Tropical deforestation: Modeling local‐ to regional‐scale climate change

Henderson‐Sellers, A.; Dickinson, Robert E.; Durbidge, T. B.; Kennedy, Patrick; McGuffie, K.; Pitman, Andrew J.

doi:10.1029/92jd02830

Cited by 404 publications

(195 citation statements)

References 35 publications

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“…Many studies on the effect of land-use change on climate consider modern vegetation compared to natural vegetation (Bonan, 1997), desertification (Charney et al, 1977) or tropical deforestation (in particular the Amazonian deforestation) (Henderson-Sellers et al, 1993). However, less is known about the impact of urbanisation, an extreme form of land-use change, on climate at a regional scale.…”

Section: Introductionmentioning

confidence: 99%

Modelling the impact of urbanisation on regional climate in the Greater London Area

Grawe

Thompson

Salmond

et al. 2012

Intl Journal of Climatology

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Urban areas have well-documented effects on climate, such as the urban heat island (UHI) effect, reduction of wind speeds, enhanced turbulence and boundary layer heights, and changes in cloud cover and precipitation. The aim of this study is to quantify the impact of the urban area of London on local and regional climate. This is achieved through the coupling of the non-hydrostatic mesoscale model METRAS with the sophisticated urban canopy scheme BEP. The model is configured for case studies of the London region, for typical UHI conditions, and the model results are evaluated using data from meteorological monitoring sites. This study develops a methodology to quantify the regional impact of urbanisation from numerical model results. The urban area, in its current form, is found to affect near surface temperature, the diurnal temperature range, the UHI, and the near surface wind speed and direction. For the selected cases, peak UHI intensities of up to 2.5 K are found during night time hours, with the timing and magnitude of the peak showing good agreement with previous experimental studies for London. The timing of the UHI peak intensity for the current urban land cover for London shows a good agreement with the results of measurements. A significant reduction in wind speed over the urban area was also simulated during both daytime and night time, due to the higher roughness of the city compared to the rural domain. The effect is shown to have a regional character, with both urban and surrounding rural areas demonstrating a significant impact. Thus, the UHI can not only be understood when focussing on local data, but the interaction with the surrounding needs to be considered.

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Section: Introductionmentioning

confidence: 99%

Modelling the impact of urbanisation on regional climate in the Greater London Area

Grawe

Thompson

Salmond

et al. 2012

Intl Journal of Climatology

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“…By changing the vegetation present in various grid cells of the underlying LSM, climate models have been used to explore the climatic consequences of historic changes in land use (Bonan, 1997) or changes in surface properties of large-scale features such as the Amazon and boreal forests (Bonan et al, 1992;Henderson-Sellers et al, 1993). More recently, researchers have modified the LSM component of several climate models to address potential climatic consequences of land use strategies (afforestation) aimed at reducing atmospheric CO 2 levels (Betts, 2000;Gibbard et al, 2005;Schaeffer et al, 2006;Bala et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration

Hollinger

Ollinger

Richardson

et al. 2010

Global Change Biology

153

106

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; Schmid, H. P.; Stoy, P. C.; Suyker, Andrew E.; and Verma, Shashi, "Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration" (2010 Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration AbstractVegetation albedo is a critical component of the Earth's climate system, yet efforts to evaluate and improve albedo parameterizations in climate models have lagged relative to other aspects of model development. Here, we calculated growing season albedos for deciduous and evergreen forests, crops, and grasslands based on over 40 site-years of data from the AmeriFlux network and compared them with estimates presently used in the land surface formulations of a variety of climate models. Generally, the albedo estimates used in land surface models agreed well with this data compilation. However, a variety of models using fixed seasonal estimates of albedo overestimated the growing season albedo of northerly evergreen trees. In contrast, climate models that rely on a common two-stream albedo submodel provided accurate predictions of boreal needle-leaf evergreen albedo but overestimated grassland albedos. Inverse analysis showed that parameters of the two-stream model were highly correlated. Consistent with recent observations based on remotely sensed albedo, the AmeriFlux dataset demonstrated a tight linear relationship between canopy albedo and foliage nitrogen concentration (for forest vegetation: albedo 5 0.01 1 0.071%N, r 2 5 0.91; forests, grassland, and maize: albedo 5 0.02 1 0.067%N, r 2 5 0.80). However, this relationship saturated at the higher nitrogen concentrations displayed by soybean foliage. We developed similar relationships between a foliar parameter used in the two-stream albedo model and foliage nitrogen concentration. These nitrogen-based relationships can serve as the basis for a new approach to land surface albedo modeling that simplifies albedo estimation while providing a link to other important ecosystem processes.

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“…affects regional climate through impacts on the surface albedo and radiative forcing (Forster et al, 2007), partitioning of available energy between sensible and latent heat, boundary layer temperature, moisture profile and depth and the partitioning of rainfall between evaporation and runoff (Betts et al, 1996;Pitman, 2003). The global and regional climate modelling communities have demonstrated impacts on surface temperature, rainfall, and turbulent energy fluxes if land cover is perturbed (Henderson- Sellers et al, 1993;Chase et al, 2000;Werth and Avissar, 2002;2005;Findell et al, 2006).…”

Section: Land Cover-climate Interactionsmentioning

confidence: 99%

Regionalizing global climate models

Pitman

Arneth

Ganzeveld

2011

Intl Journal of Climatology

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Global climate models simulate the Earth's climate impressively at scales of continents and greater. At these scales, large-scale dynamics and physics largely define the climate. At spatial scales relevant to policy makers, and to impacts and adaptation, many other processes may affect regional and local climate and perhaps trigger teleconnections that provide significant feedbacks on the global climate. These processes include fire, irrigation, land cover change (including crops and urban landscapes), and the emissions of biogenic volatile organic compounds by vegetation. Many of these interact within the atmosphere via dynamical, physical, and chemical mechanisms that lead to boundary-layer feedbacks. It is unlikely that any of these processes have a significant global-scale impact on the Earth's climate in the sense that the amount of warming due to a doubling of well mixed greenhouse gases would change if these processes were explicitly represented in climate models. These phenomena are usually local in space (e.g. urban) or in time (e.g. fire) and probably do not provide the on-going and sustained forcing to affect the global climate. However, for most impacts and adaptation research it is the regional and local climate that defines climate risk. At these scales, processes missing in climate models can have a substantially larger local-scale impact than the additional radiative forcing due to increasing greenhouse gases. Thus, while climate models are well designed for global and continental scales they exclude a suite of important processes that are locally and/or regionally important. We review these missing processes and highlight the research required to resolve the representation of these regional-scale processes in climate models. We also discuss the experimental methodology required to rigorously determine whether these processes are restricted to a local or regional-scale role or whether they do trigger robust teleconnections that would demonstrate global-scale significance.

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Tropical deforestation: Modeling local‐ to regional‐scale climate change

Cited by 404 publications

References 35 publications

Modelling the impact of urbanisation on regional climate in the Greater London Area

Modelling the impact of urbanisation on regional climate in the Greater London Area

Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration

Regionalizing global climate models

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