. To ascertain the performance of the models, we compared model output to kriged meteorological data from stations in the Global Historical Climate Network for the period from 1950 to 2000. Agreement between multimodel ensemble mean output and observations is very good for temperature (r2 all more than 0.99, root mean square errors range from 0.84 to 1.48°C) and good for precipitation (r2 ranging between 0.64 and 0.89, root mean square errors range from 322 to 1144 mm). Seasonal trends for the second half of the 20th century are generally not observed except in modelled temperature trends. Linear trends for the 21st century are significant for all seasons in all grid cells for temperature and many for precipitation. Results indicate that temperatures are likely to warm in all seasons, with the largest trends being on the order of 0.04 °C/year in summer and fall. Precipitation is likely to increase slightly in winter and decrease in summer and fall. These changes have profound implications for both natural ecosystems and agricultural land uses in the region. Copyright 2009 Royal Meteorological SocietyLand Institute Climate and Energy Project (NFP #49780-720) and the National Science Foundation EPSCoR program (NSF EPS #0553722

Influence of Spatially Variable Instrument Networks on Climatic Averages

Copyright 1991 by the American Geophysical Union.Instrument networks for measuring surface air temperature (T) and precipitation (P) have varied considerably over the last century. Inadequate observing‐station locations have produced incomplete, uneven, and biased samples of the spatial variability in climate and, in turn, terrestrial and global scale averages of T and P have been biased. New high‐resolution climatologies [Legates and Willmott, 1990a; 1990b] are intensively sampled and integrated to illustrate the effects of these nontrivial sampling biases. Since station networks may not represent spatial climatic variability adequately, their ability to represent climate through time is suspect

Global trends in visibility: implications for dust sources

There is a large uncertainty in the relative roles of human land use, climate change and carbon dioxide fertilization in changing desert dust source strength over the past 100 years, and the overall sign of human impacts on dust is not known. We used visibility data from meteorological stations in dusty regions to assess the anthropogenic impact on long term trends in desert dust emissions. We did this by looking at time series of visibility derived variables and their correlations with precipitation, drought, winds, land use and grazing. Visibility data are available at thousands of stations globally from 1900 to the present, but we focused on 357 stations with more than 30 years of data in regions where mineral aerosols play a dominant role in visibility observations. We evaluated the 1974 to 2003 time period because most of these stations have reliable records only during this time. We first evaluated the visibility data against AERONET aerosol optical depth data, and found that only in dusty regions are the two moderately correlated. Correlation coefficients between visibility-derived variables and AERONET optical depths indicate a moderate correlation (0.47), consistent with capturing about 20% of the variability in optical depths. Two visibility-derived variables appear to compare the best with AERONET observations: the fraction of observations with visibility less than 5 km (VIS5) and the surface extinction (EXT). Regional trends show that in many dusty places, VIS5 and EXT are statistically significantly correlated with the Palmer drought severity index (based on precipitation and temperature) or surface wind speeds, consistent with dust temporal variability being largely driven by meteorology. This is especially true for North African and Chinese dust sources, but less true in the Middle East, Australia or South America, where there are not consistent patterns in the correlations. Climate indices such as El Nino or the North Atlantic Oscillation are not correlated with visibility-derived variables in this analysis. There are few stations where visibility measures are correlated with cultivation or grazing estimates on a temporal basis, although this may be a function of the very coarse temporal resolution of the land use datasets. On the other hand, spatial analysis of the visibility data suggests that natural topographic lows are not correlated with VIS5 or EXT, but land use is correlated at a moderate level. This analysis is consistent with land use being important in some regions, but meteorology driving interannual variability during 1974–2003

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

Role of snow and glacier melt in controlling river hydrology in Liddar watershed (western Himalaya) under current and future climate

This is the publisher's version, also available electronically from http://onlinelibrary.wiley.com/doi/10.1029/2011WR011590/abstract.[1] Snowmelt and icemelt are believed to be important regulators of seasonal discharge of Himalayan rivers. To analyze the long term contribution of snowmelt and glacier/icemelt to river hydrology we apply a water budget model to simulate hydrology of the Liddar watershed in the western Himalaya, India for the 20th century (1901–2010) and future IPCC A1B climate change scenario. Long term (1901–2010) temperature and precipitation data in this region show a warming trend (0.08°C yr−1) and an increase in precipitation (0.28 mm yr−1), with a significant variability in seasonal trends. In particular, winter months have undergone the most warming, along with a decrease in precipitation rates; precipitation has increased throughout the spring. These trends have accelerated the melting and rapid disappearance of snow, causing a seasonal redistribution in the availability of water. Our model results show that about 60% of the annual runoff of the Liddar watershed is contributed from the snowmelt, while only 2% is contributed from glacier ice. The climate trend observed from the 1901 to 2010 time period and its impact on the availability of water will become significantly worse under the IPCC climate change scenarios. Our results suggest that there is a significant shift in the timing and quantity of water runoff in this region of the Himalayas due to snow distribution and melt. With greatly increased spring runoff and its reductions in summer potentially leading to reduced water availability for irrigation agriculture in summer

Research priorities in land use and land-cover change for the Earth system and integrated assessment modelling

Copyright © 2010 Royal Meteorological Society and Crown Copyright.This special issue has highlighted recent and innovative methods and results that integrate observations and modelling analyses of regional to global aspect of biophysical and biogeochemical interactions of land-cover change with the climate system. Both the Earth System and the Integrated Assessment modeling communities recognize the importance of an accurate representation of land use and land-cover change to understand and quantify the interactions and feedbacks with the climate and socio-economic systems, respectively. To date, cooperation between these communities has been limited. Based on common interests, this work discusses research priorities in representing land use and land-cover change for improved collaboration across modelling, observing and measurement communities. Major research topics in land use and land-cover change are those that help us better understand (1) the interaction of land use and land cover with the climate system (e.g. carbon cycle feedbacks), (2) the provision of goods and ecosystem services by terrestrial (natural and anthropogenic) land-cover types (e.g. food production), (3) land use and management decisions and (4) opportunities and limitations for managing climate change (for both mitigation and adaptation strategies)