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- Author or Editor: Debbie Polson x
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Abstract
Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive are used to calculate the zonal-mean change in seasonal land precipitation for the second half of the twentieth century in response to a range of external forcings, including anthropogenic and natural forcings combined (ALL), greenhouse gas forcing, anthropogenic aerosol forcing, anthropogenic forcings combined, and natural forcing. These simulated patterns of change are used as fingerprints in a detection and attribution study applied to four different gridded observational datasets of global land precipitation from 1951 to 2005. There are large differences in the spatial and temporal coverage in the observational datasets. Yet despite these differences, the zonal-mean patterns of change are mostly consistent except at latitudes where spatial coverage is limited. The results show some differences between datasets, but the influence of external forcings is robustly detected in March–May, December–February, and for annual changes for the three datasets more suitable for studying changes. For June–August and September–November, external forcing is only detected for the dataset that includes only long-term stations. Fingerprints for combinations of forcings that include the effect of greenhouse gases are similarly detectable to those for ALL forcings, suggesting that greenhouse gas influence drives the detectable features of the ALL forcing fingerprint. Fingerprints of only natural or only anthropogenic aerosol forcing are not detected. This, together with two-fingerprint results, suggests that at least some of the detected change in zonal land precipitation can be attributed to human influences.
Abstract
Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive are used to calculate the zonal-mean change in seasonal land precipitation for the second half of the twentieth century in response to a range of external forcings, including anthropogenic and natural forcings combined (ALL), greenhouse gas forcing, anthropogenic aerosol forcing, anthropogenic forcings combined, and natural forcing. These simulated patterns of change are used as fingerprints in a detection and attribution study applied to four different gridded observational datasets of global land precipitation from 1951 to 2005. There are large differences in the spatial and temporal coverage in the observational datasets. Yet despite these differences, the zonal-mean patterns of change are mostly consistent except at latitudes where spatial coverage is limited. The results show some differences between datasets, but the influence of external forcings is robustly detected in March–May, December–February, and for annual changes for the three datasets more suitable for studying changes. For June–August and September–November, external forcing is only detected for the dataset that includes only long-term stations. Fingerprints for combinations of forcings that include the effect of greenhouse gases are similarly detectable to those for ALL forcings, suggesting that greenhouse gas influence drives the detectable features of the ALL forcing fingerprint. Fingerprints of only natural or only anthropogenic aerosol forcing are not detected. This, together with two-fingerprint results, suggests that at least some of the detected change in zonal land precipitation can be attributed to human influences.
Abstract
The transient climate response (TCR) quantifies the warming expected during a transient doubling of greenhouse gas concentrations in the atmosphere. Many previous studies quantifying the observed historic response to greenhouse gases, and with it the TCR, use multimodel mean fingerprints and found reasonably constrained values, which contributed to the IPCC estimated (>66%) range from 1° to 2.5°C. Here, it is shown that while the multimodel mean fingerprint is statistically more powerful than any individual model’s fingerprint, it does lead to overconfident results when applied to synthetic data, if model uncertainty is neglected. Here, a Bayesian method is used that estimates TCR, accounting for climate model and observational uncertainty with indices of global temperature that aim at constraining the aerosol contribution to the historical record better. Model uncertainty in the aerosol response was found to be large. Nevertheless, an overall TCR estimate of 0.4°–3.1°C (>90%) was calculated from the historical record, which reduces to 1.0°–2.6°C when using prior information that rules out negative TCR values and model misestimates of more than a factor of 3, and to 1.2°–2.4°C when using the multimodel mean fingerprints with a variance correction. Modeled temperature, like in the observations, is calculated as a blend of sea surface and air temperatures.
Abstract
The transient climate response (TCR) quantifies the warming expected during a transient doubling of greenhouse gas concentrations in the atmosphere. Many previous studies quantifying the observed historic response to greenhouse gases, and with it the TCR, use multimodel mean fingerprints and found reasonably constrained values, which contributed to the IPCC estimated (>66%) range from 1° to 2.5°C. Here, it is shown that while the multimodel mean fingerprint is statistically more powerful than any individual model’s fingerprint, it does lead to overconfident results when applied to synthetic data, if model uncertainty is neglected. Here, a Bayesian method is used that estimates TCR, accounting for climate model and observational uncertainty with indices of global temperature that aim at constraining the aerosol contribution to the historical record better. Model uncertainty in the aerosol response was found to be large. Nevertheless, an overall TCR estimate of 0.4°–3.1°C (>90%) was calculated from the historical record, which reduces to 1.0°–2.6°C when using prior information that rules out negative TCR values and model misestimates of more than a factor of 3, and to 1.2°–2.4°C when using the multimodel mean fingerprints with a variance correction. Modeled temperature, like in the observations, is calculated as a blend of sea surface and air temperatures.
Abstract
Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from 1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.
Abstract
Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from 1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.
Abstract
Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.
Abstract
Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.
