Browse
Abstract
The Atlantic meridional overturning circulation (AMOC) plays an important role in climate, transporting heat and salt to the subpolar North Atlantic. The AMOC’s variability is sensitive to atmospheric forcing, especially the North Atlantic Oscillation (NAO). Because AMOC observations are short, climate models are a valuable tool to study the AMOC’s variability. Yet, there are known issues with climate models, like uncertainties and systematic biases. To investigate this, preindustrial control experiments from models participating in the phase 6 of Coupled Model Intercomparison Project (CMIP6) are evaluated. There is a large, but correlated, spread in the models’ subpolar gyre mean surface temperature and salinity. By splitting models into groups of either a warm–salty or cold–fresh subpolar gyre, it is shown that warm–salty models have a lower sea ice cover in the Labrador Sea and, hence, enable a larger heat loss during a positive NAO. Stratification in the Labrador Sea is also weaker in warm–salty models, such that the larger NAO-related heat loss can also affect greater depths. As a result, subsurface density anomalies are much stronger in the warm–salty models than in those that tend to be cold and fresh. As these anomalies propagate southward along the western boundary, they establish a zonal density gradient anomaly that promotes a stronger delayed AMOC response to the NAO in the warm–salty models. These findings demonstrate how model mean state errors are linked across variables and affect variability, emphasizing the need for improvement of the subpolar North Atlantic mean states in models.
Abstract
The Atlantic meridional overturning circulation (AMOC) plays an important role in climate, transporting heat and salt to the subpolar North Atlantic. The AMOC’s variability is sensitive to atmospheric forcing, especially the North Atlantic Oscillation (NAO). Because AMOC observations are short, climate models are a valuable tool to study the AMOC’s variability. Yet, there are known issues with climate models, like uncertainties and systematic biases. To investigate this, preindustrial control experiments from models participating in the phase 6 of Coupled Model Intercomparison Project (CMIP6) are evaluated. There is a large, but correlated, spread in the models’ subpolar gyre mean surface temperature and salinity. By splitting models into groups of either a warm–salty or cold–fresh subpolar gyre, it is shown that warm–salty models have a lower sea ice cover in the Labrador Sea and, hence, enable a larger heat loss during a positive NAO. Stratification in the Labrador Sea is also weaker in warm–salty models, such that the larger NAO-related heat loss can also affect greater depths. As a result, subsurface density anomalies are much stronger in the warm–salty models than in those that tend to be cold and fresh. As these anomalies propagate southward along the western boundary, they establish a zonal density gradient anomaly that promotes a stronger delayed AMOC response to the NAO in the warm–salty models. These findings demonstrate how model mean state errors are linked across variables and affect variability, emphasizing the need for improvement of the subpolar North Atlantic mean states in models.
Abstract
The likely changes to precipitation seasonality with warming are both impactful and not well understood. This work aims to describe areas that experience similar changes to seasonal precipitation irrespective of the original underlying precipitation seasonality. We train a self-organizing map on the difference between the seasonal cycle of precipitation in the past and in a high-warming future climate as represented by the Community Earth System Model, version 2, to create regions with similar changes in precipitation seasonality. This method is applied separately over land and ocean surfaces because of the differing processes leading to precipitation over each. This method indicates that future changes in seasonal precipitation are most varied in the tropics because of a southward shift in the intertropical convergence zone. The seasonal shifts found over midlatitude oceans indicate a poleward shift in atmospheric river activity. We find a correspondence between certain land-based precipitation changes and Köppen climate classification. The seasonality of large-scale and convective precipitation is examined for each region. The relationship between the seasonal changes to precipitation and associated atmospheric processes is discussed. These processes include atmospheric rivers, the intertropical convergence zone, tropical cyclones, and monsoons.
Abstract
The likely changes to precipitation seasonality with warming are both impactful and not well understood. This work aims to describe areas that experience similar changes to seasonal precipitation irrespective of the original underlying precipitation seasonality. We train a self-organizing map on the difference between the seasonal cycle of precipitation in the past and in a high-warming future climate as represented by the Community Earth System Model, version 2, to create regions with similar changes in precipitation seasonality. This method is applied separately over land and ocean surfaces because of the differing processes leading to precipitation over each. This method indicates that future changes in seasonal precipitation are most varied in the tropics because of a southward shift in the intertropical convergence zone. The seasonal shifts found over midlatitude oceans indicate a poleward shift in atmospheric river activity. We find a correspondence between certain land-based precipitation changes and Köppen climate classification. The seasonality of large-scale and convective precipitation is examined for each region. The relationship between the seasonal changes to precipitation and associated atmospheric processes is discussed. These processes include atmospheric rivers, the intertropical convergence zone, tropical cyclones, and monsoons.
Abstract
This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950–2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations significantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, interannual, and decadal scales, including significant long-term trends on their frequency. CPs influence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures—the subtropical Azores high—leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.
Abstract
This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950–2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations significantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, interannual, and decadal scales, including significant long-term trends on their frequency. CPs influence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures—the subtropical Azores high—leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.
Abstract
We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO.
Abstract
We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO.
Abstract
We consider the combined and individual influences of Arctic sea-ice loss, sea surface temperature (SST) warming, and the direct radiative effect of increased CO2 on the Northern Hemispheric climate. The surface climate (e.g., temperature, precipitation) and atmospheric circulation responses (e.g., sea level pressure, wind) to these drivers are quantified using simulations from the Polar Amplification Model Intercomparison Project (for sea-ice loss and SST warming) and the Cloud Feedback Model Intercomparison Project (for increased CO2). We verify the linear additivity of the PAMIP-derived winter responses to sea-ice loss and SST change. The responses to SST change are of greater magnitude than that due to sea-ice loss or due to CO2 direct radiative forcing in most seasons and regions, excluding the Arctic. Notably however, sea-ice loss is at least as important as SST change for the winter atmospheric circulation response over the North Atlantic and Siberia. The dynamical responses to sea-ice loss and SST change oppose each other in many regions in winter, while the responses to SST and CO2 direct radiative forcing are often opposing in summer. Such opposing responses are less evident for the thermodynamical response. The sum of all three responses reproduces well the spatial patterns of change at 2 °C global warming in winter and autumn in the Coupled Model Intercomparison Project phase 6 projections, but overestimates their magnitude.
Abstract
We consider the combined and individual influences of Arctic sea-ice loss, sea surface temperature (SST) warming, and the direct radiative effect of increased CO2 on the Northern Hemispheric climate. The surface climate (e.g., temperature, precipitation) and atmospheric circulation responses (e.g., sea level pressure, wind) to these drivers are quantified using simulations from the Polar Amplification Model Intercomparison Project (for sea-ice loss and SST warming) and the Cloud Feedback Model Intercomparison Project (for increased CO2). We verify the linear additivity of the PAMIP-derived winter responses to sea-ice loss and SST change. The responses to SST change are of greater magnitude than that due to sea-ice loss or due to CO2 direct radiative forcing in most seasons and regions, excluding the Arctic. Notably however, sea-ice loss is at least as important as SST change for the winter atmospheric circulation response over the North Atlantic and Siberia. The dynamical responses to sea-ice loss and SST change oppose each other in many regions in winter, while the responses to SST and CO2 direct radiative forcing are often opposing in summer. Such opposing responses are less evident for the thermodynamical response. The sum of all three responses reproduces well the spatial patterns of change at 2 °C global warming in winter and autumn in the Coupled Model Intercomparison Project phase 6 projections, but overestimates their magnitude.
Abstract
Two common methods used to develop a process-level understanding of global cloud cover are 1) analyzing large-scale meteorological variables (cloud controlling factors) associated with cloud variability and 2) classifying cloud types using clustering algorithms applied to satellite data, such as the International Satellite Cloud Climatology Project (ISCCP) weather states. The cloud controlling factor method is advantageous to apply to climate models, as it does not rely on cloud parameterizations or the availability of satellite simulator output. The purpose of this study is to document the relationship between cloud controlling factors and the ISCCP weather states in the observational record, providing a benchmark for the application of cloud controlling factors to study individual cloud types in future studies. Most ISCCP weather states are linked to distinct dynamical regimes characterized by unique combinations of six cloud controlling factors. These relationships are present in both the long-term mean climatology and daily-to-monthly climate variability. For example, deep convective and midlatitude storm clouds dominate ascending regions. In descending regions, shallow cumulus is more frequent in regimes characterized by weak boundary layer temperature inversions [estimated inversion strength (EIS)] and strong subsidence, and stratocumulus is more frequent in regimes with larger values of EIS, weaker subsidence, and relatively weak near-surface cold advection. Midlevel clouds are prominent in descending regions with strong cold advection. Overall, the results of this study suggest promise in using cloud controlling factors to identify dynamical regimes where individual cloud types are more or less likely and to understand the physical processes responsible for the transitions among them.
Abstract
Two common methods used to develop a process-level understanding of global cloud cover are 1) analyzing large-scale meteorological variables (cloud controlling factors) associated with cloud variability and 2) classifying cloud types using clustering algorithms applied to satellite data, such as the International Satellite Cloud Climatology Project (ISCCP) weather states. The cloud controlling factor method is advantageous to apply to climate models, as it does not rely on cloud parameterizations or the availability of satellite simulator output. The purpose of this study is to document the relationship between cloud controlling factors and the ISCCP weather states in the observational record, providing a benchmark for the application of cloud controlling factors to study individual cloud types in future studies. Most ISCCP weather states are linked to distinct dynamical regimes characterized by unique combinations of six cloud controlling factors. These relationships are present in both the long-term mean climatology and daily-to-monthly climate variability. For example, deep convective and midlatitude storm clouds dominate ascending regions. In descending regions, shallow cumulus is more frequent in regimes characterized by weak boundary layer temperature inversions [estimated inversion strength (EIS)] and strong subsidence, and stratocumulus is more frequent in regimes with larger values of EIS, weaker subsidence, and relatively weak near-surface cold advection. Midlevel clouds are prominent in descending regions with strong cold advection. Overall, the results of this study suggest promise in using cloud controlling factors to identify dynamical regimes where individual cloud types are more or less likely and to understand the physical processes responsible for the transitions among them.
Abstract
Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability.
Significance Statement
Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.
Abstract
Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability.
Significance Statement
Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.
Abstract
The variability of Arctic sea ice extent (SIE) on interannual and multi-decadal timescales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in 20th-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850-1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE), but exhibit pronounced inter-model spread in multi-decadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the sub-polar North Atlantic. We find that this is associated with differences in models’ sensitivity to northern hemispheric sea surface temperatures (SST). Additionally, we show that while CMIP6 models are generally capable of simulating multi-decadal changes in Arctic sea ice from the mid-20th century to present day, they tend to underestimate the observed sea ice decline during the Early Twentieth-Century Warming (ETCW; 1915-1945). These results suggest the need for an improved characterization of the sea ice response to multi-decadal climate variability, in order to address the sources of model bias and reduce the uncertainty in future projections arising from inter-model spread.
Abstract
The variability of Arctic sea ice extent (SIE) on interannual and multi-decadal timescales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in 20th-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850-1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE), but exhibit pronounced inter-model spread in multi-decadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the sub-polar North Atlantic. We find that this is associated with differences in models’ sensitivity to northern hemispheric sea surface temperatures (SST). Additionally, we show that while CMIP6 models are generally capable of simulating multi-decadal changes in Arctic sea ice from the mid-20th century to present day, they tend to underestimate the observed sea ice decline during the Early Twentieth-Century Warming (ETCW; 1915-1945). These results suggest the need for an improved characterization of the sea ice response to multi-decadal climate variability, in order to address the sources of model bias and reduce the uncertainty in future projections arising from inter-model spread.
Abstract
Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP.
Significance Statement
Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming.
Abstract
Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP.
Significance Statement
Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming.
