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- Author or Editor: Jie Jiang x
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Abstract
The Indian summer monsoon (ISM) rainfall affects a large population in South Asia. Observations show a decline in ISM rainfall from 1950 to 1999 and a recovery from 1999 to 2013. While the decline has been attributed to global warming, aerosol effects, deforestation, and a negative-to-positive phase transition of the interdecadal Pacific oscillation (IPO), the cause for the recovery remains largely unclear. Through analyses of a 57-member perturbed-parameter ensemble of model simulations, this study shows that the externally forced rainfall trend is relatively weak and is overwhelmed by large internal variability during both 1950–99 and 1999–2013. The IPO is identified as the internal mode that helps modulate the recent decline and recovery of the ISM rainfall. The IPO induces ISM rainfall changes through moisture convergence anomalies associated with an anomalous Walker circulation and meridional tropospheric temperature gradients and the resultant anomalous convection and zonal moisture advection. The negative-to-positive IPO phase transition from 1950 to 1999 reduces what would have been an externally forced weak upward rainfall trend of 0.01 to −0.15 mm day−1 decade−1 during that period, while the rainfall trend from 1999 to 2013 increases from the forced value of 0.42 to 0.68 mm day−1 decade−1 associated with a positive-to-negative IPO phase transition. Such a significant modulation of the historical ISM rainfall trends by the IPO is confirmed by another 100-member ensemble of simulations using perturbed initial conditions. Our findings highlight that the interplay between the effects of external forcing and the IPO needs be considered for climate adaptation and mitigation strategies in South Asia.
Abstract
The Indian summer monsoon (ISM) rainfall affects a large population in South Asia. Observations show a decline in ISM rainfall from 1950 to 1999 and a recovery from 1999 to 2013. While the decline has been attributed to global warming, aerosol effects, deforestation, and a negative-to-positive phase transition of the interdecadal Pacific oscillation (IPO), the cause for the recovery remains largely unclear. Through analyses of a 57-member perturbed-parameter ensemble of model simulations, this study shows that the externally forced rainfall trend is relatively weak and is overwhelmed by large internal variability during both 1950–99 and 1999–2013. The IPO is identified as the internal mode that helps modulate the recent decline and recovery of the ISM rainfall. The IPO induces ISM rainfall changes through moisture convergence anomalies associated with an anomalous Walker circulation and meridional tropospheric temperature gradients and the resultant anomalous convection and zonal moisture advection. The negative-to-positive IPO phase transition from 1950 to 1999 reduces what would have been an externally forced weak upward rainfall trend of 0.01 to −0.15 mm day−1 decade−1 during that period, while the rainfall trend from 1999 to 2013 increases from the forced value of 0.42 to 0.68 mm day−1 decade−1 associated with a positive-to-negative IPO phase transition. Such a significant modulation of the historical ISM rainfall trends by the IPO is confirmed by another 100-member ensemble of simulations using perturbed initial conditions. Our findings highlight that the interplay between the effects of external forcing and the IPO needs be considered for climate adaptation and mitigation strategies in South Asia.
Abstract
Upper-tropospheric ice cloud measurements from the Superconducting Submillimeter Limb Emission Sounder (SMILES) on the International Space Station (ISS) are used to study the diurnal cycle of upper-tropospheric ice cloud in the tropics and midlatitudes (40°S–40°N) and to quantitatively evaluate ice cloud diurnal variability simulated by 10 climate models. Over land, the SMILES-observed diurnal cycle has a maximum around 1800 local solar time (LST), while the model-simulated diurnal cycles have phases differing from the observed cycle by −4 to 12 h. Over ocean, the observations show much smaller diurnal cycle amplitudes than over land with a peak at 1200 LST, while the modeled diurnal cycle phases are widely distributed throughout the 24-h period. Most models show smaller diurnal cycle amplitudes over ocean than over land, which is in agreement with the observations. However, there is a large spread of modeled diurnal cycle amplitudes ranging from 20% to more than 300% of the observed over both land and ocean. Empirical orthogonal function (EOF) analysis on the observed and model-simulated variations of ice clouds finds that the first EOF modes over land from both observation and model simulations explain more than 70% of the ice cloud diurnal variations and they have similar spatial and temporal patterns. Over ocean, the first EOF from observation explains 26.4% of the variance, while the first EOF from most models explains more than 70%. The modeled spatial and temporal patterns of the leading EOFs over ocean show large differences from observations, indicating that the physical mechanisms governing the diurnal cycle of oceanic ice clouds are more complicated and not well simulated by the current climate models.
Abstract
Upper-tropospheric ice cloud measurements from the Superconducting Submillimeter Limb Emission Sounder (SMILES) on the International Space Station (ISS) are used to study the diurnal cycle of upper-tropospheric ice cloud in the tropics and midlatitudes (40°S–40°N) and to quantitatively evaluate ice cloud diurnal variability simulated by 10 climate models. Over land, the SMILES-observed diurnal cycle has a maximum around 1800 local solar time (LST), while the model-simulated diurnal cycles have phases differing from the observed cycle by −4 to 12 h. Over ocean, the observations show much smaller diurnal cycle amplitudes than over land with a peak at 1200 LST, while the modeled diurnal cycle phases are widely distributed throughout the 24-h period. Most models show smaller diurnal cycle amplitudes over ocean than over land, which is in agreement with the observations. However, there is a large spread of modeled diurnal cycle amplitudes ranging from 20% to more than 300% of the observed over both land and ocean. Empirical orthogonal function (EOF) analysis on the observed and model-simulated variations of ice clouds finds that the first EOF modes over land from both observation and model simulations explain more than 70% of the ice cloud diurnal variations and they have similar spatial and temporal patterns. Over ocean, the first EOF from observation explains 26.4% of the variance, while the first EOF from most models explains more than 70%. The modeled spatial and temporal patterns of the leading EOFs over ocean show large differences from observations, indicating that the physical mechanisms governing the diurnal cycle of oceanic ice clouds are more complicated and not well simulated by the current climate models.
