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Abstract
The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s−1). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction.
Significance Statement
Storm size has long been speculated to play a crucial role in modulating the TC self-induced sea surface temperature (SST) cooling and thus potentially influence TC intensification through ocean negative feedback. Nevertheless, systematic analysis is lacking. Here we show that larger TCs tend to generate stronger SST cooling and have longer exposure to the cooling effect, both of which enhance the strength of the negative feedback. Consequently, larger TCs undergo weaker intensification and are less likely to experience rapid intensification than smaller TCs. These results demonstrate that storm size can influence TC intensification not only from the atmospheric pathway, but also via the oceanic pathway. Accurate characterization of this oceanic pathway in coupled models is important to accurately forecast TC intensity.
Abstract
The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s−1). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction.
Significance Statement
Storm size has long been speculated to play a crucial role in modulating the TC self-induced sea surface temperature (SST) cooling and thus potentially influence TC intensification through ocean negative feedback. Nevertheless, systematic analysis is lacking. Here we show that larger TCs tend to generate stronger SST cooling and have longer exposure to the cooling effect, both of which enhance the strength of the negative feedback. Consequently, larger TCs undergo weaker intensification and are less likely to experience rapid intensification than smaller TCs. These results demonstrate that storm size can influence TC intensification not only from the atmospheric pathway, but also via the oceanic pathway. Accurate characterization of this oceanic pathway in coupled models is important to accurately forecast TC intensity.
Abstract
Climate model projections of atmospheric circulation patterns, their frequency, and associated temperature and precipitation anomalies under a high-end global warming scenario are assessed over the Pacific Northwest of North America for the final three decades of the twenty-first century. Model simulations are from phase 6 of the Coupled Model Intercomparison Project (CMIP6) and circulation patterns are identified using the self-organizing maps (SOMs) approach, applied to 500-hPa geopotential height (Z500) anomalies. Overall, the range of projected circulation patterns is similar to that in the current climate, especially in winter, whereas in summer the models project a general reduction in the magnitude of Z500 anomalies. Significant changes in pattern frequencies are also projected in summer, with an overall decrease in the frequency of patterns with large Z500 anomalies. In winter, patterns historically associated with anomalously cold weather in northern latitudes are projected to warm the most, and in summer the largest temperature increases are projected over inland areas. Precipitation is found to increase across all seasons and most SOM patterns. However, some summer patterns that are associated with above-average precipitation in the current climate are projected to become significantly drier by the end of the century.
Significance Statement
This paper uses a novel method to analyze projections of large-scale atmospheric circulation over the Pacific Northwest of North America, reducing the uncertainty of changes to the circulation patterns over the region under a high-emissions scenario of global warming.
Abstract
Climate model projections of atmospheric circulation patterns, their frequency, and associated temperature and precipitation anomalies under a high-end global warming scenario are assessed over the Pacific Northwest of North America for the final three decades of the twenty-first century. Model simulations are from phase 6 of the Coupled Model Intercomparison Project (CMIP6) and circulation patterns are identified using the self-organizing maps (SOMs) approach, applied to 500-hPa geopotential height (Z500) anomalies. Overall, the range of projected circulation patterns is similar to that in the current climate, especially in winter, whereas in summer the models project a general reduction in the magnitude of Z500 anomalies. Significant changes in pattern frequencies are also projected in summer, with an overall decrease in the frequency of patterns with large Z500 anomalies. In winter, patterns historically associated with anomalously cold weather in northern latitudes are projected to warm the most, and in summer the largest temperature increases are projected over inland areas. Precipitation is found to increase across all seasons and most SOM patterns. However, some summer patterns that are associated with above-average precipitation in the current climate are projected to become significantly drier by the end of the century.
Significance Statement
This paper uses a novel method to analyze projections of large-scale atmospheric circulation over the Pacific Northwest of North America, reducing the uncertainty of changes to the circulation patterns over the region under a high-emissions scenario of global warming.
Abstract
For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden–Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describing MJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a direct MJO influence on precipitation corresponding to propagation of the MJO’s convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October–November) and southwest monsoon (SWM; May–September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method.
Significance Statement
Rainfall variability at the subseasonal (weeks–months) time scale is critical to societal well-being, given its fundamental importance for agriculture, flood risk, hydropower generation, and disease incidence. Our work describes how such rainfall variability in Sri Lanka is impacted by the Madden–Julian oscillation, in which a region of enhanced rainfall and cloudiness, paired with a region of decreased rainfall and cloudiness, circles the globe every 30–60 days. Our results suggest that its influence on Sri Lankan rainfall may be strong enough that incorporating knowledge of the Madden–Julian oscillation into forecasts can improve the accuracy of rainfall prediction for Sri Lanka. Future work should develop a more comprehensive forecast method to assess viability in real-world forecasting scenarios.
Abstract
For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden–Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describing MJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a direct MJO influence on precipitation corresponding to propagation of the MJO’s convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October–November) and southwest monsoon (SWM; May–September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method.
Significance Statement
Rainfall variability at the subseasonal (weeks–months) time scale is critical to societal well-being, given its fundamental importance for agriculture, flood risk, hydropower generation, and disease incidence. Our work describes how such rainfall variability in Sri Lanka is impacted by the Madden–Julian oscillation, in which a region of enhanced rainfall and cloudiness, paired with a region of decreased rainfall and cloudiness, circles the globe every 30–60 days. Our results suggest that its influence on Sri Lankan rainfall may be strong enough that incorporating knowledge of the Madden–Julian oscillation into forecasts can improve the accuracy of rainfall prediction for Sri Lanka. Future work should develop a more comprehensive forecast method to assess viability in real-world forecasting scenarios.
Abstract
Under the background of global warming, the Arctic region has warmed faster than the Antarctic, which is referred to as asymmetric Arctic and Antarctic warming. The new generation of model simulations from the CMIP6 offers an opportunity to identify the major factors contributing to the asymmetric warming and its inter-model spread. In this study, the pre-industrial and abrupt-4 × CO2 experiments from eighteen CMIP6 models are examined to extract the asymmetric warming and its inter-model spread. A climate feedback-response analysis method is applied to reveal the contributions of external and internal feedback processes to the asymmetric warming and its inter-model spread, by decomposing total warming into the partial temperature changes caused by individual factors. It is found that a seasonal energy transfer mechanism (SETM) dominates in both polar warmings. The direct consequence of the sea ice declining in response to the anthropogenic forcing is an increase in the effective heat capacity of the ocean surface layer. Such increase in the effective heat capacity temporally withholds most of the extra solar energy absorbed during summer and then releases it during winter, contributing to stronger warming in winter. However, the background oceanic circulation in the Southern Ocean, namely the Antarctic Circumpolar Current, continually transports energy equatorward, resulting in a suppressed SETM and surface warming in the Antarctic. The key factor that accounts for inter-model spread in the asymmetric warming is the difference in their strength of SETM. The poleward atmospheric transport and water vapor feedback also contribute to the inter-model spread.
Abstract
Under the background of global warming, the Arctic region has warmed faster than the Antarctic, which is referred to as asymmetric Arctic and Antarctic warming. The new generation of model simulations from the CMIP6 offers an opportunity to identify the major factors contributing to the asymmetric warming and its inter-model spread. In this study, the pre-industrial and abrupt-4 × CO2 experiments from eighteen CMIP6 models are examined to extract the asymmetric warming and its inter-model spread. A climate feedback-response analysis method is applied to reveal the contributions of external and internal feedback processes to the asymmetric warming and its inter-model spread, by decomposing total warming into the partial temperature changes caused by individual factors. It is found that a seasonal energy transfer mechanism (SETM) dominates in both polar warmings. The direct consequence of the sea ice declining in response to the anthropogenic forcing is an increase in the effective heat capacity of the ocean surface layer. Such increase in the effective heat capacity temporally withholds most of the extra solar energy absorbed during summer and then releases it during winter, contributing to stronger warming in winter. However, the background oceanic circulation in the Southern Ocean, namely the Antarctic Circumpolar Current, continually transports energy equatorward, resulting in a suppressed SETM and surface warming in the Antarctic. The key factor that accounts for inter-model spread in the asymmetric warming is the difference in their strength of SETM. The poleward atmospheric transport and water vapor feedback also contribute to the inter-model spread.
Abstract
Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century.
Significance Statement
Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.
Abstract
Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century.
Significance Statement
Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.
Abstract
This study reveals that South China precipitation (SCP) anomalies tend to persist well from winter to the following spring after the late 1990s, favoring long-lasting drought or flood events over South China. Mechanism analysis indicates that the interdecadal changes in El Niño–Southern Oscillation (ENSO) and the preceding November central Asian snow cover could contribute to the increased persistence of winter-to-spring SCP anomalies. ENSO has a stable impact on winter SCP, whereas its impact on spring SCP is significantly enhanced after the late 1990s. With a weakened intensity and faster decay rate in the recent two decades, the ENSO-related spring SST anomalies over the tropical Pacific are relatively weaker, inducing a weakened and more southward-located western North Pacific anticyclone. This further leads to an interdecadal migration of the spring rainfall belt anomaly, consequently favoring the persistence of winter-to-spring SCP anomalies after the late 1990s. Additionally, the impacts of November central Asian snow cover on winter and spring SCP are both strengthened after the late 1990s. In the most recent two decades, the snow-cover-related cooling effect has become stronger, which induces winter cyclonic anomalies over Lake Baikal, favoring increased winter SCP. In addition, increased snow cover excites upward-propagating waves from the troposphere to the stratosphere, consequently weakening the stratospheric polar vortex. In spring, the stratospheric polar vortex signals propagate downward and result in a negative Arctic Oscillation in the troposphere, favoring more spring SCP. Therefore, central Asian snow cover is also conductive to the persistence of winter-to-spring SCP anomalies after the late 1990s.
Abstract
This study reveals that South China precipitation (SCP) anomalies tend to persist well from winter to the following spring after the late 1990s, favoring long-lasting drought or flood events over South China. Mechanism analysis indicates that the interdecadal changes in El Niño–Southern Oscillation (ENSO) and the preceding November central Asian snow cover could contribute to the increased persistence of winter-to-spring SCP anomalies. ENSO has a stable impact on winter SCP, whereas its impact on spring SCP is significantly enhanced after the late 1990s. With a weakened intensity and faster decay rate in the recent two decades, the ENSO-related spring SST anomalies over the tropical Pacific are relatively weaker, inducing a weakened and more southward-located western North Pacific anticyclone. This further leads to an interdecadal migration of the spring rainfall belt anomaly, consequently favoring the persistence of winter-to-spring SCP anomalies after the late 1990s. Additionally, the impacts of November central Asian snow cover on winter and spring SCP are both strengthened after the late 1990s. In the most recent two decades, the snow-cover-related cooling effect has become stronger, which induces winter cyclonic anomalies over Lake Baikal, favoring increased winter SCP. In addition, increased snow cover excites upward-propagating waves from the troposphere to the stratosphere, consequently weakening the stratospheric polar vortex. In spring, the stratospheric polar vortex signals propagate downward and result in a negative Arctic Oscillation in the troposphere, favoring more spring SCP. Therefore, central Asian snow cover is also conductive to the persistence of winter-to-spring SCP anomalies after the late 1990s.
Abstract
The Indian monsoon is of utmost concern to agriculture, the economy, and the livelihoods of billions in South Asia. However, little attention has been paid to the possibility of distinct subseasonal episodes phase-locked in the Indian monsoon annual cycle. This study addresses this gap by utilizing the self-organizing map (SOM) method to objectively classify six distinct subseasonal stages based on the 850-hPa wind fields. Each subseasonal stage ranges from 23 to 90 days. The Indian summer monsoon (ISM) consists of three substages, the ISM-onset, ISM-peak, and ISM-withdrawal, altogether contributing to 82% of the annual precipitation. The three substages signify the rapid northward advance, dominance, and gradual southward retreat of southwesterlies from mid-May to early October. The winter monsoon also comprises three substages (fall, winter, and spring), distinguishable by the latitude of the Arabian Sea high pressure ridge and hydrological conditions. This study proposes two compact indices based on zonal winds in the northern and southern Arabian Sea to measure the winter and summer monsoons, respectively. These indices capture the development and turnabouts of the six SOM-derived stages and can be used for subseasonal monsoon monitoring and forecasts. The spring and the ISM-onset episodes are highly susceptible to compound hazards of droughts and heatwaves, while the greatest flood risk occurs during the ISM-peak stage. The fall stage heralds the peak season for tropical storms over the Arabian Sea and the Bay of Bengal. The annual start and end dates of the ISM-peak are highly correlated (0.6–0.8) with the criteria-based dates proposed previously, supporting the delineation of the Indian monsoon subseasonal features.
Significance Statement
This research explores the existence of subseasonal features in the Indian monsoon annual cycle. Through the use of machine learning, we discover that the Indian summer monsoon and winter monsoon each consist of three substages. These substages’ evolution can be measured by two compact indices proposed herein, which can aid in subseasonal monsoon monitoring and forecasts in South Asia. Pertaining to hazard adaptations, this work pinpoints the subseasonal episodes most susceptible to droughts, heatwaves, floods, and tropical storms. High correlations are obtained when validating the substages’ yearly start and end dates against those documented in the existing literature, offering credibility to the subseasonal features of the Indian monsoon.
Abstract
The Indian monsoon is of utmost concern to agriculture, the economy, and the livelihoods of billions in South Asia. However, little attention has been paid to the possibility of distinct subseasonal episodes phase-locked in the Indian monsoon annual cycle. This study addresses this gap by utilizing the self-organizing map (SOM) method to objectively classify six distinct subseasonal stages based on the 850-hPa wind fields. Each subseasonal stage ranges from 23 to 90 days. The Indian summer monsoon (ISM) consists of three substages, the ISM-onset, ISM-peak, and ISM-withdrawal, altogether contributing to 82% of the annual precipitation. The three substages signify the rapid northward advance, dominance, and gradual southward retreat of southwesterlies from mid-May to early October. The winter monsoon also comprises three substages (fall, winter, and spring), distinguishable by the latitude of the Arabian Sea high pressure ridge and hydrological conditions. This study proposes two compact indices based on zonal winds in the northern and southern Arabian Sea to measure the winter and summer monsoons, respectively. These indices capture the development and turnabouts of the six SOM-derived stages and can be used for subseasonal monsoon monitoring and forecasts. The spring and the ISM-onset episodes are highly susceptible to compound hazards of droughts and heatwaves, while the greatest flood risk occurs during the ISM-peak stage. The fall stage heralds the peak season for tropical storms over the Arabian Sea and the Bay of Bengal. The annual start and end dates of the ISM-peak are highly correlated (0.6–0.8) with the criteria-based dates proposed previously, supporting the delineation of the Indian monsoon subseasonal features.
Significance Statement
This research explores the existence of subseasonal features in the Indian monsoon annual cycle. Through the use of machine learning, we discover that the Indian summer monsoon and winter monsoon each consist of three substages. These substages’ evolution can be measured by two compact indices proposed herein, which can aid in subseasonal monsoon monitoring and forecasts in South Asia. Pertaining to hazard adaptations, this work pinpoints the subseasonal episodes most susceptible to droughts, heatwaves, floods, and tropical storms. High correlations are obtained when validating the substages’ yearly start and end dates against those documented in the existing literature, offering credibility to the subseasonal features of the Indian monsoon.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-hour precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics scheme on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distribution of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step towards better simulation of precipitation intermittence in future model development. Lastly, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at suddaily time scales, as model evaluation heavily relies on high-quality observations.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-hour precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics scheme on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distribution of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step towards better simulation of precipitation intermittence in future model development. Lastly, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at suddaily time scales, as model evaluation heavily relies on high-quality observations.
Abstract
In this paper, we present a late summer (August–September) temperature reconstruction over the period 1792–2020 based on a tree-ring maximum latewood density (MXD) chronology for the southern Tibetan Plateau (TP). The reconstruction explained 66.2% of the variance in the instrumental temperature records during the calibration period 1960–2020 and captured the warming trend since the 1960s, which would support the current warming on the TP. In addition, a warming hiatus existed during 2001–12 and the last 20 years (2000–20) were the warmest period in the past two centuries. The reconstruction matched other MXD- and mean latewood density (LWD)-based late summer temperature reconstructions from neighboring regions, and fluctuated in synchrony with the Climatic Research Unit (CRU) Northern Hemisphere land surface temperature during 1850–2020. Multitaper method analysis and wavelet analysis revealed significant periodicities of 2–3, 20–30, and 40–60 years in the reconstructed series. Our reconstructed series was very consistent and highly correlated with the Atlantic multidecadal oscillation (AMO). During the warm phase of the AMO, higher pressure and divergent horizontal winds over the TP contribute to warmer summers in the region. In addition, we found that the southern TP experienced the lowest temperature and downward solar radiation in the second year following large volcanic eruptions. The decrease in downward solar radiation may be directly responsible for the occurrence of the lowest temperatures. The results indicate that the AMO and large volcanic eruptions were impacting factors on temperature in our study area.
Abstract
In this paper, we present a late summer (August–September) temperature reconstruction over the period 1792–2020 based on a tree-ring maximum latewood density (MXD) chronology for the southern Tibetan Plateau (TP). The reconstruction explained 66.2% of the variance in the instrumental temperature records during the calibration period 1960–2020 and captured the warming trend since the 1960s, which would support the current warming on the TP. In addition, a warming hiatus existed during 2001–12 and the last 20 years (2000–20) were the warmest period in the past two centuries. The reconstruction matched other MXD- and mean latewood density (LWD)-based late summer temperature reconstructions from neighboring regions, and fluctuated in synchrony with the Climatic Research Unit (CRU) Northern Hemisphere land surface temperature during 1850–2020. Multitaper method analysis and wavelet analysis revealed significant periodicities of 2–3, 20–30, and 40–60 years in the reconstructed series. Our reconstructed series was very consistent and highly correlated with the Atlantic multidecadal oscillation (AMO). During the warm phase of the AMO, higher pressure and divergent horizontal winds over the TP contribute to warmer summers in the region. In addition, we found that the southern TP experienced the lowest temperature and downward solar radiation in the second year following large volcanic eruptions. The decrease in downward solar radiation may be directly responsible for the occurrence of the lowest temperatures. The results indicate that the AMO and large volcanic eruptions were impacting factors on temperature in our study area.
Abstract
Previous studies have investigated how sea surface temperature (SST) affects the potential intensity of tropical cyclones (TCs). However, this is an upper bound only on the maximum near-surface azimuthal winds, and does not fully account for the effects of atmospheric moisture. Potential intensity might not vary in the same way as the total kinetic energy (WKE ) of a TC would with changing SST. WKE is related, via the conceptualization of the TC as a heat engine, to TC mechanical efficiency. We investigate how TC mechanical efficiency varies with SST in a series of moist, axisymmetric, radiative-convective numerical experiments with constant SSTs ranging from 295K to 307.5K. We find a −2.1 %K−1 decrease in the mechanical efficiency with SST. While the increase in the net heat energy gained by the TC heat engine acts to increase WKE , the mechanical efficiency still decreases with SST due to the effects of moisture on WKE and on the total heat input to the TC. Moist convection in an unsaturated atmosphere is associated with substantial irreversible entropy production, which detracts from the energy that the TC can use to power its winds. The increasing moisture content in a warmer atmosphere predicted by Clausius-Clapeyron scaling leads this irreversibility to increase in an unsaturated atmosphere, presenting a larger penalty on WKE and decreasing the mechanical efficiency. Our results highlight the importance of giving full consideration to the effects of moisture on the TC heat engine in studies of how climate affects TCs.
Abstract
Previous studies have investigated how sea surface temperature (SST) affects the potential intensity of tropical cyclones (TCs). However, this is an upper bound only on the maximum near-surface azimuthal winds, and does not fully account for the effects of atmospheric moisture. Potential intensity might not vary in the same way as the total kinetic energy (WKE ) of a TC would with changing SST. WKE is related, via the conceptualization of the TC as a heat engine, to TC mechanical efficiency. We investigate how TC mechanical efficiency varies with SST in a series of moist, axisymmetric, radiative-convective numerical experiments with constant SSTs ranging from 295K to 307.5K. We find a −2.1 %K−1 decrease in the mechanical efficiency with SST. While the increase in the net heat energy gained by the TC heat engine acts to increase WKE , the mechanical efficiency still decreases with SST due to the effects of moisture on WKE and on the total heat input to the TC. Moist convection in an unsaturated atmosphere is associated with substantial irreversible entropy production, which detracts from the energy that the TC can use to power its winds. The increasing moisture content in a warmer atmosphere predicted by Clausius-Clapeyron scaling leads this irreversibility to increase in an unsaturated atmosphere, presenting a larger penalty on WKE and decreasing the mechanical efficiency. Our results highlight the importance of giving full consideration to the effects of moisture on the TC heat engine in studies of how climate affects TCs.
