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- Author or Editor: Julia E. Cole x
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Abstract
The authors examine relationships between Indian Ocean sea surface temperature (SST) variability and the variability of the Indian monsoon, including analysis of potential long-lead predictions of Indian rainfall by regional SST and the influence of ENSO and decadal variability on the stability of the relationships. Using monthly gridded (4° × 4°) SST data from the Global Sea-Ice and Sea Surface Temperature (GISST) dataset that spans 1945–94, the correlation fields between the All-India Rainfall Index (AIRI) and SST fields over the tropical Indian Ocean are calculated. In the boreal fall and winter preceding the summer Indian monsoon, SST throughout the tropical Indian Ocean correlates positively with subsequent monsoon rainfall. Negative correlation occurs between SST and the AIRI in the subsequent autumn in the northern Indian Ocean only. A strong correlation (0.53) is found between the summer AIRI and the preceding December–February Arabian Sea SST. The correlation between the AIRI and the SST to the northwest of Australia for the same period is 0.58. The highest correlation (0.87) for the years following 1977 is found between the AIRI and the central Indian Ocean SST in the preceding September–November, but this relationship is much weaker in earlier years. Based upon these correlations, the authors define Arabian Sea (AS1), northwest Australia (NWA1), and central Indian Ocean (CIO1) SST indexes. The relationships of these indexes to the AIRI and ENSO are examined. The authors find that the high correlation of the AS1 and NWA1 SST indexes with the Indian summer rainfall is largely unaffected by the removal of the ENSO signal, whereas the correlation of the CIO1 index with the AIRI is reduced. The authors examine the interdecadal variability of the relationships between SST and the AIRI and show that the Indian Ocean has undergone significant secular variation associated with a climate shift in 1976. The possible mechanisms underlying the correlation patterns and the implications of the relationship to the biennial nature of the monsoon and predictability are discussed.
Abstract
The authors examine relationships between Indian Ocean sea surface temperature (SST) variability and the variability of the Indian monsoon, including analysis of potential long-lead predictions of Indian rainfall by regional SST and the influence of ENSO and decadal variability on the stability of the relationships. Using monthly gridded (4° × 4°) SST data from the Global Sea-Ice and Sea Surface Temperature (GISST) dataset that spans 1945–94, the correlation fields between the All-India Rainfall Index (AIRI) and SST fields over the tropical Indian Ocean are calculated. In the boreal fall and winter preceding the summer Indian monsoon, SST throughout the tropical Indian Ocean correlates positively with subsequent monsoon rainfall. Negative correlation occurs between SST and the AIRI in the subsequent autumn in the northern Indian Ocean only. A strong correlation (0.53) is found between the summer AIRI and the preceding December–February Arabian Sea SST. The correlation between the AIRI and the SST to the northwest of Australia for the same period is 0.58. The highest correlation (0.87) for the years following 1977 is found between the AIRI and the central Indian Ocean SST in the preceding September–November, but this relationship is much weaker in earlier years. Based upon these correlations, the authors define Arabian Sea (AS1), northwest Australia (NWA1), and central Indian Ocean (CIO1) SST indexes. The relationships of these indexes to the AIRI and ENSO are examined. The authors find that the high correlation of the AS1 and NWA1 SST indexes with the Indian summer rainfall is largely unaffected by the removal of the ENSO signal, whereas the correlation of the CIO1 index with the AIRI is reduced. The authors examine the interdecadal variability of the relationships between SST and the AIRI and show that the Indian Ocean has undergone significant secular variation associated with a climate shift in 1976. The possible mechanisms underlying the correlation patterns and the implications of the relationship to the biennial nature of the monsoon and predictability are discussed.
CORRIGENDUM
Indian Ocean SST and Indian Summer Rainfall: Predictive Relationships and Their Decadal Variability
Abstract
The variance of the rainfall during the October–November–December (OND) “short rain” season along the coast in Kenya and Tanzania correlates strongly with sea surface temperature (SST) in the Indian Ocean between 1950 and 1999. A zonal pattern of positive correlation in the Arabian Sea and negative correlation southwest of Sumatra forms in the summer preceding the rainy season. The positive correlation strengthens in the western Indian Ocean and the negative correlation in the eastern Indian Ocean weakens in the subsequent fall concurrent with the short rain. Reduced OND East African rainfall is associated with the reversed SST pattern. The OND rainfall also correlates strongly with ENSO. The SST–rain correlation pattern breaks down between the years 1983 and 1993, as does the correlation with ENSO. However, between 1994 and 1999 the OND rainfall, ENSO, and the SST zonal mode again return to strong correlation, as in the years preceding 1983.
Abstract
The variance of the rainfall during the October–November–December (OND) “short rain” season along the coast in Kenya and Tanzania correlates strongly with sea surface temperature (SST) in the Indian Ocean between 1950 and 1999. A zonal pattern of positive correlation in the Arabian Sea and negative correlation southwest of Sumatra forms in the summer preceding the rainy season. The positive correlation strengthens in the western Indian Ocean and the negative correlation in the eastern Indian Ocean weakens in the subsequent fall concurrent with the short rain. Reduced OND East African rainfall is associated with the reversed SST pattern. The OND rainfall also correlates strongly with ENSO. The SST–rain correlation pattern breaks down between the years 1983 and 1993, as does the correlation with ENSO. However, between 1994 and 1999 the OND rainfall, ENSO, and the SST zonal mode again return to strong correlation, as in the years preceding 1983.
Abstract
Projected changes in global rainfall patterns will likely alter water supplies and ecosystems in semiarid regions during the coming century. Instrumental and paleoclimate data indicate that natural hydroclimate fluctuations tend to be more energetic at low (multidecadal to multicentury) than at high (interannual) frequencies. State-of-the-art global climate models do not capture this characteristic of hydroclimate variability, suggesting that the models underestimate the risk of future persistent droughts. Methods are developed here for assessing the risk of such events in the coming century using climate model projections as well as observational (paleoclimate) information. Where instrumental and paleoclimate data are reliable, these methods may provide a more complete view of prolonged drought risk. In the U.S. Southwest, for instance, state-of-the-art climate model projections suggest the risk of a decade-scale megadrought in the coming century is less than 50%; the analysis herein suggests that the risk is at least 80%, and may be higher than 90% in certain areas. The likelihood of longer-lived events (>35 yr) is between 20% and 50%, and the risk of an unprecedented 50-yr megadrought is nonnegligible under the most severe warming scenario (5%–10%). These findings are important to consider as adaptation and mitigation strategies are developed to cope with regional impacts of climate change, where population growth is high and multidecadal megadrought—worse than anything seen during the last 2000 years—would pose unprecedented challenges to water resources in the region.
Abstract
Projected changes in global rainfall patterns will likely alter water supplies and ecosystems in semiarid regions during the coming century. Instrumental and paleoclimate data indicate that natural hydroclimate fluctuations tend to be more energetic at low (multidecadal to multicentury) than at high (interannual) frequencies. State-of-the-art global climate models do not capture this characteristic of hydroclimate variability, suggesting that the models underestimate the risk of future persistent droughts. Methods are developed here for assessing the risk of such events in the coming century using climate model projections as well as observational (paleoclimate) information. Where instrumental and paleoclimate data are reliable, these methods may provide a more complete view of prolonged drought risk. In the U.S. Southwest, for instance, state-of-the-art climate model projections suggest the risk of a decade-scale megadrought in the coming century is less than 50%; the analysis herein suggests that the risk is at least 80%, and may be higher than 90% in certain areas. The likelihood of longer-lived events (>35 yr) is between 20% and 50%, and the risk of an unprecedented 50-yr megadrought is nonnegligible under the most severe warming scenario (5%–10%). These findings are important to consider as adaptation and mitigation strategies are developed to cope with regional impacts of climate change, where population growth is high and multidecadal megadrought—worse than anything seen during the last 2000 years—would pose unprecedented challenges to water resources in the region.
Abstract
Accurate assessments of future climate impacts require realistic simulation of interannual–century-scale temperature and precipitation variability. Here, well-constrained paleoclimate data and the latest generation of Earth system model data are used to evaluate the magnitude and spatial consistency of climate variance distributions across interannual to centennial frequencies. It is found that temperature variance generally increases with time scale in patterns that are spatially consistent among models, especially over the mid- and high-latitude oceans. However, precipitation is similar to white noise across much of the globe. When Earth system model variance is compared to variance generated by simple autocorrelation, it is found that tropical temperature variability in Earth system models is difficult to distinguish from variability generated by simple autocorrelation. By contrast, both forced and unforced Earth system models produce variability distinct from a simple autoregressive process over most high-latitude oceans. This new analysis of tropical paleoclimate records suggests that low-frequency variance dominates the temperature spectrum across the tropical Pacific and Indian Oceans, but in many Earth system models, interannual variance dominates the simulated central and eastern tropical Pacific temperature spectrum, regardless of forcing. Tropical Pacific model spectra are compared to spectra from the instrumental record, but the short instrumental record likely cannot provide accurate multidecadal–centennial-scale variance estimates. In the coming decades, both forced and natural patterns of decade–century-scale variability will determine climate-related risks. Underestimating low-frequency temperature and precipitation variability may significantly alter our understanding of the projections of these climate impacts.
Abstract
Accurate assessments of future climate impacts require realistic simulation of interannual–century-scale temperature and precipitation variability. Here, well-constrained paleoclimate data and the latest generation of Earth system model data are used to evaluate the magnitude and spatial consistency of climate variance distributions across interannual to centennial frequencies. It is found that temperature variance generally increases with time scale in patterns that are spatially consistent among models, especially over the mid- and high-latitude oceans. However, precipitation is similar to white noise across much of the globe. When Earth system model variance is compared to variance generated by simple autocorrelation, it is found that tropical temperature variability in Earth system models is difficult to distinguish from variability generated by simple autocorrelation. By contrast, both forced and unforced Earth system models produce variability distinct from a simple autoregressive process over most high-latitude oceans. This new analysis of tropical paleoclimate records suggests that low-frequency variance dominates the temperature spectrum across the tropical Pacific and Indian Oceans, but in many Earth system models, interannual variance dominates the simulated central and eastern tropical Pacific temperature spectrum, regardless of forcing. Tropical Pacific model spectra are compared to spectra from the instrumental record, but the short instrumental record likely cannot provide accurate multidecadal–centennial-scale variance estimates. In the coming decades, both forced and natural patterns of decade–century-scale variability will determine climate-related risks. Underestimating low-frequency temperature and precipitation variability may significantly alter our understanding of the projections of these climate impacts.
Abstract
The distribution of climatic variance across the frequency spectrum has substantial importance for anticipating how climate will evolve in the future. Here power spectra and power laws (β) are estimated from instrumental, proxy, and climate model data to characterize the hydroclimate continuum in western North America (WNA). The significance of the estimates of spectral densities and β are tested against the null hypothesis that they reflect solely the effects of local (nonclimate) sources of autocorrelation at the monthly time scale. Although tree-ring-based hydroclimate reconstructions are generally consistent with this null hypothesis, values of β calculated from long moisture-sensitive chronologies (as opposed to reconstructions) and other types of hydroclimate proxies exceed null expectations. Therefore it may be argued that there is more low-frequency variability in hydroclimate than monthly autocorrelation alone can generate. Coupled model results archived as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are consistent with the null hypothesis and appear unable to generate variance in hydroclimate commensurate with paleoclimate records. Consequently, at decadal-to-multidecadal time scales there is more variability in instrumental and proxy data than in the models, suggesting that the risk of prolonged droughts under climate change may be underestimated by CMIP5 simulations of the future.
Abstract
The distribution of climatic variance across the frequency spectrum has substantial importance for anticipating how climate will evolve in the future. Here power spectra and power laws (β) are estimated from instrumental, proxy, and climate model data to characterize the hydroclimate continuum in western North America (WNA). The significance of the estimates of spectral densities and β are tested against the null hypothesis that they reflect solely the effects of local (nonclimate) sources of autocorrelation at the monthly time scale. Although tree-ring-based hydroclimate reconstructions are generally consistent with this null hypothesis, values of β calculated from long moisture-sensitive chronologies (as opposed to reconstructions) and other types of hydroclimate proxies exceed null expectations. Therefore it may be argued that there is more low-frequency variability in hydroclimate than monthly autocorrelation alone can generate. Coupled model results archived as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are consistent with the null hypothesis and appear unable to generate variance in hydroclimate commensurate with paleoclimate records. Consequently, at decadal-to-multidecadal time scales there is more variability in instrumental and proxy data than in the models, suggesting that the risk of prolonged droughts under climate change may be underestimated by CMIP5 simulations of the future.
