Johannes J. Feddema

The Importance of Land-Cover Change in Simulating Future Climates

Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.

Predicted change in global secondary organic aerosol concentrations in response to future climate, emissions, and land use change

[1] The sensitivity of secondary organic aerosol (SOA) concentration to changes in climate and emissions is investigated using a coupled global atmosphere-land model driven by the year 2100 IPCC A1B scenario predictions. The Community Atmosphere Model (CAM3) is updated with recent laboratory determined yields for SOA formation from monoterpene oxidation, isoprene photooxidation and aromatic photooxidation. Biogenic emissions of isoprene and monoterpenes are simulated interactively using the Model of Emissions of Gases and Aerosols (MEGAN2) within the Community Land Model (CLM3). The global mean SOA burden is predicted to increase by 36% in 2100, primarily the result of rising biogenic and anthropogenic emissions which independently increase the burden by 26% and 7%. The later includes enhanced biogenic SOA formation due to increased emissions of primary organic aerosol (5–25% increases in surface SOA concentrations in 2100). Climate change alone (via temperature, removal rates, and oxidative capacity) does not change the global mean SOA production, but the global burden increases by 6%. The global burden of anthropogenic SOA experiences proportionally more growth than biogenic SOA in 2100 from the net effect of climate and emissions (67% increase predicted). Projected anthropogenic land use change for 2100 (A2) is predicted to reduce the global SOA burden by 14%, largely the result of cropland expansion. South America is the largest global source region for SOA in the present day and 2100, but Asia experiences the largest relative growth in SOA production by 2100 because of the large predicted increases in Asian anthropogenic aromatic emissions. The projected decrease in global sulfur emissions implies that SOA will contribute a progressively larger fraction of the global aerosol burden.

The role of climate in a pine forest regeneration pulse in the southwestern United States

Abstract:The role of historical influences in patterning forest landscapes was explored in a case study of forest structure change in the American Southwest. A group of ponderosa pine trees was destructively sampled and year of germination identified in order to assess the strength and nature of the climate signal in influencing ponderosa pine germination in this century. A novel methodology for estimating year of germination in woody species by identifying the root-shoot boundary appears to be promising. Both rare seasonal and interannual climatic factors and a unique set of circumstances associated with anthropogenic disturbances played a role in shaping a germination pulse early in the 20th century. A cohort originating in 1919 captured available space and, barring major disturbance, will dominate forest structure at the site for centuries. Such rare germination events support the view that forest communities are essentially dynamic and non-equilibrial over the long-term.

Simulating the Biogeochemical and Biogeophysical Impacts of Transient Land Cover Change and Wood Harvest in the Community Climate System Model (CCSM4) from 1850 to 2100

AbstractTo assess the climate impacts of historical and projected land cover change in the Community Climate System Model, version 4 (CCSM4), new time series of transient Community Land Model, version 4 (CLM4) plant functional type (PFT) and wood harvest parameters have been developed. The new parameters capture the dynamics of the Coupled Model Intercomparison Project phase 5 (CMIP5) land cover change and wood harvest trajectories for the historical period from 1850 to 2005 and for the four representative concentration pathway (RCP) scenarios from 2006 to 2100. Analysis of the biogeochemical impacts of land cover change in CCSM4 reveals that the model produced a historical cumulative land use flux of 127.7 PgC from 1850 to 2005, which is in general agreement with other global estimates of 156 PgC for the same period. The biogeophysical impacts of the transient land cover change parameters were cooling of the near-surface atmosphere over land by −0.1°C, through increased surface albedo and reduced shortwa...

An Urban Parameterization for a Global Climate Model. Part I: Formulation and Evaluation for Two Cities

Abstract Urbanization, the expansion of built-up areas, is an important yet less-studied aspect of land use/land cover change in climate science. To date, most global climate models used to evaluate effects of land use/land cover change on climate do not include an urban parameterization. Here, the authors describe the formulation and evaluation of a parameterization of urban areas that is incorporated into the Community Land Model, the land surface component of the Community Climate System Model. The model is designed to be simple enough to be compatible with structural and computational constraints of a land surface model coupled to a global climate model yet complex enough to explore physically based processes known to be important in determining urban climatology. The city representation is based upon the “urban canyon” concept, which consists of roofs, sunlit and shaded walls, and canyon floor. The canyon floor is divided into pervious (e.g., residential lawns, parks) and impervious (e.g., roads, par...

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

(c) American Geophysical Union. This article can be found on the publishers website at http://dx.doi.org/10.1029/2009GL042194

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

In 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.

Evaluating the Usability of a Tool for Visualizing the Uncertainty of the Future Global Water Balance

We describe the development of software that is intended to enable decision makers (and their scientific advisors) to visualize uncertainties associated with the future global water balance. This is an important task because the future water balance is a function of numerous factors that are not precisely known, including the historical climatology, the model of potential evapotranspiration, the soil water holding capacity, and the global circulation models (GCMs) used to predict the effect of increased CO2 in the atmosphere. In developing the software, we utilized the principles of usability engineering. In our case, we utilized six steps: prototype development, evaluation by domain experts, software revision, evaluation by usability experts, software revision, and evaluation by decision makers. Although this approach led to an improved piece of software, decision makers should have been involved earlier in the software design process, possibly at step two (instead of the domain experts). Decision makers found the notion of uncertainty discomforting, but their positive comments regarding the software suggest that it could prove beneficial, especially with improvements in spatial and temporal resolution. One interesting characteristic of our approach was the utilization of a wall-size display measuring 25 x 6 feet. The wall-size display engendered great interest, but determining whether it is truly effective will require a study that directly compares it with more traditional approaches.

Interannual variations of the grassland boundaries bordering the eastern edges of the Gobi Desert in central Asia

The Mongolian Steppe that borders the northern and eastern edges of the Gobi Desert in central Asia is one of the worlds largest grasslands, extending across the nation of Mongolia and the Inner Mongolian Autonomous Region (IMAR) of China. Recent findings show that this region has one of the strongest warming signals on Earth since the late 1970s. The objective of this study was to evaluate the relationships between climate and interannual variation of the grassland boundaries in Mongolia and IMAR between 1982 and 1990. The remote sensing data used in this study were the 15–day maximum Normalized Difference Vegetation Index (NDVI) composites derived from the Global Area Coverage of the Advanced Very High Resolution Radiometer (AVHRR). Monthly precipitation, mean monthly temperature, and monthly actual evapotranspiration (AE) were derived from meteorological station records acquired during the study period across the eastern Mongolian Steppe. The occurrence of onset of green–up, as determined with time-series NDVI data, was used to identify vegetated and non-vegetated areas. Great interannual variation of the Gobi boundary position was observed over the study period. This boundary variation was largely controlled by the climate before the growing season (the ‘preseason’ climate). Along the eastern edge of the Gobi desert in central IMAR, preseason AE was the major climatic factor affecting the annual shift of the Gobi boundary, while further north in Mongolia, preseason temperature was the driving climatic factor. Our findings suggest that the response of vegetation communities to climate changes varied as a function of land-use intensity within the ecosystem.

Influence of spatially variable instrument networks on climatic averages

Instrument networks for measuring surface air temperature (T) and precipitation (P)have varied consid- erably 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 Willmort, 1990a; 1990b) are intensively sampled and inte- grated 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.