Abstract
Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and non-linearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature match-ups between GMI and the other instruments over both open ocean and rainforest scenes. In order to assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in-situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multi-decadal climate data record from passive microwave satellite observations.
Abstract
Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and non-linearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature match-ups between GMI and the other instruments over both open ocean and rainforest scenes. In order to assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in-situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multi-decadal climate data record from passive microwave satellite observations.
Abstract
This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.
Significance Statement
An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.
Abstract
This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.
Significance Statement
An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.
Abstract
The high-frequency (10–25-day, HF) boreal summer intraseasonal oscillation (BSISO) connects tightly to day-to-day weather and is inimitable in regulating extreme weather events. The western North Pacific (WNP) exhibits the most robust HF-BSISO variability worldwide, overshadowing the low-frequency (25–90-day) BSISO variability. Revealing the diversity of the HF-BSISO over the WNP area is crucial for understanding the BSISO physics and the Week Two-to-Four subseasonal forecast. This study objectively identified four types of HF-BSISO by cluster analysis of the (two-dimensional) propagation of OLR anomalies, including two northwestward-propagating patterns, with trajectories predominantly being located to the east and west of the Philippines, a westward-propagating Rossby wave train from the Philippine Sea to the Bay of Bengal, and a quasi-standing oscillation pattern centered around the Luzon Island. We have explored the propagation mechanisms of each archetype through a column-integrated moist static energy (MSE) budget diagnosis. Potential factors influencing the diverse propagation patterns are identified and compared. Basically, advections by the background flow predominantly drive the three propagating patterns, while the quasi-standing oscillation pattern is more sensitive to the surface turbulent heat fluxes. It is suggested that the diverse propagation patterns of the HF-BSISO over WNP could be impacted by the background states. However, the origins of these diversified propagation forms are complex and deserve further exploration.
Abstract
The high-frequency (10–25-day, HF) boreal summer intraseasonal oscillation (BSISO) connects tightly to day-to-day weather and is inimitable in regulating extreme weather events. The western North Pacific (WNP) exhibits the most robust HF-BSISO variability worldwide, overshadowing the low-frequency (25–90-day) BSISO variability. Revealing the diversity of the HF-BSISO over the WNP area is crucial for understanding the BSISO physics and the Week Two-to-Four subseasonal forecast. This study objectively identified four types of HF-BSISO by cluster analysis of the (two-dimensional) propagation of OLR anomalies, including two northwestward-propagating patterns, with trajectories predominantly being located to the east and west of the Philippines, a westward-propagating Rossby wave train from the Philippine Sea to the Bay of Bengal, and a quasi-standing oscillation pattern centered around the Luzon Island. We have explored the propagation mechanisms of each archetype through a column-integrated moist static energy (MSE) budget diagnosis. Potential factors influencing the diverse propagation patterns are identified and compared. Basically, advections by the background flow predominantly drive the three propagating patterns, while the quasi-standing oscillation pattern is more sensitive to the surface turbulent heat fluxes. It is suggested that the diverse propagation patterns of the HF-BSISO over WNP could be impacted by the background states. However, the origins of these diversified propagation forms are complex and deserve further exploration.
Abstract
One of the leading goals of rogue wave research is to develop a robust rogue wave warning system to mitigate the danger they pose. One such system has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), called the freak wave warning system (FWWS), based on nonlinear wave effects. The FWWS predicts maximum expected wave envelope height as a risk parameter for forecasts. Recently, a data-driven alternative has been proposed by Häfner et al., which was distilled from a neural network using wave buoy observations. However, it has yet to be evaluated by a spectral wave model for application to operational wave forecasting. The data-driven, learned model emphasizes bandwidth-controlled linear superposition as the predominant mechanism in crest-to-trough rogue wave generation, while nonlinear effects are a secondary term. The present work evaluates the performance of the empirical model using output from an ECMWF global wave hindcast. We find that the prediction models based on bandwidth effects have the highest log likelihood scores, with the empirical model outperforming all other tested models. In contrast, the expected maximum envelope wave height from the FWWS does not predict the occurrence of rogue waves. These results indicate that the empirical model with wave model input is a skillful predictor and should be considered for operational implementation to improve rogue wave forecasting.
Significance Statement
Rogue waves are unexpectedly large and unpredictable waves. Encounters with rogue waves can result in damage to marine vessels and offshore infrastructures. Research is devoted to developing systems to predict the risk of rogue wave events. A novel predictive model has been shown to perform well in predicting rogue wave occurrences based on wave buoy observations. This model is a symbolic expression distilled from an artificial neural network that incorporates known rogue wave dynamics and that can be evaluated alongside traditional risk estimates with minor adjustments to operational forecasting systems. This study evaluates the newly proposed empirical model in a forecasting setting. Our work demonstrates the efficacy of the empirical model for operational forecasting purposes.
Abstract
One of the leading goals of rogue wave research is to develop a robust rogue wave warning system to mitigate the danger they pose. One such system has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), called the freak wave warning system (FWWS), based on nonlinear wave effects. The FWWS predicts maximum expected wave envelope height as a risk parameter for forecasts. Recently, a data-driven alternative has been proposed by Häfner et al., which was distilled from a neural network using wave buoy observations. However, it has yet to be evaluated by a spectral wave model for application to operational wave forecasting. The data-driven, learned model emphasizes bandwidth-controlled linear superposition as the predominant mechanism in crest-to-trough rogue wave generation, while nonlinear effects are a secondary term. The present work evaluates the performance of the empirical model using output from an ECMWF global wave hindcast. We find that the prediction models based on bandwidth effects have the highest log likelihood scores, with the empirical model outperforming all other tested models. In contrast, the expected maximum envelope wave height from the FWWS does not predict the occurrence of rogue waves. These results indicate that the empirical model with wave model input is a skillful predictor and should be considered for operational implementation to improve rogue wave forecasting.
Significance Statement
Rogue waves are unexpectedly large and unpredictable waves. Encounters with rogue waves can result in damage to marine vessels and offshore infrastructures. Research is devoted to developing systems to predict the risk of rogue wave events. A novel predictive model has been shown to perform well in predicting rogue wave occurrences based on wave buoy observations. This model is a symbolic expression distilled from an artificial neural network that incorporates known rogue wave dynamics and that can be evaluated alongside traditional risk estimates with minor adjustments to operational forecasting systems. This study evaluates the newly proposed empirical model in a forecasting setting. Our work demonstrates the efficacy of the empirical model for operational forecasting purposes.
Abstract
This paper implements the “spatial first differences” (SFD) method to a Ricardian approach to analyze spatial differences in climate and evaluate their impact on farmland values in New Zealand. We use property valuation data from 1993 to 2018 across different agricultural land-uses. Ricardian analyses harness cross-sectional variation in climate and farmland values to estimate the effect of climate on agriculture. However, Ricardian studies are vulnerable to omitted variables varying slowly or not varying over time. The idea of the SFD approach is to apply spatial analogous of the “first-differences” estimator for longitudinal analyses to control for omitted variables that vary differently than climate variables among neighboring units. The results suggest that a warmer or drier climate is associated with higher farmland values in New Zealand. These findings may be mediated by productivity differences, costly physical improvements, and climate amenity values. Besides contributing novel insights by applying the Ricardian approach to New Zealand data and overcoming challenges of unobserved heterogeneity with the SFD method, we conclude that, for a better understanding of the welfare implications of climate change, it is crucial to differentiate between the effects of costly improvements and amenity values, apart from the pure climate productivity effect.
Abstract
This paper implements the “spatial first differences” (SFD) method to a Ricardian approach to analyze spatial differences in climate and evaluate their impact on farmland values in New Zealand. We use property valuation data from 1993 to 2018 across different agricultural land-uses. Ricardian analyses harness cross-sectional variation in climate and farmland values to estimate the effect of climate on agriculture. However, Ricardian studies are vulnerable to omitted variables varying slowly or not varying over time. The idea of the SFD approach is to apply spatial analogous of the “first-differences” estimator for longitudinal analyses to control for omitted variables that vary differently than climate variables among neighboring units. The results suggest that a warmer or drier climate is associated with higher farmland values in New Zealand. These findings may be mediated by productivity differences, costly physical improvements, and climate amenity values. Besides contributing novel insights by applying the Ricardian approach to New Zealand data and overcoming challenges of unobserved heterogeneity with the SFD method, we conclude that, for a better understanding of the welfare implications of climate change, it is crucial to differentiate between the effects of costly improvements and amenity values, apart from the pure climate productivity effect.
Abstract
This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products.
Abstract
This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products.
Abstract
The characteristics of modulated internal solitary waves (ISWs) under the influence of one mesoscale eddy pair in the Luzon Strait, involving one anticyclonic eddy (AE) and one cyclonic eddy (CE) induced by the Kuroshio intrusion, were investigated using a nested high-resolution numerical model in the northeastern South China Sea (SCS). The presence of mesoscale eddies greatly impacts the nonlinear evolution of type-a and type-b ISWs. The eddy pair contributes to distinct wave properties and energy evolutions. Compared to type-b waves, type-a waves display more pronounced modulatory characteristics with a larger spatial scale. CE currents and horizontal inhomogeneous stratification are crucial in modulating the wave behaviors, which induce extremely large-amplitude depression ISWs. The AE thereafter yields retardation effects on the wave energy evolution. The average depth-integrated available potential and kinetic energy showed relative growth rates of −66.12% and −46.07%, respectively, for type-a waves, and −24.26% and −20.15%, respectively, for type-b waves along the propagation path up to the AE core. The deformed and distorted ISW crest lines propagating further northward exhibit a more dramatic shoaling evolution. The maximum total energies of type-a and type-b waves at the north station are approximately 13.5 and 3.5 times, respectively, greater than those at the south station on the continental shelf of the Dongsha Atoll. This work provides essential insights into modulated ISW dynamics under the mesoscale eddy pair within the northeastern SCS deep basin.
Abstract
The characteristics of modulated internal solitary waves (ISWs) under the influence of one mesoscale eddy pair in the Luzon Strait, involving one anticyclonic eddy (AE) and one cyclonic eddy (CE) induced by the Kuroshio intrusion, were investigated using a nested high-resolution numerical model in the northeastern South China Sea (SCS). The presence of mesoscale eddies greatly impacts the nonlinear evolution of type-a and type-b ISWs. The eddy pair contributes to distinct wave properties and energy evolutions. Compared to type-b waves, type-a waves display more pronounced modulatory characteristics with a larger spatial scale. CE currents and horizontal inhomogeneous stratification are crucial in modulating the wave behaviors, which induce extremely large-amplitude depression ISWs. The AE thereafter yields retardation effects on the wave energy evolution. The average depth-integrated available potential and kinetic energy showed relative growth rates of −66.12% and −46.07%, respectively, for type-a waves, and −24.26% and −20.15%, respectively, for type-b waves along the propagation path up to the AE core. The deformed and distorted ISW crest lines propagating further northward exhibit a more dramatic shoaling evolution. The maximum total energies of type-a and type-b waves at the north station are approximately 13.5 and 3.5 times, respectively, greater than those at the south station on the continental shelf of the Dongsha Atoll. This work provides essential insights into modulated ISW dynamics under the mesoscale eddy pair within the northeastern SCS deep basin.
Abstract
Tropical cyclone numbers can vary from week to week within a hurricane season. Recent studies suggest that convectively coupled Kelvin waves can be partly responsible for such variability. However, the precise physical mechanisms responsible for that modulation remain uncertain partly due to the inability of previous studies to isolate the effects of Kelvin waves from other factors. This study uses an idealized modeling framework—called an aquaplanet—to uniquely isolate the effects of Kelvin waves on tropical cyclogenesis. The framework also captures the convective-scale dynamics of both tropical cyclones and Kelvin waves. Our results confirm an uptick in tropical cyclogenesis after the passage of a Kelvin wave—twice as many tropical cyclones form 2 days after a Kelvin wave peak than at any other time lag from the peak. A detailed composite analysis shows anomalously weak ventilation during and after (or to the west of) the Kelvin wave peak. The weak ventilation stems primarily from anomalously moist conditions, with weaker vertical wind shear playing a secondary role. In contrast to previous studies, our results demonstrate that Kelvin waves modulate both kinematic and thermodynamic synoptic-scale conditions that are necessary for tropical cyclone formation. These results suggest that numerical models must capture the three-dimensional structure of Kelvin waves to produce accurate subseasonal predictions of tropical cyclone activity.
Significance Statement
Anticipating active tropical cyclone periods several weeks in advance could help mitigate the loss of lives and property due to these phenomena. Recent studies suggest that a type of tropical cloud cluster—known as convectively coupled Kelvin waves—can promote tropical cyclone formation. Kelvin waves travel around the world and can be detected days to weeks in advance. We use a simplified numerical model to isolate the effects of Kelvin waves on tropical cyclone formation. Our unique approach confirms that tropical cyclones are more likely to form 2 days after a Kelvin wave than before the wave. We also demonstrate that—contrary to previous perception—the enhancement of tropical cyclogenesis is due to both more moisture and weaker wind currents following the waves.
Abstract
Tropical cyclone numbers can vary from week to week within a hurricane season. Recent studies suggest that convectively coupled Kelvin waves can be partly responsible for such variability. However, the precise physical mechanisms responsible for that modulation remain uncertain partly due to the inability of previous studies to isolate the effects of Kelvin waves from other factors. This study uses an idealized modeling framework—called an aquaplanet—to uniquely isolate the effects of Kelvin waves on tropical cyclogenesis. The framework also captures the convective-scale dynamics of both tropical cyclones and Kelvin waves. Our results confirm an uptick in tropical cyclogenesis after the passage of a Kelvin wave—twice as many tropical cyclones form 2 days after a Kelvin wave peak than at any other time lag from the peak. A detailed composite analysis shows anomalously weak ventilation during and after (or to the west of) the Kelvin wave peak. The weak ventilation stems primarily from anomalously moist conditions, with weaker vertical wind shear playing a secondary role. In contrast to previous studies, our results demonstrate that Kelvin waves modulate both kinematic and thermodynamic synoptic-scale conditions that are necessary for tropical cyclone formation. These results suggest that numerical models must capture the three-dimensional structure of Kelvin waves to produce accurate subseasonal predictions of tropical cyclone activity.
Significance Statement
Anticipating active tropical cyclone periods several weeks in advance could help mitigate the loss of lives and property due to these phenomena. Recent studies suggest that a type of tropical cloud cluster—known as convectively coupled Kelvin waves—can promote tropical cyclone formation. Kelvin waves travel around the world and can be detected days to weeks in advance. We use a simplified numerical model to isolate the effects of Kelvin waves on tropical cyclone formation. Our unique approach confirms that tropical cyclones are more likely to form 2 days after a Kelvin wave than before the wave. We also demonstrate that—contrary to previous perception—the enhancement of tropical cyclogenesis is due to both more moisture and weaker wind currents following the waves.
Abstract
Aerosols play a very important role in climate change with large uncertainties. Using the multi-model results from CMIP6, we analyzed the aerosol effective radiative forcing (ERF) and aerosol-induced surface air temperature (SAT) change in China in the present day (PD, 11-year mean of 2004–2014) relative to the pre-industrial time (PI, 11-year mean of 1850–1860). With the increase in the anthropogenic emissions, the simulated surface PM2.5 concentration and aerosol optical depth (AOD) averaged over eastern China (EC, 18–44°N, 103–122°E) increased by 21.43±7.58 μg m−3 and 0.47±0.33, respectively, from PI to PD. The simulated aerosol ERFs in EC were −4.91±2.56 and −5.35±2.40 W m−2 from equilibrium and transient simulations, respectively. The simulated change in SAT caused by the increases in aerosols was −1.37±0.38℃ in EC from PI to PD. The simulated values of equilibrium and transient climate sensitivity to aerosols (CSA, aerosol-induced SAT change per unit aerosol ERF) in EC were 0.236 and 0.222℃ (W m−2)−1, respectively. By using the observed AOD from MODIS to constrain aerosol ERF, the constrained aerosol equilibrium and transient ERFs over EC were −4.66 W m−2 and −4.93 W m−2, respectively, which were smaller in magnitude than the simulated values directly from the models. By using the observed SAT from the Climatic Research Unit temperature version 5 to constrain aerosol-induced cooling, the surface cooling caused by aerosols was magnified to −1.47℃. The adjusted CSA after the constraint was calculated by dividing adjusted aerosol-induced SAT change by adjusted aerosol ERF. Adjusted equilibrium and transient CSA values in EC were 0.32 and 0.34℃ (W m−2)−1, respectively.
Abstract
Aerosols play a very important role in climate change with large uncertainties. Using the multi-model results from CMIP6, we analyzed the aerosol effective radiative forcing (ERF) and aerosol-induced surface air temperature (SAT) change in China in the present day (PD, 11-year mean of 2004–2014) relative to the pre-industrial time (PI, 11-year mean of 1850–1860). With the increase in the anthropogenic emissions, the simulated surface PM2.5 concentration and aerosol optical depth (AOD) averaged over eastern China (EC, 18–44°N, 103–122°E) increased by 21.43±7.58 μg m−3 and 0.47±0.33, respectively, from PI to PD. The simulated aerosol ERFs in EC were −4.91±2.56 and −5.35±2.40 W m−2 from equilibrium and transient simulations, respectively. The simulated change in SAT caused by the increases in aerosols was −1.37±0.38℃ in EC from PI to PD. The simulated values of equilibrium and transient climate sensitivity to aerosols (CSA, aerosol-induced SAT change per unit aerosol ERF) in EC were 0.236 and 0.222℃ (W m−2)−1, respectively. By using the observed AOD from MODIS to constrain aerosol ERF, the constrained aerosol equilibrium and transient ERFs over EC were −4.66 W m−2 and −4.93 W m−2, respectively, which were smaller in magnitude than the simulated values directly from the models. By using the observed SAT from the Climatic Research Unit temperature version 5 to constrain aerosol-induced cooling, the surface cooling caused by aerosols was magnified to −1.47℃. The adjusted CSA after the constraint was calculated by dividing adjusted aerosol-induced SAT change by adjusted aerosol ERF. Adjusted equilibrium and transient CSA values in EC were 0.32 and 0.34℃ (W m−2)−1, respectively.
