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Abstract
We investigate the large-scale weather patterns during extreme precipitation (PEx) events over the conterminous United States (CONUS) by applying a version of the quasigeostrophic (QG) omega equation. This work aims to develop a climatology of the weather patterns most related to PEx events during current climate. Extreme events are examined for each of seven regions defined by consistent annual cycles of precipitation and spanning the CONUS. For the CONUS we train several self-organizing maps (SOM) on a pressure–time series of vertical velocity from each of the advective forcing terms in the QG omega equation for each extreme event. The unsupervised learning of the SOM allows us to identify the most descriptive set of nine patterns in vertical velocity associated with precipitation extremes. This method finds multiple frontal- and cyclone-driven patterns while grouping primarily convective events into one pattern. Frontal events include a synoptic pattern consistent with West Coast atmospheric river events as well as pattern groups linked to developing and to mature (“occluded”) frontal cyclones. The primary patterns found during PEx events vary seasonally and geographically. Frontal cyclone patterns are most common during PEx events during summer in the part of the Great Plains and during winter for the Northeast, Southeast, Pacific Northwest, and Southwest. Convection is the most common pattern during summer in all regions. Except in the Southeast, the annual cycles of monthly number of PEx events and average precipitation match well, partially validating our choice of regions to aggregate PEx events.
Abstract
We investigate the large-scale weather patterns during extreme precipitation (PEx) events over the conterminous United States (CONUS) by applying a version of the quasigeostrophic (QG) omega equation. This work aims to develop a climatology of the weather patterns most related to PEx events during current climate. Extreme events are examined for each of seven regions defined by consistent annual cycles of precipitation and spanning the CONUS. For the CONUS we train several self-organizing maps (SOM) on a pressure–time series of vertical velocity from each of the advective forcing terms in the QG omega equation for each extreme event. The unsupervised learning of the SOM allows us to identify the most descriptive set of nine patterns in vertical velocity associated with precipitation extremes. This method finds multiple frontal- and cyclone-driven patterns while grouping primarily convective events into one pattern. Frontal events include a synoptic pattern consistent with West Coast atmospheric river events as well as pattern groups linked to developing and to mature (“occluded”) frontal cyclones. The primary patterns found during PEx events vary seasonally and geographically. Frontal cyclone patterns are most common during PEx events during summer in the part of the Great Plains and during winter for the Northeast, Southeast, Pacific Northwest, and Southwest. Convection is the most common pattern during summer in all regions. Except in the Southeast, the annual cycles of monthly number of PEx events and average precipitation match well, partially validating our choice of regions to aggregate PEx events.
Abstract
Model evidence for the “pattern effect” assumes that global climate models (GCMs) faithfully simulate how clouds respond to varying sea surface temperature (SST) patterns and associated meteorological perturbations. We exploit time-invariant satellite-based estimates of the sensitivity of marine low clouds to meteorological perturbations to estimate how these clouds responded to time-varying SST patterns and meteorology between 1870 and 2014. GCMs and reanalyses provide estimates of the historical meteorological changes. Observations suggest that increasing estimated inversion strength (EIS) between 1980 and 2014 produced a negative low cloud feedback, opposite to the positive feedback expected from increasing CO2. This indicates that the processes responsible for marine cloud changes from 1980 to the near present are distinct from those associated with an increase in CO2. We also observationally constrain the difference between the historical near-global marine low cloud feedback,
Abstract
Model evidence for the “pattern effect” assumes that global climate models (GCMs) faithfully simulate how clouds respond to varying sea surface temperature (SST) patterns and associated meteorological perturbations. We exploit time-invariant satellite-based estimates of the sensitivity of marine low clouds to meteorological perturbations to estimate how these clouds responded to time-varying SST patterns and meteorology between 1870 and 2014. GCMs and reanalyses provide estimates of the historical meteorological changes. Observations suggest that increasing estimated inversion strength (EIS) between 1980 and 2014 produced a negative low cloud feedback, opposite to the positive feedback expected from increasing CO2. This indicates that the processes responsible for marine cloud changes from 1980 to the near present are distinct from those associated with an increase in CO2. We also observationally constrain the difference between the historical near-global marine low cloud feedback,
Abstract
The southwest monsoon delivers over 70% of India’s annual rainfall and is crucial to the success of agriculture across much of South Asia. Monsoon precipitation is known to be sensitive to sea surface temperature (SST) in the Bay of Bengal (BoB). Here, we use a configuration of the Unified Model of the Met Office coupled to an ocean mixed layer model to investigate the role of upper-ocean features in the BoB on southwest monsoon precipitation. We focus on the pronounced zonal and meridional SST gradients characteristic of the BoB; the zonal gradient in particular has an as-yet unknown effect on monsoon rainfall. We find that the zonal SST gradient is responsible for a 50% decrease in rainfall over the southern BoB (approximately 5 mm day−1), and a 50% increase in rainfall over Bangladesh and northern India (approximately 1 mm day−1). This increase is remotely forced by a strengthening of the monsoon Hadley circulation. The meridional SST gradient acts to decrease precipitation over the BoB itself, similarly to the zonal SST gradient, but does not have comparable effects over land. The impacts of barrier layers and high-salinity subsurface water are also investigated, but neither has significant effects on monsoon precipitation in this model; the influence of barrier layers on precipitation is felt in the months after the southwest monsoon. Models should accurately represent oceanic processes that directly influence BoB SST, such as the BoB cold pool, in order to faithfully represent monsoon rainfall.
Abstract
The southwest monsoon delivers over 70% of India’s annual rainfall and is crucial to the success of agriculture across much of South Asia. Monsoon precipitation is known to be sensitive to sea surface temperature (SST) in the Bay of Bengal (BoB). Here, we use a configuration of the Unified Model of the Met Office coupled to an ocean mixed layer model to investigate the role of upper-ocean features in the BoB on southwest monsoon precipitation. We focus on the pronounced zonal and meridional SST gradients characteristic of the BoB; the zonal gradient in particular has an as-yet unknown effect on monsoon rainfall. We find that the zonal SST gradient is responsible for a 50% decrease in rainfall over the southern BoB (approximately 5 mm day−1), and a 50% increase in rainfall over Bangladesh and northern India (approximately 1 mm day−1). This increase is remotely forced by a strengthening of the monsoon Hadley circulation. The meridional SST gradient acts to decrease precipitation over the BoB itself, similarly to the zonal SST gradient, but does not have comparable effects over land. The impacts of barrier layers and high-salinity subsurface water are also investigated, but neither has significant effects on monsoon precipitation in this model; the influence of barrier layers on precipitation is felt in the months after the southwest monsoon. Models should accurately represent oceanic processes that directly influence BoB SST, such as the BoB cold pool, in order to faithfully represent monsoon rainfall.
Abstract
This study examines the changes in the outer size distribution of landfalling tropical cyclones (TCs) over the Chinese mainland from 1977 to 2020. The period was divided into two epochs: 1977–98 and 1999–2020. The results show that the size distribution of landfalling TCs over South China has no apparent change, while that of landfalling TCs over East China (LTCEC) is narrower in the second epoch, and the difference in the median sizes between East China and South China become more significant. Furthermore, it is found that LTCEC formed over the western part of the western North Pacific (W-WNP) shifted to a larger size range (300–500 km) at landfall, while those formed over the eastern part of the western North Pacific (E-WNP) rarely grew to extremely large size (>500 km). Further investigation revealed that over the W-WNP, the genesis position of LTCEC migrated equatorward during the second epoch, leading to a longer TC lifetime before landfall. Also, the increase of background relative vorticity and moisture associated with the southward migration is conducive to larger initial vortices. For TCs originating from the E-WNP, the change in the active area of TC passages reduced the frequency of TCs affecting the Chinese coast. Moreover, the growth of TC size during the intensification stage was significantly suppressed, lowering the occurrence probability of extremely large TCs. Changes in the large-scale thermodynamic environments between the two epochs were explored. Increased static stability and decreased convective available potential energy are possible factors limiting TC size increase.
Abstract
This study examines the changes in the outer size distribution of landfalling tropical cyclones (TCs) over the Chinese mainland from 1977 to 2020. The period was divided into two epochs: 1977–98 and 1999–2020. The results show that the size distribution of landfalling TCs over South China has no apparent change, while that of landfalling TCs over East China (LTCEC) is narrower in the second epoch, and the difference in the median sizes between East China and South China become more significant. Furthermore, it is found that LTCEC formed over the western part of the western North Pacific (W-WNP) shifted to a larger size range (300–500 km) at landfall, while those formed over the eastern part of the western North Pacific (E-WNP) rarely grew to extremely large size (>500 km). Further investigation revealed that over the W-WNP, the genesis position of LTCEC migrated equatorward during the second epoch, leading to a longer TC lifetime before landfall. Also, the increase of background relative vorticity and moisture associated with the southward migration is conducive to larger initial vortices. For TCs originating from the E-WNP, the change in the active area of TC passages reduced the frequency of TCs affecting the Chinese coast. Moreover, the growth of TC size during the intensification stage was significantly suppressed, lowering the occurrence probability of extremely large TCs. Changes in the large-scale thermodynamic environments between the two epochs were explored. Increased static stability and decreased convective available potential energy are possible factors limiting TC size increase.
Abstract
We examine the hypothesis that the observed connection between the stratospheric quasi-biennial oscillation (QBO) and the strength of the Madden–Julian oscillation (MJO) is modulated by the sea surface temperature (SST)—for example, by El Niño–Southern Oscillation (ENSO). A composite analysis shows that, globally, La Niña SSTs are remarkably similar to those that occur during the easterly phase of the QBO. A maximum covariance analysis suggests that MJO power and SST are strongly linked on both the ENSO time scale and the QBO time scale. We analyze simulations with a modified configuration of version 2 of the Community Earth System Model, with a high top and fine vertical resolution. The model is able to simulate ENSO, the QBO, and the MJO. The ocean-coupled version of the model simulates the QBO, ENSO, and MJO, but does not simulate the observed QBO–MJO connection. When driven with prescribed observed SST anomalies based on composites for QBO east and QBO west (QBOE and QBOW), however, the same atmospheric model produces a modest enhancement of MJO power during QBOE relative to QBOW, as observed. We explore the possibility that the SST anomalies are forced by the QBO itself. Indeed, composite Hovmöller diagrams based on observations show the propagation of QBO zonal wind anomalies all the way from the upper stratosphere to the surface. Also, subsurface ocean temperature composites reveal a similarity between the western Pacific and Indian Ocean subsurface signal between La Niña and QBOE.
Abstract
We examine the hypothesis that the observed connection between the stratospheric quasi-biennial oscillation (QBO) and the strength of the Madden–Julian oscillation (MJO) is modulated by the sea surface temperature (SST)—for example, by El Niño–Southern Oscillation (ENSO). A composite analysis shows that, globally, La Niña SSTs are remarkably similar to those that occur during the easterly phase of the QBO. A maximum covariance analysis suggests that MJO power and SST are strongly linked on both the ENSO time scale and the QBO time scale. We analyze simulations with a modified configuration of version 2 of the Community Earth System Model, with a high top and fine vertical resolution. The model is able to simulate ENSO, the QBO, and the MJO. The ocean-coupled version of the model simulates the QBO, ENSO, and MJO, but does not simulate the observed QBO–MJO connection. When driven with prescribed observed SST anomalies based on composites for QBO east and QBO west (QBOE and QBOW), however, the same atmospheric model produces a modest enhancement of MJO power during QBOE relative to QBOW, as observed. We explore the possibility that the SST anomalies are forced by the QBO itself. Indeed, composite Hovmöller diagrams based on observations show the propagation of QBO zonal wind anomalies all the way from the upper stratosphere to the surface. Also, subsurface ocean temperature composites reveal a similarity between the western Pacific and Indian Ocean subsurface signal between La Niña and QBOE.
Abstract
Tropical cyclones (TC) often induce strong mixing in the upper ocean that generates a trail of cooler sea surface temperature (Twake) in their wakes. The Twake can affect TC intensity, so its prediction is important, especially in coastal regions where TCs can make landfall. Coastal Twakes are often more complex than those in the open ocean due to the influences of coastline geometry, highly variable water depth, continental runoff, and shelf processes. Using observational data since 2002, here we show a significantly stronger global mean Twake in coastal regions compared to offshore regions. Temperature stratification is the main driver of stronger coastal Twakes in the North Atlantic and east Pacific. In the northwest Pacific and north Indian Ocean, the differences between coastal and offshore Twakes are smaller due to compensation between TC forcings and ocean stratification. The north Indian Ocean is unique in the Northern Hemisphere because salinity stratification plays a major role on the spatial distribution of Twake. In the South Pacific Ocean, TC intensity and translation speed are crucial for explaining coastal–offshore Twake differences, while ocean stratification and mixed layer depth are more important for the coastal–offshore Twake differences in the south Indian Ocean. These findings suggest that coastal–offshore differences in ocean stratification need to be properly represented in models in order to capture changes in TC-induced ocean cooling as storms approach landfall.
Significance Statement
Landfalling tropical cyclones (TCs) often cause considerable damage in coastal regions with dense human populations. Understanding TC–ocean interaction and how it differs between coastal and offshore regions can help predict TC intensity prior to landfall. Sea surface cooling after TC passage is an important proxy for TC–ocean interaction. A global evaluation of coastal TC-induced cooling has not been conducted. Using data covering two decades, we show significantly stronger TC-induced surface cooling in coastal regions compared to offshore regions at the global scale and in all basins except the northwest Pacific and north Indian Ocean. The difference is driven mainly by upper-ocean conditions in the North Atlantic, east Pacific, and south Indian Ocean, and by TC characteristics in the South Pacific.
Abstract
Tropical cyclones (TC) often induce strong mixing in the upper ocean that generates a trail of cooler sea surface temperature (Twake) in their wakes. The Twake can affect TC intensity, so its prediction is important, especially in coastal regions where TCs can make landfall. Coastal Twakes are often more complex than those in the open ocean due to the influences of coastline geometry, highly variable water depth, continental runoff, and shelf processes. Using observational data since 2002, here we show a significantly stronger global mean Twake in coastal regions compared to offshore regions. Temperature stratification is the main driver of stronger coastal Twakes in the North Atlantic and east Pacific. In the northwest Pacific and north Indian Ocean, the differences between coastal and offshore Twakes are smaller due to compensation between TC forcings and ocean stratification. The north Indian Ocean is unique in the Northern Hemisphere because salinity stratification plays a major role on the spatial distribution of Twake. In the South Pacific Ocean, TC intensity and translation speed are crucial for explaining coastal–offshore Twake differences, while ocean stratification and mixed layer depth are more important for the coastal–offshore Twake differences in the south Indian Ocean. These findings suggest that coastal–offshore differences in ocean stratification need to be properly represented in models in order to capture changes in TC-induced ocean cooling as storms approach landfall.
Significance Statement
Landfalling tropical cyclones (TCs) often cause considerable damage in coastal regions with dense human populations. Understanding TC–ocean interaction and how it differs between coastal and offshore regions can help predict TC intensity prior to landfall. Sea surface cooling after TC passage is an important proxy for TC–ocean interaction. A global evaluation of coastal TC-induced cooling has not been conducted. Using data covering two decades, we show significantly stronger TC-induced surface cooling in coastal regions compared to offshore regions at the global scale and in all basins except the northwest Pacific and north Indian Ocean. The difference is driven mainly by upper-ocean conditions in the North Atlantic, east Pacific, and south Indian Ocean, and by TC characteristics in the South Pacific.
Abstract
Arctic summer sea ice has been declining in recent decades. In this study, we investigate the beginning of the Arctic melting season, i.e., sea ice melt onset (MO), in the Laptev Sea (LS) and East Siberian Sea (ESS) along the Northern Sea route. Three leading modes are identified by EOF decomposition, which we call the LE-mode, L-mode, and E-mode. In positive phases these modes exhibit earlier MO in the two seas, a seesaw-like structure in the southwest–northeast direction with earlier MO in the LS, or in the southeast–northwest direction with earlier MO in the ESS. The LE-mode, L-mode, and E-mode are closely related to the Arctic Oscillation (AO) in April, the Barents Oscillation (BO) in April, and the AO in May, respectively. When the AO in April is positive, a low pressure anomaly northwest of the LS and ESS brings warm, moist air masses from the lower latitudes toward the LS and ESS and causes earlier MO, corresponding to the positive LE-mode. When the BO in April is negative, a cyclonic anomaly around the Barents Sea tends to warm and moisten the LS and cause earlier MO there, corresponding to the positive L-mode. When AO in May is positive, a low pressure anomaly northeast of the LS and ESS brings more warm, moist air toward the ESS and causes earlier MO there, corresponding to the positive E-mode. In the 1980s, the negative LE-mode was prominent whereas in the early 1990s the positive LE-mode was dominant. Since the mid-1990s, the L-mode and E-mode have appeared more frequently.
Abstract
Arctic summer sea ice has been declining in recent decades. In this study, we investigate the beginning of the Arctic melting season, i.e., sea ice melt onset (MO), in the Laptev Sea (LS) and East Siberian Sea (ESS) along the Northern Sea route. Three leading modes are identified by EOF decomposition, which we call the LE-mode, L-mode, and E-mode. In positive phases these modes exhibit earlier MO in the two seas, a seesaw-like structure in the southwest–northeast direction with earlier MO in the LS, or in the southeast–northwest direction with earlier MO in the ESS. The LE-mode, L-mode, and E-mode are closely related to the Arctic Oscillation (AO) in April, the Barents Oscillation (BO) in April, and the AO in May, respectively. When the AO in April is positive, a low pressure anomaly northwest of the LS and ESS brings warm, moist air masses from the lower latitudes toward the LS and ESS and causes earlier MO, corresponding to the positive LE-mode. When the BO in April is negative, a cyclonic anomaly around the Barents Sea tends to warm and moisten the LS and cause earlier MO there, corresponding to the positive L-mode. When AO in May is positive, a low pressure anomaly northeast of the LS and ESS brings more warm, moist air toward the ESS and causes earlier MO there, corresponding to the positive E-mode. In the 1980s, the negative LE-mode was prominent whereas in the early 1990s the positive LE-mode was dominant. Since the mid-1990s, the L-mode and E-mode have appeared more frequently.
Abstract
El Niño–Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large-ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3–7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes.” We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces a strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.
Abstract
El Niño–Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large-ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3–7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes.” We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces a strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.
Abstract
Historical precipitation and temperature trends and variations over global land regions are compared with simulations of two climate models focusing on grid points with substantial observational coverage from the early twentieth century. Potential mechanisms for the differences between modeled and observed trends are investigated using subsets of historical forcings, including ones using only anthropogenic greenhouse gases or aerosols, and simulations forced with the observed sea surface temperature and sea ice distribution. For century-scale (1915–2014) precipitation trends, underestimated increasing or unrealistic decreasing trends are found in the models over the extratropical Northern Hemisphere. The temporal evolution of key discrepancies between the observations and simulations indicates that 1) for averages over 15°–45°N, while there is not a significant trend in observations, both models simulate reduced precipitation from 1940 to 2014, and 2) for 45°–80°N observations suggest sizable precipitation increases while models do not show a significant increase, particularly during ∼1950–80. The timing of differences between models and observations suggests a key role for aerosols in these dry trend biases over the extratropical Northern Hemisphere. Additionally, 3) for 15°S–15°N the observed multidecadal decrease over tropical west Africa (1950–80) is only roughly captured by simulations forced with observed sea surface temperature; additionally, 4) in the all-forcing runs, the model with higher global climate sensitivity simulates increasing trends of temperature and precipitation over lands north of 45°N that are significantly stronger than the lower-sensitivity model and more consistent with the observed increases. Thus, underestimated greenhouse gas–induced warming—particularly in the lower sensitivity model—may be another important factor, besides aerosols, contributing to the modeled biases in precipitation trends.
Abstract
Historical precipitation and temperature trends and variations over global land regions are compared with simulations of two climate models focusing on grid points with substantial observational coverage from the early twentieth century. Potential mechanisms for the differences between modeled and observed trends are investigated using subsets of historical forcings, including ones using only anthropogenic greenhouse gases or aerosols, and simulations forced with the observed sea surface temperature and sea ice distribution. For century-scale (1915–2014) precipitation trends, underestimated increasing or unrealistic decreasing trends are found in the models over the extratropical Northern Hemisphere. The temporal evolution of key discrepancies between the observations and simulations indicates that 1) for averages over 15°–45°N, while there is not a significant trend in observations, both models simulate reduced precipitation from 1940 to 2014, and 2) for 45°–80°N observations suggest sizable precipitation increases while models do not show a significant increase, particularly during ∼1950–80. The timing of differences between models and observations suggests a key role for aerosols in these dry trend biases over the extratropical Northern Hemisphere. Additionally, 3) for 15°S–15°N the observed multidecadal decrease over tropical west Africa (1950–80) is only roughly captured by simulations forced with observed sea surface temperature; additionally, 4) in the all-forcing runs, the model with higher global climate sensitivity simulates increasing trends of temperature and precipitation over lands north of 45°N that are significantly stronger than the lower-sensitivity model and more consistent with the observed increases. Thus, underestimated greenhouse gas–induced warming—particularly in the lower sensitivity model—may be another important factor, besides aerosols, contributing to the modeled biases in precipitation trends.
Abstract
Anthropogenic aerosol interacts strongly with incoming solar radiation, perturbing Earth’s energy budget and precipitation on both local and global scales. Understanding these changes in precipitation has proven particularly difficult for the case of absorbing aerosol, which absorbs a significant amount of incoming solar radiation and hence acts as a source of localized diabatic heating to the atmosphere. In this work, we use an ensemble of atmosphere-only climate model simulations forced by identical absorbing aerosol perturbations in different geographical locations across the globe to develop a basic physical understanding of how this localized heating impacts the atmosphere and how these changes impact on precipitation both globally and locally. In agreement with previous studies we find that absorbing aerosol causes a decrease in global-mean precipitation, but we also show that even for identical aerosol optical depth perturbations, the global-mean precipitation change varies by over an order of magnitude depending on the location of the aerosol burden. Our experiments also demonstrate that the local precipitation response to absorbing aerosol is opposite in sign between the tropics and the extratropics, as found by previous work. We then show that this contrasting response can be understood in terms of different mechanisms by which the large-scale circulation responds to heating in the extratropics and in the tropics. We provide a simple theory to explain variations in the local precipitation response to absorbing aerosol in the tropics. Our work highlights that the spatial pattern of absorbing aerosol and its interactions with circulation are a key determinant of its overall climate impact and must be taken into account when developing our understanding of aerosol–climate interactions.
Abstract
Anthropogenic aerosol interacts strongly with incoming solar radiation, perturbing Earth’s energy budget and precipitation on both local and global scales. Understanding these changes in precipitation has proven particularly difficult for the case of absorbing aerosol, which absorbs a significant amount of incoming solar radiation and hence acts as a source of localized diabatic heating to the atmosphere. In this work, we use an ensemble of atmosphere-only climate model simulations forced by identical absorbing aerosol perturbations in different geographical locations across the globe to develop a basic physical understanding of how this localized heating impacts the atmosphere and how these changes impact on precipitation both globally and locally. In agreement with previous studies we find that absorbing aerosol causes a decrease in global-mean precipitation, but we also show that even for identical aerosol optical depth perturbations, the global-mean precipitation change varies by over an order of magnitude depending on the location of the aerosol burden. Our experiments also demonstrate that the local precipitation response to absorbing aerosol is opposite in sign between the tropics and the extratropics, as found by previous work. We then show that this contrasting response can be understood in terms of different mechanisms by which the large-scale circulation responds to heating in the extratropics and in the tropics. We provide a simple theory to explain variations in the local precipitation response to absorbing aerosol in the tropics. Our work highlights that the spatial pattern of absorbing aerosol and its interactions with circulation are a key determinant of its overall climate impact and must be taken into account when developing our understanding of aerosol–climate interactions.
