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Abstract
The 3-km Norwegian Reanalysis (NORA3) is a convection-permitting, nonhydrostatic hindcast for the North Sea, the Norwegian Sea, and the Barents Sea as well as the Scandinavian Peninsula. It has a horizontal resolution of 3 km and provides a full three-dimensional atmospheric state for the period 1995–2020 with a surface analysis and boundary conditions from ERA5, a global reanalysis. In complex terrain it is found to outperform both the host reanalysis ERA5 and also the earlier hydrostatic 10-km Norwegian Hindcast Archive (NORA10), in terms of 2-m temperature and daily precipitation. Of particular interest is the representation of extreme rainfall. It is found that the upper percentiles are much better represented than in ERA5, with very little bias up to 99.9%, suggesting that the new hindcast archive is well suited for hydrological mapping and extreme-value analysis of rainfall in complex terrain.
Significance Statement
High-resolution hindcasts that permit realistic convection allow very detailed modeling of the surface temperature, precipitation, and wind field in complex terrain. There is a need for detailed mapping of rainfall and temperature extremes (upper percentiles) to assess the impact of rapid climate change. We present an assessment of the performance of the model from Part I for near-surface temperature and precipitation, which are found to be much improved in comparison with ERA5 and with earlier NORA10. The focus is on the Norwegian mainland and the Svalbard Archipelago, because the complex terrain found in these regions is challenging to represent in weather prediction models. The improvement in precipitation statistics is particularly pronounced, with nearly unbiased results up to the 99.9th percentile.
Abstract
The 3-km Norwegian Reanalysis (NORA3) is a convection-permitting, nonhydrostatic hindcast for the North Sea, the Norwegian Sea, and the Barents Sea as well as the Scandinavian Peninsula. It has a horizontal resolution of 3 km and provides a full three-dimensional atmospheric state for the period 1995–2020 with a surface analysis and boundary conditions from ERA5, a global reanalysis. In complex terrain it is found to outperform both the host reanalysis ERA5 and also the earlier hydrostatic 10-km Norwegian Hindcast Archive (NORA10), in terms of 2-m temperature and daily precipitation. Of particular interest is the representation of extreme rainfall. It is found that the upper percentiles are much better represented than in ERA5, with very little bias up to 99.9%, suggesting that the new hindcast archive is well suited for hydrological mapping and extreme-value analysis of rainfall in complex terrain.
Significance Statement
High-resolution hindcasts that permit realistic convection allow very detailed modeling of the surface temperature, precipitation, and wind field in complex terrain. There is a need for detailed mapping of rainfall and temperature extremes (upper percentiles) to assess the impact of rapid climate change. We present an assessment of the performance of the model from Part I for near-surface temperature and precipitation, which are found to be much improved in comparison with ERA5 and with earlier NORA10. The focus is on the Norwegian mainland and the Svalbard Archipelago, because the complex terrain found in these regions is challenging to represent in weather prediction models. The improvement in precipitation statistics is particularly pronounced, with nearly unbiased results up to the 99.9th percentile.
Abstract
Knowledge of regional differences in future climate projections is important for effective adaptation strategies. Extreme events often arise regionally, but multiscale factors likely act together. Hence, we need discussion of multiple scales for the regional characteristics of future changes of extremes. In this study, using a large ensemble climate simulation database (d4PDF) created by global and regional climate models, the change in the temperature extreme defined as the top 10% of summertime daily maximum temperature in Japan is investigated under a globally 2- and 4-K-warmer climate, with emphasis on its regionality. Under global warming, the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A simple composite analysis of extreme events shows that the high temperature occurs under a site-specific spatial pattern of sea level pressure (SLP), with a common feature of a warm anomaly up to the upper troposphere over the sites. The SLP pattern reflects the local topography and favors a foehnlike wind that increases the near-surface temperature. The impact of climate change in SLP on the foehn-inducing pattern varies with site, leading to regional differences in high-temperature changes. Therefore, the dynamic response of SLP to global warming results in a characteristic spatial distribution for the high-temperature change, which differs from the distribution for the mean-temperature change that generally shows the thermodynamic response. The characteristic is expected to appear in mountainous regions of the world, and this study helps in understanding future projections of high temperature there.
Significance Statement
Regionality in future climate projections strongly influences the usefulness of adaptation strategies to climate change. This study indicates that the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A site-specific spatial pattern of sea level pressure (SLP) reflecting the local topography contributes to the location of high temperature via a foehnlike wind. The impact of climate change in SLP on the pattern varies with site and leads to the regionality in high-temperature changes, which is the dynamic response to global warming unlike the thermodynamic response appearing on the mean-temperature change. This study helps us to understand future projections of temperature extreme in mountainous regions and their surroundings around the world.
Abstract
Knowledge of regional differences in future climate projections is important for effective adaptation strategies. Extreme events often arise regionally, but multiscale factors likely act together. Hence, we need discussion of multiple scales for the regional characteristics of future changes of extremes. In this study, using a large ensemble climate simulation database (d4PDF) created by global and regional climate models, the change in the temperature extreme defined as the top 10% of summertime daily maximum temperature in Japan is investigated under a globally 2- and 4-K-warmer climate, with emphasis on its regionality. Under global warming, the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A simple composite analysis of extreme events shows that the high temperature occurs under a site-specific spatial pattern of sea level pressure (SLP), with a common feature of a warm anomaly up to the upper troposphere over the sites. The SLP pattern reflects the local topography and favors a foehnlike wind that increases the near-surface temperature. The impact of climate change in SLP on the foehn-inducing pattern varies with site, leading to regional differences in high-temperature changes. Therefore, the dynamic response of SLP to global warming results in a characteristic spatial distribution for the high-temperature change, which differs from the distribution for the mean-temperature change that generally shows the thermodynamic response. The characteristic is expected to appear in mountainous regions of the world, and this study helps in understanding future projections of high temperature there.
Significance Statement
Regionality in future climate projections strongly influences the usefulness of adaptation strategies to climate change. This study indicates that the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A site-specific spatial pattern of sea level pressure (SLP) reflecting the local topography contributes to the location of high temperature via a foehnlike wind. The impact of climate change in SLP on the pattern varies with site and leads to the regionality in high-temperature changes, which is the dynamic response to global warming unlike the thermodynamic response appearing on the mean-temperature change. This study helps us to understand future projections of temperature extreme in mountainous regions and their surroundings around the world.
Abstract
Intentional release of hygroscopic particles, or seeding, in convective clouds is one of the postulated methods to artificially enhance rainfall. Motivated by the general uncertainty in the underlying physics, this work employs a large-eddy simulation code together with a detailed aerosol–cloud microphysics model to investigate the conditions and processes conducive to seeding in the United Arab Emirates. Mixed-phase processes are identified as the main source for rainfall in convective clouds in this area owing to the continental aerosol characteristics and a high cloud-base altitude relatively close to the freezing level. Subsequently, our model experiments highlight the importance of mixed-phase processes in mediating the effects of hygroscopic seeding on rainfall as well. The seeding particles acted to accelerate riming by increasing the number of large droplets taken above the freezing level by the convective updrafts. The rime fraction was increased by up to 15%, which promotes the growth of the frozen hydrometeors, eventually leading to enhanced rainfall via melting. The peak enhancement in surface rainfall was up to 20%–30%, although this is almost certainly an overestimation relative to real-world operations because of the simplified description of the seeding in the model. The strongest rain enhancement was obtained with a high background aerosol concentration of approximately 4500 cm−3, whereas reduced aerosol resulted in weaker enhancement. The latter case showed an overall higher rime fraction indicating an already efficient precipitation formation process, which suppressed the seeding-induced enhancement. The conclusions of our work encourage more careful consideration of the mixed-phase processes in quantifying the hygroscopic seeding effects in continental convective clouds.
Abstract
Intentional release of hygroscopic particles, or seeding, in convective clouds is one of the postulated methods to artificially enhance rainfall. Motivated by the general uncertainty in the underlying physics, this work employs a large-eddy simulation code together with a detailed aerosol–cloud microphysics model to investigate the conditions and processes conducive to seeding in the United Arab Emirates. Mixed-phase processes are identified as the main source for rainfall in convective clouds in this area owing to the continental aerosol characteristics and a high cloud-base altitude relatively close to the freezing level. Subsequently, our model experiments highlight the importance of mixed-phase processes in mediating the effects of hygroscopic seeding on rainfall as well. The seeding particles acted to accelerate riming by increasing the number of large droplets taken above the freezing level by the convective updrafts. The rime fraction was increased by up to 15%, which promotes the growth of the frozen hydrometeors, eventually leading to enhanced rainfall via melting. The peak enhancement in surface rainfall was up to 20%–30%, although this is almost certainly an overestimation relative to real-world operations because of the simplified description of the seeding in the model. The strongest rain enhancement was obtained with a high background aerosol concentration of approximately 4500 cm−3, whereas reduced aerosol resulted in weaker enhancement. The latter case showed an overall higher rime fraction indicating an already efficient precipitation formation process, which suppressed the seeding-induced enhancement. The conclusions of our work encourage more careful consideration of the mixed-phase processes in quantifying the hygroscopic seeding effects in continental convective clouds.
Abstract
The evaluation of the representation of the surface downward shortwave flux (DSF) from atmospheric reanalysis data products is required to obtain reliable information for the resource assessment of surface solar energy. The representation of the DSF from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis data product was evaluated using surface solar radiation from ground-based observations in Japan. The cloud fraction (CFR) and cloud optical thickness (COT) from Moderate Resolution Imaging Spectroradiometer (MODIS) were also used as references. The CFR from MERRA-2 tends to be smaller than that from MODIS, and the correlation between the difference in the CFR and that in the DSF is negative. The correlation between the difference in the COT and that in the DSF is weakly negative. To quantify the effects of the difference in the CFR and COT to that of the DSF, a regression model based on an artificial neural network architecture that emulates the process of the DSF in MERRA-2 was constructed. Numerical experiments using the emulator quantify contributions of each of the differences in the CFR and COT and joint contributions of the two variables. In addition, a cluster analysis was performed to clarify the differences in the seasonal changes in the monthly mean bias error (MBE) in the DSF among ground observation stations, and three clusters were identified. Contributions of the differences in the CFR and COT to the seasonal change in the monthly MBE were also clarified using the results of the numerical experiments.
Abstract
The evaluation of the representation of the surface downward shortwave flux (DSF) from atmospheric reanalysis data products is required to obtain reliable information for the resource assessment of surface solar energy. The representation of the DSF from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis data product was evaluated using surface solar radiation from ground-based observations in Japan. The cloud fraction (CFR) and cloud optical thickness (COT) from Moderate Resolution Imaging Spectroradiometer (MODIS) were also used as references. The CFR from MERRA-2 tends to be smaller than that from MODIS, and the correlation between the difference in the CFR and that in the DSF is negative. The correlation between the difference in the COT and that in the DSF is weakly negative. To quantify the effects of the difference in the CFR and COT to that of the DSF, a regression model based on an artificial neural network architecture that emulates the process of the DSF in MERRA-2 was constructed. Numerical experiments using the emulator quantify contributions of each of the differences in the CFR and COT and joint contributions of the two variables. In addition, a cluster analysis was performed to clarify the differences in the seasonal changes in the monthly mean bias error (MBE) in the DSF among ground observation stations, and three clusters were identified. Contributions of the differences in the CFR and COT to the seasonal change in the monthly MBE were also clarified using the results of the numerical experiments.
Abstract
In this study, we examine variations in boundary layer processes spanning the shallow-to-deep cumulus transition. This is accomplished by differentiating boundary layer properties on the basis of convective outcomes, ranging from shallow to deep, as observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma. Doppler lidar, radar, and radiosonde data are combined to determine statistical differences in boundary layer and cloud-layer properties using a large sample (236) of days with a range of convective outcomes: shallow, congestus, and deep convection. In these analyses, the radar characterizes diurnal cloud depth, the lidar quantifies updraft and downdraft properties in the subcloud layer, and daily radiosonde data provide the convective inhibition (CIN). Combined, these data are used to test the hypothesis that deep convection occurs when the strength of the boundary layer turbulence (i.e., TKE) exceeds the strength of the energy barrier (i.e., CIN) at the top of the CBL. Results show that days with deep convective clouds have significantly lower vertical velocity variance and weaker updrafts within the subcloud layer. However, CIN values are also found to be significantly lower on deep convective days, allowing for these weaker updrafts to penetrate the energy barrier and reach the level of free convection. In contrast, shallow convective outcomes occur when the updrafts are strong in an absolute sense but are weak when compared with the strength of the energy barrier. These findings support the use of the CIN/TKE framework in parameterizing convection in coarse resolution models.
Abstract
In this study, we examine variations in boundary layer processes spanning the shallow-to-deep cumulus transition. This is accomplished by differentiating boundary layer properties on the basis of convective outcomes, ranging from shallow to deep, as observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma. Doppler lidar, radar, and radiosonde data are combined to determine statistical differences in boundary layer and cloud-layer properties using a large sample (236) of days with a range of convective outcomes: shallow, congestus, and deep convection. In these analyses, the radar characterizes diurnal cloud depth, the lidar quantifies updraft and downdraft properties in the subcloud layer, and daily radiosonde data provide the convective inhibition (CIN). Combined, these data are used to test the hypothesis that deep convection occurs when the strength of the boundary layer turbulence (i.e., TKE) exceeds the strength of the energy barrier (i.e., CIN) at the top of the CBL. Results show that days with deep convective clouds have significantly lower vertical velocity variance and weaker updrafts within the subcloud layer. However, CIN values are also found to be significantly lower on deep convective days, allowing for these weaker updrafts to penetrate the energy barrier and reach the level of free convection. In contrast, shallow convective outcomes occur when the updrafts are strong in an absolute sense but are weak when compared with the strength of the energy barrier. These findings support the use of the CIN/TKE framework in parameterizing convection in coarse resolution models.
Abstract
Satellite-derived observations of land surface temperature (LST) are being utilized in a growing number of land surface studies; however, these observations are generally obtained from optical sensors that exclude cloudy observations of the land surface. The impact of using only clear-sky observations of land surfaces on monthly and annual estimates of daytime LST over two U.S. Climate Reference Network (USCRN) sites was evaluated over five years with daily in situ LST observations available for all-sky (clear and cloudy) conditions. The in situ LST observations were obtained for the nominal daytime observations associated with the MODIS sensors on board the Terra and Aqua satellites and were identified as all-sky or clear-sky conditions by utilizing cloud information provided with the MODIS LST product. Both monthly/annual mean and monthly/annual maximum values of daytime LST were significantly different when only clear-sky values were utilized, in comparison with all-sky values. Monthly averaged differences between the mean clear- and all-sky daytime LST (dLST) values ranged from −0.1° ± 1.5°C for January to 5.6° ± 1.8°C for May. Annually averaged dLST values, over the five years of the study, were 2.58°C, and differences between the maximum values of clear- and all-sky daytime LST values were −1.03°C. Although significant differences between mean annual clear-sky and all-sky daytime LST values were more frequent than differences observed for the annual maximum daytime LST values, the results suggest that the exclusive use of either mean or maximum clear-sky daytime LST values is not advisable for applications in which the use of daytime all-sky LST values would be more applicable.
Abstract
Satellite-derived observations of land surface temperature (LST) are being utilized in a growing number of land surface studies; however, these observations are generally obtained from optical sensors that exclude cloudy observations of the land surface. The impact of using only clear-sky observations of land surfaces on monthly and annual estimates of daytime LST over two U.S. Climate Reference Network (USCRN) sites was evaluated over five years with daily in situ LST observations available for all-sky (clear and cloudy) conditions. The in situ LST observations were obtained for the nominal daytime observations associated with the MODIS sensors on board the Terra and Aqua satellites and were identified as all-sky or clear-sky conditions by utilizing cloud information provided with the MODIS LST product. Both monthly/annual mean and monthly/annual maximum values of daytime LST were significantly different when only clear-sky values were utilized, in comparison with all-sky values. Monthly averaged differences between the mean clear- and all-sky daytime LST (dLST) values ranged from −0.1° ± 1.5°C for January to 5.6° ± 1.8°C for May. Annually averaged dLST values, over the five years of the study, were 2.58°C, and differences between the maximum values of clear- and all-sky daytime LST values were −1.03°C. Although significant differences between mean annual clear-sky and all-sky daytime LST values were more frequent than differences observed for the annual maximum daytime LST values, the results suggest that the exclusive use of either mean or maximum clear-sky daytime LST values is not advisable for applications in which the use of daytime all-sky LST values would be more applicable.
Abstract
Patterns of freshwater availability—its variability and distribution—are already shifting as a function of global climate change and climate variability. High-resolution global gridded reanalysis products present an important tool to understand the already observed changes and thereby improve future scenarios as the climate evolves. A historical 100-yr-long district rainfall dataset and a unique set of highly detailed rainfall data from the highveld of South Africa spanning a 10-yr period provide an opportunity to independently evaluate the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis product. Evaluation is challenged by the episodic nature of significant rainfall events of southern Africa as well as differences in spatial and temporal resolution between model output and surface precipitation data. Here we present a convergent methodology spanning annual to event time scales and regional to gauge-level spatial scales to identify the characteristics of systematic biases in variability and amount of rain as well as timing of events. We find that ERA5 is consistently wetter than observed in ways that affect the timing of individual events while performing well on metrics associated with large-scale trends and seasonal variability. Errors are associated with both stratiform and convective rainfall types, but the timing of onset of convective rainfall is a challenge that is critical in this summer-rainfall-dominated region.
Significance Statement
High-resolution gridded datasets are invaluable tools for gaining improved understanding of historical rainfall variations under the influence of climate change. In addition, these datasets provide consistent information for purposes such as water resources management. Quantification of dataset biases provides important guidance for robust decision-making as well as for the development of future climate scenarios. However, rainfall is an especially challenging quantity to assess. With the increasing incidence of drought and flood, methods that independently validate this high-resolution gridded data are needed to ensure high-quality knowledge support. This study demonstrates an approach using convergent streams of evidence to assess the European Centre for Medium-Range Weather Forecasts gridded rainfall dataset with the purpose of better understanding the evolving characteristics of rainfall in southern Africa.
Abstract
Patterns of freshwater availability—its variability and distribution—are already shifting as a function of global climate change and climate variability. High-resolution global gridded reanalysis products present an important tool to understand the already observed changes and thereby improve future scenarios as the climate evolves. A historical 100-yr-long district rainfall dataset and a unique set of highly detailed rainfall data from the highveld of South Africa spanning a 10-yr period provide an opportunity to independently evaluate the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis product. Evaluation is challenged by the episodic nature of significant rainfall events of southern Africa as well as differences in spatial and temporal resolution between model output and surface precipitation data. Here we present a convergent methodology spanning annual to event time scales and regional to gauge-level spatial scales to identify the characteristics of systematic biases in variability and amount of rain as well as timing of events. We find that ERA5 is consistently wetter than observed in ways that affect the timing of individual events while performing well on metrics associated with large-scale trends and seasonal variability. Errors are associated with both stratiform and convective rainfall types, but the timing of onset of convective rainfall is a challenge that is critical in this summer-rainfall-dominated region.
Significance Statement
High-resolution gridded datasets are invaluable tools for gaining improved understanding of historical rainfall variations under the influence of climate change. In addition, these datasets provide consistent information for purposes such as water resources management. Quantification of dataset biases provides important guidance for robust decision-making as well as for the development of future climate scenarios. However, rainfall is an especially challenging quantity to assess. With the increasing incidence of drought and flood, methods that independently validate this high-resolution gridded data are needed to ensure high-quality knowledge support. This study demonstrates an approach using convergent streams of evidence to assess the European Centre for Medium-Range Weather Forecasts gridded rainfall dataset with the purpose of better understanding the evolving characteristics of rainfall in southern Africa.
Abstract
Under the intensification of global warming, the characteristics of the Three Rivers source region (TRSR; i.e., headwaters of the Yellow River, the Yangtze River, and the Lancang River) in China were diagnosed in the summer season from 1979 to 2015 using observations and reanalysis data. The diagnoses indicate that summer precipitation decreased from 1979 to 2002 [by 9.01 mm day−1 (10 yr)−1; p < 0.05 by Student’s t test] and increased significantly after 2002 [by 5.52 mm day−1 (10 yr)−1]. This abrupt change year (2002) was further confirmed by the cumulative anomaly method, the moving t-test method, and the Yamamoto method. By compositing the thermodynamics before and after the abrupt change year (2002), the results reveal that increased water vapor and more substantial lower-level convergence were present over the TRSR during 2003–15. This marked interdecadal variability in the TRSR summer precipitation responded to the interdecadal position and intensity of the large-scale forcing East Asian westerly jet (EAWJ), which is significantly modulated by the low-frequency variability associated with Southern Oscillation index. The connection between the interannual TRSR precipitation and the location and intensity of EAWJ was also explored. The position index of the EAWJ is negatively (with correlation coefficient R of −0.446; p < 0.05 by Student’s t test) correlated with the precipitation over the TRSR, implying that southward and northward years of EAWJ are respectively associated with intensifying and weakening the TRSR summer precipitation, whereas the intensity of EAWJ is insignificantly correlated with the TRSR summer precipitation.
Abstract
Under the intensification of global warming, the characteristics of the Three Rivers source region (TRSR; i.e., headwaters of the Yellow River, the Yangtze River, and the Lancang River) in China were diagnosed in the summer season from 1979 to 2015 using observations and reanalysis data. The diagnoses indicate that summer precipitation decreased from 1979 to 2002 [by 9.01 mm day−1 (10 yr)−1; p < 0.05 by Student’s t test] and increased significantly after 2002 [by 5.52 mm day−1 (10 yr)−1]. This abrupt change year (2002) was further confirmed by the cumulative anomaly method, the moving t-test method, and the Yamamoto method. By compositing the thermodynamics before and after the abrupt change year (2002), the results reveal that increased water vapor and more substantial lower-level convergence were present over the TRSR during 2003–15. This marked interdecadal variability in the TRSR summer precipitation responded to the interdecadal position and intensity of the large-scale forcing East Asian westerly jet (EAWJ), which is significantly modulated by the low-frequency variability associated with Southern Oscillation index. The connection between the interannual TRSR precipitation and the location and intensity of EAWJ was also explored. The position index of the EAWJ is negatively (with correlation coefficient R of −0.446; p < 0.05 by Student’s t test) correlated with the precipitation over the TRSR, implying that southward and northward years of EAWJ are respectively associated with intensifying and weakening the TRSR summer precipitation, whereas the intensity of EAWJ is insignificantly correlated with the TRSR summer precipitation.
Abstract
Cloud vertical profile measurements from the CALIPSO and CloudSat active sensors are used to improve top-of-atmosphere (TOA) shortwave (SW) broadband (BB) irradiance computations. The active sensor measurements, which occasionally miss parts of the cloud columns because of the full attenuation of sensor signals, surface clutter, or insensitivity to a certain range of cloud particle sizes, are adjusted using column-integrated cloud optical depth derived from the passive MODIS sensor. Specifically, we consider two steps in generating cloud profiles from multiple sensors for irradiance computations. First, cloud extinction coefficient and cloud effective radius (CER) profiles are merged using available active and passive measurements. Second, the merged cloud extinction profiles are constrained by the MODIS visible scaled cloud optical depth, defined as a visible cloud optical depth multiplied by (1 − asymmetry parameter), to compensate for missing cloud parts by active sensors. It is shown that the multisensor-combined cloud profiles significantly reduce positive TOA SW BB biases, relative to those with MODIS-derived cloud properties only. The improvement is more pronounced for optically thick clouds, where MODIS ice CER is largely underestimated. Within the SW BB (0.18–4 μm), the 1.04–1.90-μm spectral region is mainly affected by the CER, where both the cloud absorption and solar incoming irradiance are considerable.
Significance Statement
The purpose of this study is to improve shortwave irradiance computations at the top of the atmosphere by using combined cloud properties from active and passive sensor measurements. Relative to the simulation results with passive sensor cloud measurements only, the combined cloud profiles provide more accurate shortwave simulation results. This is achieved by more realistic profiles of cloud extinction coefficient and cloud particle effective radius. The benefit is pronounced for optically thick clouds composed of large ice particles.
Abstract
Cloud vertical profile measurements from the CALIPSO and CloudSat active sensors are used to improve top-of-atmosphere (TOA) shortwave (SW) broadband (BB) irradiance computations. The active sensor measurements, which occasionally miss parts of the cloud columns because of the full attenuation of sensor signals, surface clutter, or insensitivity to a certain range of cloud particle sizes, are adjusted using column-integrated cloud optical depth derived from the passive MODIS sensor. Specifically, we consider two steps in generating cloud profiles from multiple sensors for irradiance computations. First, cloud extinction coefficient and cloud effective radius (CER) profiles are merged using available active and passive measurements. Second, the merged cloud extinction profiles are constrained by the MODIS visible scaled cloud optical depth, defined as a visible cloud optical depth multiplied by (1 − asymmetry parameter), to compensate for missing cloud parts by active sensors. It is shown that the multisensor-combined cloud profiles significantly reduce positive TOA SW BB biases, relative to those with MODIS-derived cloud properties only. The improvement is more pronounced for optically thick clouds, where MODIS ice CER is largely underestimated. Within the SW BB (0.18–4 μm), the 1.04–1.90-μm spectral region is mainly affected by the CER, where both the cloud absorption and solar incoming irradiance are considerable.
Significance Statement
The purpose of this study is to improve shortwave irradiance computations at the top of the atmosphere by using combined cloud properties from active and passive sensor measurements. Relative to the simulation results with passive sensor cloud measurements only, the combined cloud profiles provide more accurate shortwave simulation results. This is achieved by more realistic profiles of cloud extinction coefficient and cloud particle effective radius. The benefit is pronounced for optically thick clouds composed of large ice particles.
Abstract
A 2020 metastudy by Knutson et al. gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. The results form a great resource for those who model the impacts of tropical cyclones. However, a number of steps of processing may be required to use the results in practice. These include interpolation in time, distribution fitting, and reverse engineering of correlations. In this paper we study another processing step that may be required, which is adjusting the frequency change results so that they apply to landfalling frequencies. An adjustment is required because the metastudy results give frequency adjustments as a function of storm lifetime maximum intensity rather than landfall intensity. Increases in the frequency of category-4 and category-5 storms, by lifetime maximum intensity, then contribute to increases in the frequencies of storms of all intensities at landfall. We consider North Atlantic Ocean storms and use historical storm information to quantify this effect as a function of landfall intensity and region. Whereas the original metastudy results suggest that the mean frequency of category-3 storms will decrease, our analysis suggests that the mean frequency of landfalling category-3 storms will increase. Our results are highly uncertain, particularly because we assume that tracks and genesis locations of storms will not change, even though some recent climate model results suggest otherwise. However, making the adjustments we describe is likely to be a better way to model future landfall risk than applying the original metastudy frequency changes directly at landfall.
Significance Statement
A recent metastudy gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. For the North Atlantic Ocean, we show how to convert these results to changes at landfall. This conversion increases the changes in the frequencies of storms in intensity categories 0–3, and, in particular, the mean frequency change of storms in category 3 flips from decreasing to increasing in most regions.
Abstract
A 2020 metastudy by Knutson et al. gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. The results form a great resource for those who model the impacts of tropical cyclones. However, a number of steps of processing may be required to use the results in practice. These include interpolation in time, distribution fitting, and reverse engineering of correlations. In this paper we study another processing step that may be required, which is adjusting the frequency change results so that they apply to landfalling frequencies. An adjustment is required because the metastudy results give frequency adjustments as a function of storm lifetime maximum intensity rather than landfall intensity. Increases in the frequency of category-4 and category-5 storms, by lifetime maximum intensity, then contribute to increases in the frequencies of storms of all intensities at landfall. We consider North Atlantic Ocean storms and use historical storm information to quantify this effect as a function of landfall intensity and region. Whereas the original metastudy results suggest that the mean frequency of category-3 storms will decrease, our analysis suggests that the mean frequency of landfalling category-3 storms will increase. Our results are highly uncertain, particularly because we assume that tracks and genesis locations of storms will not change, even though some recent climate model results suggest otherwise. However, making the adjustments we describe is likely to be a better way to model future landfall risk than applying the original metastudy frequency changes directly at landfall.
Significance Statement
A recent metastudy gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. For the North Atlantic Ocean, we show how to convert these results to changes at landfall. This conversion increases the changes in the frequencies of storms in intensity categories 0–3, and, in particular, the mean frequency change of storms in category 3 flips from decreasing to increasing in most regions.
