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- Author or Editor: Guiling Wang x
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Abstract
This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.
Abstract
This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.
Abstract
This study compares projected changes of precipitation characteristics in the U.S. Northeast in two analog-based climate downscaling products, Multivariate Adaptive Constructed Analogs (MACA) and Localized Constructed Analogs (LOCA). The level of similarity or differences between the two products varies with the type of precipitation metrics. For the total precipitation amount, the two products project significant annual increases that are similar in magnitude, spatial pattern, and seasonal distribution, with the largest increases in winter and spring. For the overall precipitation intensity or temporal aggregation of heavy precipitation (e.g., number of days with more than one inch of precipitation, the simple intensity index, and the fraction of annual precipitation accounted for by heavy events), both products project significant increases across the region with strong model consensus; the magnitude of absolute increases are similar between the two products, but the relative increases are larger in LOCA due to an underestimation of heavy precipitation in LOCA’s training data. For precipitation extremes such as the annual maximum 1-day precipitation, both products project significant increases in the long-term mean, but the magnitude of both the absolute and relative changes are much smaller in LOCA than in MACA, indicating that the extreme precipitation differences in the training data are amplified in future projections as a result of the analog-based downscaling algorithms. The two products differ the most in the intensity and frequency of rare extremes (e.g., 1-in-20-years events) for which MACA projects significant increases while the LOCA-projected changes are inconclusive over much of the study area.
Abstract
This study compares projected changes of precipitation characteristics in the U.S. Northeast in two analog-based climate downscaling products, Multivariate Adaptive Constructed Analogs (MACA) and Localized Constructed Analogs (LOCA). The level of similarity or differences between the two products varies with the type of precipitation metrics. For the total precipitation amount, the two products project significant annual increases that are similar in magnitude, spatial pattern, and seasonal distribution, with the largest increases in winter and spring. For the overall precipitation intensity or temporal aggregation of heavy precipitation (e.g., number of days with more than one inch of precipitation, the simple intensity index, and the fraction of annual precipitation accounted for by heavy events), both products project significant increases across the region with strong model consensus; the magnitude of absolute increases are similar between the two products, but the relative increases are larger in LOCA due to an underestimation of heavy precipitation in LOCA’s training data. For precipitation extremes such as the annual maximum 1-day precipitation, both products project significant increases in the long-term mean, but the magnitude of both the absolute and relative changes are much smaller in LOCA than in MACA, indicating that the extreme precipitation differences in the training data are amplified in future projections as a result of the analog-based downscaling algorithms. The two products differ the most in the intensity and frequency of rare extremes (e.g., 1-in-20-years events) for which MACA projects significant increases while the LOCA-projected changes are inconclusive over much of the study area.
Abstract
Global and national climate assessments are comprehensive, authoritative sources of information about observed and projected climate changes and their impacts on society. These assessments follow well-known, accepted procedures to create credible, legitimate, salient sources of information for policy- and decision-making, build capacity for action, and educate the public. While there is a great deal of research on assessments at global and national scales, there is little research or guidance for assessment at the U.S. state scale. To address the need for guidance for state climate assessments (SCAs), the authors combined insights from the literature, firsthand experience with four SCAs, and interviews with individuals involved in 10 other SCAs to identify challenges, draw lessons, and point out future research needs to guide SCAs. SCAs are challenged by sparseness of literature and data, insufficient support for ongoing assessment, short time lines, limited funding, and surprisingly, little deliberate effort to address legitimacy as a concern. Lessons learned suggest SCAs should consider credibility, legitimacy, and salience as core criteria; happen at regular intervals; identify assessment scope, resource allocation, and trade-offs between generation of new knowledge, engagement, and communication up front; and leverage boundary organizations. Future research should build on ongoing efforts to advance assessments, examine the effectiveness of different SCA approaches, and seek to inform both broad and specific guidance for SCAs.
Abstract
Global and national climate assessments are comprehensive, authoritative sources of information about observed and projected climate changes and their impacts on society. These assessments follow well-known, accepted procedures to create credible, legitimate, salient sources of information for policy- and decision-making, build capacity for action, and educate the public. While there is a great deal of research on assessments at global and national scales, there is little research or guidance for assessment at the U.S. state scale. To address the need for guidance for state climate assessments (SCAs), the authors combined insights from the literature, firsthand experience with four SCAs, and interviews with individuals involved in 10 other SCAs to identify challenges, draw lessons, and point out future research needs to guide SCAs. SCAs are challenged by sparseness of literature and data, insufficient support for ongoing assessment, short time lines, limited funding, and surprisingly, little deliberate effort to address legitimacy as a concern. Lessons learned suggest SCAs should consider credibility, legitimacy, and salience as core criteria; happen at regular intervals; identify assessment scope, resource allocation, and trade-offs between generation of new knowledge, engagement, and communication up front; and leverage boundary organizations. Future research should build on ongoing efforts to advance assessments, examine the effectiveness of different SCA approaches, and seek to inform both broad and specific guidance for SCAs.
