Abstract
The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) aims at supporting the assessment of satellite ocean color radiometric products with in situ reference data derived from automated above-water measurements. This study, applying metrology principles and taking advantage of recent technology and science advances, revisits the uncertainty estimates formerly provided for AERONET-OC normalized water-leaving radiances L WN. The new uncertainty values are quantified for a number of AERONET-OC sites located in marine regions representative of chlorophyll-a-dominated waters (i.e., Case 1) and a variety of optically complex waters. Results show uncertainties typically increasing with the optical complexity of water and wind speed. Relative and absolute uncertainty values are provided for the various sites together with contributions from each source of uncertainty affecting measurements. In view of supporting AERONET-OC data users, the study also suggests practical solutions to quantify uncertainties for L WN from its spectral values. Additionally, results from an evaluation of the temporal variability characterizing L WN at various AERONET-OC sites are presented to address the impact of temporal mismatches between in situ and satellite data in matchup analysis.
Abstract
The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) aims at supporting the assessment of satellite ocean color radiometric products with in situ reference data derived from automated above-water measurements. This study, applying metrology principles and taking advantage of recent technology and science advances, revisits the uncertainty estimates formerly provided for AERONET-OC normalized water-leaving radiances L WN. The new uncertainty values are quantified for a number of AERONET-OC sites located in marine regions representative of chlorophyll-a-dominated waters (i.e., Case 1) and a variety of optically complex waters. Results show uncertainties typically increasing with the optical complexity of water and wind speed. Relative and absolute uncertainty values are provided for the various sites together with contributions from each source of uncertainty affecting measurements. In view of supporting AERONET-OC data users, the study also suggests practical solutions to quantify uncertainties for L WN from its spectral values. Additionally, results from an evaluation of the temporal variability characterizing L WN at various AERONET-OC sites are presented to address the impact of temporal mismatches between in situ and satellite data in matchup analysis.
Abstract
With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%–21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation.
Abstract
With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%–21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation.
Abstract
A novel high-resolution regional reanalysis is used to investigate the mesoscale processes that preceded the formation of tropical cyclone (TC) Mora (2017). Both satellite observations and the regional reanalysis show early morning mesoscale convective systems (MCSs) persistently initiated and organized in the downshear quadrant of the preexisting tropical disturbance a few days prior to the genesis of TC Mora. The diurnal MCSs gradually enhanced the meso-α-scale vortex near the center of the preexisting tropical disturbance through vortex stretching, providing a vorticity-rich and moist environment for the following burst of deep convection and enhancement of the meso-β-scale vortex. The regional reanalysis shows that the gravity waves that radiated from afternoon convection over the northern coast of the Bay of Bengal might play an important role in modulating the diurnal cycle of pregenesis MCSs. The diurnal convectively forced gravity waves increased the tropospheric stability, reduced the column saturation fraction, and suppressed deep convection within the preexisting tropical disturbance from noon to evening. Similar quasi-diurnal cycle of organized deep convection prior to TC genesis has also been observed over other basins. However, modeling studies are needed to conclusively demonstrate the relationships between the gravity waves and pregenesis diurnal MCSs. Also, whether diurnal gravity waves play a similar role in modulating the pregenesis deep convection in other TCs is worth future investigations.
Abstract
A novel high-resolution regional reanalysis is used to investigate the mesoscale processes that preceded the formation of tropical cyclone (TC) Mora (2017). Both satellite observations and the regional reanalysis show early morning mesoscale convective systems (MCSs) persistently initiated and organized in the downshear quadrant of the preexisting tropical disturbance a few days prior to the genesis of TC Mora. The diurnal MCSs gradually enhanced the meso-α-scale vortex near the center of the preexisting tropical disturbance through vortex stretching, providing a vorticity-rich and moist environment for the following burst of deep convection and enhancement of the meso-β-scale vortex. The regional reanalysis shows that the gravity waves that radiated from afternoon convection over the northern coast of the Bay of Bengal might play an important role in modulating the diurnal cycle of pregenesis MCSs. The diurnal convectively forced gravity waves increased the tropospheric stability, reduced the column saturation fraction, and suppressed deep convection within the preexisting tropical disturbance from noon to evening. Similar quasi-diurnal cycle of organized deep convection prior to TC genesis has also been observed over other basins. However, modeling studies are needed to conclusively demonstrate the relationships between the gravity waves and pregenesis diurnal MCSs. Also, whether diurnal gravity waves play a similar role in modulating the pregenesis deep convection in other TCs is worth future investigations.
Abstract
Coupled climate models project robust wintertime wetting trend over midlatitude East Asia under global warming scenarios, but the projected change in precipitation shows large intermodel uncertainty over subtropical East Asia from southern China to southwestern Japan. Based on an ensemble of climate models participating in CMIP6, this study shows that the weakened southern branch westerly jet (SWJ) on the southern side of Tibetan Plateau (TP) plays a key role in suppressing subtropical East Asian precipitation. The SWJ is deflected into southwesterly wind on the southeastern side of TP, bringing ascent and precipitation to subtropical East Asia primarily through isentropic gliding. As a result of the poleward and upward shift of the planetary-scale westerly jet under global warming, the SWJ becomes weaker and it acts to suppress subtropical East Asian precipitation by weakening the southwesterly wind and ascent. The SWJ–precipitation linkage also exists on interannual time scales, but the sensitivity of precipitation to interannual SWJ variability is systematically underestimated by the models compared with observation. The combined effects of the change in SWJ strength and the sensitivity of precipitation to SWJ explain about 40% of the intermodel spread of the projected precipitation changes. Observational constraint on the SWJ–precipitation relationship amplifies the projected drying trend and narrows the intermodel spread. It shows that the regional-averaged precipitation over subtropical East Asia decreases by 3.3% per degree of warming, and the amplitude of precipitation reduction over subtropical East Asia (southern China) is about 1.4 (3.4) times the raw projection.
Abstract
Coupled climate models project robust wintertime wetting trend over midlatitude East Asia under global warming scenarios, but the projected change in precipitation shows large intermodel uncertainty over subtropical East Asia from southern China to southwestern Japan. Based on an ensemble of climate models participating in CMIP6, this study shows that the weakened southern branch westerly jet (SWJ) on the southern side of Tibetan Plateau (TP) plays a key role in suppressing subtropical East Asian precipitation. The SWJ is deflected into southwesterly wind on the southeastern side of TP, bringing ascent and precipitation to subtropical East Asia primarily through isentropic gliding. As a result of the poleward and upward shift of the planetary-scale westerly jet under global warming, the SWJ becomes weaker and it acts to suppress subtropical East Asian precipitation by weakening the southwesterly wind and ascent. The SWJ–precipitation linkage also exists on interannual time scales, but the sensitivity of precipitation to interannual SWJ variability is systematically underestimated by the models compared with observation. The combined effects of the change in SWJ strength and the sensitivity of precipitation to SWJ explain about 40% of the intermodel spread of the projected precipitation changes. Observational constraint on the SWJ–precipitation relationship amplifies the projected drying trend and narrows the intermodel spread. It shows that the regional-averaged precipitation over subtropical East Asia decreases by 3.3% per degree of warming, and the amplitude of precipitation reduction over subtropical East Asia (southern China) is about 1.4 (3.4) times the raw projection.
Abstract
Atmospheric Rivers (ARs) have the potential to generate large-impact hydrometeorological events over mountainous topography. In this study, we investigate ARs’ impacts on the hydrology of Indus Basin (IB) and Ganga Basin (GB), two highly populated basins of the Himalayas. We used the recently developed 37-year long ERA5-based AR database over the Himalayas to explore the influence of ARs on total and extreme precipitation, snowfall, and floods over these basins. We find that ARs contribute ~25% to the annual rainfall in the IB and ~15% in the GB. Over the mountainous regions, ARs contribute more than 50% to winter precipitation in Karakoram (KA), Hindu-Kush (HK), central (CH) and western Himalayas (WH), and respectively explain over 75%, 57%, 42%, and 30% of their interannual variability. The seasonal rainfall extremes over the mountain foothills are most often (50 – 100%) associated with ARs in winter and spring, whereas the summer and autumn extremes over the plains and mountains foothills appear moderately associated with ARs (10 – 40%). The two most catastrophic flood events (2014 Kashmir flood and 2013 Uttarakhand flood) in these basins are found to be linked with Category 5 ARs. Upon further examination of floods over long period, we noted that 56% and 73% of the floods in IB and GB, respectively, are related to ARs. Thus, our results establish that the variance of ARs is a major source of hydro-climate variability in the two Himalayan basins.
Abstract
Atmospheric Rivers (ARs) have the potential to generate large-impact hydrometeorological events over mountainous topography. In this study, we investigate ARs’ impacts on the hydrology of Indus Basin (IB) and Ganga Basin (GB), two highly populated basins of the Himalayas. We used the recently developed 37-year long ERA5-based AR database over the Himalayas to explore the influence of ARs on total and extreme precipitation, snowfall, and floods over these basins. We find that ARs contribute ~25% to the annual rainfall in the IB and ~15% in the GB. Over the mountainous regions, ARs contribute more than 50% to winter precipitation in Karakoram (KA), Hindu-Kush (HK), central (CH) and western Himalayas (WH), and respectively explain over 75%, 57%, 42%, and 30% of their interannual variability. The seasonal rainfall extremes over the mountain foothills are most often (50 – 100%) associated with ARs in winter and spring, whereas the summer and autumn extremes over the plains and mountains foothills appear moderately associated with ARs (10 – 40%). The two most catastrophic flood events (2014 Kashmir flood and 2013 Uttarakhand flood) in these basins are found to be linked with Category 5 ARs. Upon further examination of floods over long period, we noted that 56% and 73% of the floods in IB and GB, respectively, are related to ARs. Thus, our results establish that the variance of ARs is a major source of hydro-climate variability in the two Himalayan basins.
Abstract
The sea surface temperature anomaly (SSTA) plays a key role in climate change and extreme weather processes. Usually, SSTA forecast methods consist of numerical and conventional statistical models, the former can be seriously influenced by the uncertainty of physical parameterization schemes, the nonlinearity of ocean dynamic processes, and the nonrobustness of numerical discretization algorithms. Recently, deep learning has been explored to address forecast issues in the field of oceanography. However, existing deep learning models for ocean forecasting are mainly site-specific, which were designed for forecasting on a single point or for an independent variable. Moreover, few special deep learning networks have been developed to deal with SSTA field forecasts under typhoon conditions. In this study, a multivariable convolutional neural network (MCNN) is proposed, which can be applied for synoptic-scale SSTA forecasting in the South China Sea. In addition to the SSTA itself, the surface wind speed and the surface current velocity are regarded as input variables for the prediction networks, effectively reflecting the influences of both local atmospheric dynamic forcing and nonlocal oceanic thermal advection. Experimental results demonstrate that MCNN exhibits better performance than single-variable convolutional neural network (SCNN), especially for the SSTA forecast during the typhoon passage. While forecast results deteriorate rapidly in the SCNN during the passage of a typhoon, forecast errors in the MCNN can be effectively restrained to slowly increase over the forecast time due to the introduction of the surface wind speed in this network.
Abstract
The sea surface temperature anomaly (SSTA) plays a key role in climate change and extreme weather processes. Usually, SSTA forecast methods consist of numerical and conventional statistical models, the former can be seriously influenced by the uncertainty of physical parameterization schemes, the nonlinearity of ocean dynamic processes, and the nonrobustness of numerical discretization algorithms. Recently, deep learning has been explored to address forecast issues in the field of oceanography. However, existing deep learning models for ocean forecasting are mainly site-specific, which were designed for forecasting on a single point or for an independent variable. Moreover, few special deep learning networks have been developed to deal with SSTA field forecasts under typhoon conditions. In this study, a multivariable convolutional neural network (MCNN) is proposed, which can be applied for synoptic-scale SSTA forecasting in the South China Sea. In addition to the SSTA itself, the surface wind speed and the surface current velocity are regarded as input variables for the prediction networks, effectively reflecting the influences of both local atmospheric dynamic forcing and nonlocal oceanic thermal advection. Experimental results demonstrate that MCNN exhibits better performance than single-variable convolutional neural network (SCNN), especially for the SSTA forecast during the typhoon passage. While forecast results deteriorate rapidly in the SCNN during the passage of a typhoon, forecast errors in the MCNN can be effectively restrained to slowly increase over the forecast time due to the introduction of the surface wind speed in this network.
Abstract
Oceanic submesoscale flows are considered to be a crucial conduit for the downscale transfer of oceanic mesoscale kinetic energy and upper-ocean material exchange, both laterally and vertically, but defining observations revealing submesoscale dynamics and/or transport properties remain sparse. Here, we report on an elaborate observation of a warm and fresh filament intruding into a cyclonic mesoscale eddy. By integrating cruise measurements, satellite observations, particle-tracking simulations, and the trajectory of a surface drifter, we show that the filament originated from an anticyclonic eddy immediately to the west of the cyclonic eddy, and the evolution of the filament was mainly due to the geostrophic flows associated with the eddy pair. Our observations reveal the mass exchange of the eddy pair and suggest that submesoscale flows can degrade the coherence of mesoscale eddies, providing important implications for the transport properties of mesoscale eddies. Vigorous submesoscale turbulence was found within the eddy core region, due to filamentous intrusion and frontogenesis. Our findings have thus offered novel insights into the dynamics and transport properties of oceanic submesoscale flows, which should be taken into account in their simulation and parameterization in ocean and climate models.
Abstract
Oceanic submesoscale flows are considered to be a crucial conduit for the downscale transfer of oceanic mesoscale kinetic energy and upper-ocean material exchange, both laterally and vertically, but defining observations revealing submesoscale dynamics and/or transport properties remain sparse. Here, we report on an elaborate observation of a warm and fresh filament intruding into a cyclonic mesoscale eddy. By integrating cruise measurements, satellite observations, particle-tracking simulations, and the trajectory of a surface drifter, we show that the filament originated from an anticyclonic eddy immediately to the west of the cyclonic eddy, and the evolution of the filament was mainly due to the geostrophic flows associated with the eddy pair. Our observations reveal the mass exchange of the eddy pair and suggest that submesoscale flows can degrade the coherence of mesoscale eddies, providing important implications for the transport properties of mesoscale eddies. Vigorous submesoscale turbulence was found within the eddy core region, due to filamentous intrusion and frontogenesis. Our findings have thus offered novel insights into the dynamics and transport properties of oceanic submesoscale flows, which should be taken into account in their simulation and parameterization in ocean and climate models.
Abstract
Tropical areas with mean upward motion—and as such the zonal-mean Intertropical Convergence Zone (ITCZ)—are projected to contract under global warming. To understand this process, a simple model based on dry static energy and moisture equations is introduced for zonally symmetric overturning driven by sea surface temperature (SST). Processes governing ascent area fraction and zonal mean precipitation are examined for insight into Atmospheric Model Intercomparison Project (AMIP) simulations. Bulk parameters governing radiative feedbacks and moist static energy transport in the simple model are estimated from the AMIP ensemble. Uniform warming in the simple model produces ascent area contraction and precipitation intensification—similar to observations and climate models. Contributing effects include: stronger water vapor radiative feedbacks, weaker cloud-radiative feedbacks, stronger convection-circulation feedbacks and greater poleward moisture export. The simple model identifies parameters consequential for the inter-AMIP-model spread; an ensemble generated by perturbing parameters governing shortwave water vapor feedbacks and gross moist stability changes under warming tracks inter-AMIP-model variations with a correlation coefficient ~ 0.46. The simple model also predicts the multi-model mean changes in tropical ascent area and precipitation with reasonable accuracy. Furthermore, the simple model reproduces relationships among ascent area precipitation, ascent strength and ascent area fraction observed in AMIP models. A substantial portion of the inter-AMIP-model spread is traced to the spread in how moist static energy and vertical velocity profiles change under warming, which in turn impact the gross moist stability in deep convective regions—highlighting the need for observational constraints on these quantities.
Abstract
Tropical areas with mean upward motion—and as such the zonal-mean Intertropical Convergence Zone (ITCZ)—are projected to contract under global warming. To understand this process, a simple model based on dry static energy and moisture equations is introduced for zonally symmetric overturning driven by sea surface temperature (SST). Processes governing ascent area fraction and zonal mean precipitation are examined for insight into Atmospheric Model Intercomparison Project (AMIP) simulations. Bulk parameters governing radiative feedbacks and moist static energy transport in the simple model are estimated from the AMIP ensemble. Uniform warming in the simple model produces ascent area contraction and precipitation intensification—similar to observations and climate models. Contributing effects include: stronger water vapor radiative feedbacks, weaker cloud-radiative feedbacks, stronger convection-circulation feedbacks and greater poleward moisture export. The simple model identifies parameters consequential for the inter-AMIP-model spread; an ensemble generated by perturbing parameters governing shortwave water vapor feedbacks and gross moist stability changes under warming tracks inter-AMIP-model variations with a correlation coefficient ~ 0.46. The simple model also predicts the multi-model mean changes in tropical ascent area and precipitation with reasonable accuracy. Furthermore, the simple model reproduces relationships among ascent area precipitation, ascent strength and ascent area fraction observed in AMIP models. A substantial portion of the inter-AMIP-model spread is traced to the spread in how moist static energy and vertical velocity profiles change under warming, which in turn impact the gross moist stability in deep convective regions—highlighting the need for observational constraints on these quantities.
Abstract
Marine air temperatures recorded on ships during the daytime are known to be biased warm on average due to energy storage by the superstructure of the vessels. This makes unadjusted daytime observations unsuitable for many applications including for the monitoring of long-term temperature change over the oceans. In this paper a physics-based approach is used to estimate this heating bias in ship observations from ICOADS. Under this approach, empirically determined coefficients represent the energy transfer terms of a heat budget model that quantifies the heating bias and is applied as a function of cloud cover and the relative wind speed over individual ships. The coefficients for each ship are derived from the anomalous diurnal heating relative to nighttime air temperature. Model coefficients, cloud cover, and relative wind speed are then used to estimate the heating bias ship by ship and generate nighttime-equivalent time series. A variety of methodological approaches were tested. Application of this method enables the inclusion of some daytime observations in climate records based on marine air temperatures, allowing an earlier start date and giving an increase in spatial coverage compared to existing records that exclude daytime observations.
Significance Statement
Currently, the longest available record of air temperature over the oceans starts in 1880. We present an approach that enables observations of air temperatures over the oceans to be used in the creation of long-term climate records that are presently excluded. We do this by estimating the biases inherent in daytime temperature reports from ships, and adjust for these biases by implementing a numerical heat-budget model. The adjustment can be applied to the variety of ship types present in observational archives. The resulting adjusted temperatures can be used to create a more spatially complete record over the oceans, that extends further back in time, potentially into the late eighteenth century.
Abstract
Marine air temperatures recorded on ships during the daytime are known to be biased warm on average due to energy storage by the superstructure of the vessels. This makes unadjusted daytime observations unsuitable for many applications including for the monitoring of long-term temperature change over the oceans. In this paper a physics-based approach is used to estimate this heating bias in ship observations from ICOADS. Under this approach, empirically determined coefficients represent the energy transfer terms of a heat budget model that quantifies the heating bias and is applied as a function of cloud cover and the relative wind speed over individual ships. The coefficients for each ship are derived from the anomalous diurnal heating relative to nighttime air temperature. Model coefficients, cloud cover, and relative wind speed are then used to estimate the heating bias ship by ship and generate nighttime-equivalent time series. A variety of methodological approaches were tested. Application of this method enables the inclusion of some daytime observations in climate records based on marine air temperatures, allowing an earlier start date and giving an increase in spatial coverage compared to existing records that exclude daytime observations.
Significance Statement
Currently, the longest available record of air temperature over the oceans starts in 1880. We present an approach that enables observations of air temperatures over the oceans to be used in the creation of long-term climate records that are presently excluded. We do this by estimating the biases inherent in daytime temperature reports from ships, and adjust for these biases by implementing a numerical heat-budget model. The adjustment can be applied to the variety of ship types present in observational archives. The resulting adjusted temperatures can be used to create a more spatially complete record over the oceans, that extends further back in time, potentially into the late eighteenth century.
Abstract
The Pacific Meridional Mode (PMM) can modulate El Niño-Southern Oscillation (ENSO), and is also affected by ENSO-related tropical Pacific sea-surface temperature anomalies (SSTAs). Two tropical feedbacks on the PMM have been proposed: the positive one of central tropical Pacific SSTAs and the negative one of eastern tropical Pacific (ETP) SSTAs, the latter of which is suggested to be active only during strong eastern Pacific (EP) El Niño events like 1982/1983 and 1997/1998. However, we find that no strong negative PMM-like SSTAs appeared although the PMM indices (PMMIs) were strongly negative in spring of 1983 and 1998. Observation and model experiments show that tropical warming in 1983 and 1998 not only occurred in the ETP, but also extended to the dateline, thus inducing wind anomalies unfavorable for establishing the wind-evaporation-SST feedback for negative PMM in the subtropics. To understand the discrepancy between the large negative PMMIs and weak PMM-related subtropical cooling during strong EP El Niño events, we isolate the relative contributions of subtropical and tropical SSTAs to the PMMIs by calculating their spatial projections on the PMM. Analysis combinedly using observation and CMIP6 models shows that despite the large contribution from subtropical SSTAs, the large tropical SSTAs, especially the extreme ETP warming, during strong EP El Niño events could cause large negative PMMIs even without strong negative subtropical SSTAs. Our study clarifies the impact of ETP warming in causing negative PMM and indicates the overstatement of negative PMMIs by tropical SSTAs during strong EP El Niño events.
Abstract
The Pacific Meridional Mode (PMM) can modulate El Niño-Southern Oscillation (ENSO), and is also affected by ENSO-related tropical Pacific sea-surface temperature anomalies (SSTAs). Two tropical feedbacks on the PMM have been proposed: the positive one of central tropical Pacific SSTAs and the negative one of eastern tropical Pacific (ETP) SSTAs, the latter of which is suggested to be active only during strong eastern Pacific (EP) El Niño events like 1982/1983 and 1997/1998. However, we find that no strong negative PMM-like SSTAs appeared although the PMM indices (PMMIs) were strongly negative in spring of 1983 and 1998. Observation and model experiments show that tropical warming in 1983 and 1998 not only occurred in the ETP, but also extended to the dateline, thus inducing wind anomalies unfavorable for establishing the wind-evaporation-SST feedback for negative PMM in the subtropics. To understand the discrepancy between the large negative PMMIs and weak PMM-related subtropical cooling during strong EP El Niño events, we isolate the relative contributions of subtropical and tropical SSTAs to the PMMIs by calculating their spatial projections on the PMM. Analysis combinedly using observation and CMIP6 models shows that despite the large contribution from subtropical SSTAs, the large tropical SSTAs, especially the extreme ETP warming, during strong EP El Niño events could cause large negative PMMIs even without strong negative subtropical SSTAs. Our study clarifies the impact of ETP warming in causing negative PMM and indicates the overstatement of negative PMMIs by tropical SSTAs during strong EP El Niño events.
