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Abstract
Fog plays a vital role in maintaining ecosystems in montane cloud forests. In these forests, a large amount of water on the surface of leaves and canopy (hereafter canopy water) evaporates during the morning. This biophysical process plays a critical factor in regulating afternoon fog formation. Recent studies have found that alterations in precipitation, temperature, humidity, and CO2 concentrations associated with future climate changes may affect terrestrial hydroclimatology, but the responses in cloud forests remain unclear. Utilizing numerical experiments with the Community Land Model, we explored changes in surface evaporative fluxes in Chi-Lan Mountain cloud forests in northeastern Taiwan under the RCP8.5 scenario with changes in the aforementioned various atmospheric variables. The results showed that increased rainfall intensity in climate change runs decreased the accumulation of canopy water, while larger water vapor concentrations led to more nighttime condensation on leaves. Elevated CO2 concentrations did not greatly impact canopy water amounts, but photosynthesis was enhanced, while transpiration was reduced and contributed to decreased latent heat fluxes, implying the importance of forest plant physiology in modulating land evaporative fluxes. Evapotranspiration decreased in Chi-Lan due to multiple combined factors, in contrast to the expected intensification in the global water cycle under global warming. The study, however, is restricted to an offline land surface model without land–atmosphere interactions and the interactions with adjacent grids, which deserves further analyses for the water cycle changes in the montane cloud forest regions.
Abstract
Fog plays a vital role in maintaining ecosystems in montane cloud forests. In these forests, a large amount of water on the surface of leaves and canopy (hereafter canopy water) evaporates during the morning. This biophysical process plays a critical factor in regulating afternoon fog formation. Recent studies have found that alterations in precipitation, temperature, humidity, and CO2 concentrations associated with future climate changes may affect terrestrial hydroclimatology, but the responses in cloud forests remain unclear. Utilizing numerical experiments with the Community Land Model, we explored changes in surface evaporative fluxes in Chi-Lan Mountain cloud forests in northeastern Taiwan under the RCP8.5 scenario with changes in the aforementioned various atmospheric variables. The results showed that increased rainfall intensity in climate change runs decreased the accumulation of canopy water, while larger water vapor concentrations led to more nighttime condensation on leaves. Elevated CO2 concentrations did not greatly impact canopy water amounts, but photosynthesis was enhanced, while transpiration was reduced and contributed to decreased latent heat fluxes, implying the importance of forest plant physiology in modulating land evaporative fluxes. Evapotranspiration decreased in Chi-Lan due to multiple combined factors, in contrast to the expected intensification in the global water cycle under global warming. The study, however, is restricted to an offline land surface model without land–atmosphere interactions and the interactions with adjacent grids, which deserves further analyses for the water cycle changes in the montane cloud forest regions.
Abstract
GRACE and GRACE Follow-On (GRACE-FO) mission data are utilized to assess mass flux derived from the North American Regional Reanalysis (NARR) and the NLDAS-2 Noah land surface model via multiple water balance formulations. Water balances are computed for 18 medium size basins in North America at the USGS Watershed Boundary Dataset HU2 level over the span of the GRACE and GRACE-FO missions (2002–21). Performance of model-derived mass flux is presented in the context of statistical agreement to changes in terrestrial water storage (ΔTWS) derived from Center for Space Research (CSR) GRACE RL06 mass concentrations (mascons), and GRACE and NARR uncertainty is estimated against comparable datasets. The land surface water balance method utilizing NLDAS-2 Noah consistently outperforms the total column method utilizing NARR, which is likely due to enhanced precipitation forcing and an updated Noah model version used in NLDAS-2. The surface approach to the calculation of atmospheric moisture flux divergence is carried through the presented analyses and is demonstrated to be comparable in performance to the more common volume approach. Mass balance methodology, basin characteristics, and ΔTWS signal characteristics are assessed to quantify effects on model performance and while factors such as basin size, basin average topography gradient, and ΔTWS annual amplitude are shown to have a measurable effect on model performance, no single factor exhibited a dominant or consistent effect. Drought conditions are shown to have a significant temporally localized effect on model-derived mass flux accuracy, with NARR being particularly susceptible to this effect.
Significance Statement
Measurements of Earth’s gravity field from the GRACE and GRACE-FO satellite missions are utilized to create estimates of water storage changes in 18 North American river basins that are compared to changes in water storage calculated from an atmospheric model reanalysis (NARR) and a land surface model (NLDAS-2 Noah). The resulting comparison demonstrates that certain basin characteristics can have a slight effect on model accuracy, while climatic conditions such as drought can have a major impact on model accuracy. This work provides useful quantification of when and where modeled water transport loses accuracy, which is integral to our understanding of the present and future distribution of this crucial resource and the natural processes that affect it.
Abstract
GRACE and GRACE Follow-On (GRACE-FO) mission data are utilized to assess mass flux derived from the North American Regional Reanalysis (NARR) and the NLDAS-2 Noah land surface model via multiple water balance formulations. Water balances are computed for 18 medium size basins in North America at the USGS Watershed Boundary Dataset HU2 level over the span of the GRACE and GRACE-FO missions (2002–21). Performance of model-derived mass flux is presented in the context of statistical agreement to changes in terrestrial water storage (ΔTWS) derived from Center for Space Research (CSR) GRACE RL06 mass concentrations (mascons), and GRACE and NARR uncertainty is estimated against comparable datasets. The land surface water balance method utilizing NLDAS-2 Noah consistently outperforms the total column method utilizing NARR, which is likely due to enhanced precipitation forcing and an updated Noah model version used in NLDAS-2. The surface approach to the calculation of atmospheric moisture flux divergence is carried through the presented analyses and is demonstrated to be comparable in performance to the more common volume approach. Mass balance methodology, basin characteristics, and ΔTWS signal characteristics are assessed to quantify effects on model performance and while factors such as basin size, basin average topography gradient, and ΔTWS annual amplitude are shown to have a measurable effect on model performance, no single factor exhibited a dominant or consistent effect. Drought conditions are shown to have a significant temporally localized effect on model-derived mass flux accuracy, with NARR being particularly susceptible to this effect.
Significance Statement
Measurements of Earth’s gravity field from the GRACE and GRACE-FO satellite missions are utilized to create estimates of water storage changes in 18 North American river basins that are compared to changes in water storage calculated from an atmospheric model reanalysis (NARR) and a land surface model (NLDAS-2 Noah). The resulting comparison demonstrates that certain basin characteristics can have a slight effect on model accuracy, while climatic conditions such as drought can have a major impact on model accuracy. This work provides useful quantification of when and where modeled water transport loses accuracy, which is integral to our understanding of the present and future distribution of this crucial resource and the natural processes that affect it.
Abstract
Shallow groundwater evaporation (Eg ) is a major component of the hydrological cycle, especially in semiarid and arid locations. Empirical methods are commonly used to estimate Eg . However, most of these methods can only weakly represent Eg variations along the soil depth and do not consider the energy driver. In this paper, a temperature coefficient was proposed and incorporated into two preferred empirical models to characterize the impacts of soil temperature and air temperature lags on Eg . The method was evaluated using in situ daily data obtained from nonweighing bare soil lysimeters. The results indicated that the models that considered the temperature gradient variable (T) conformed to the changes in the actual Eg values with depth more appropriately than the original models, accompanied by 4.3%–8.8% accuracy improvements overall. Shallow groundwater evaporation Eg was found to be influenced by the water table depth (H), T, and pan evaporation (E 0) in descending order, and strong interactions were found between H and T. Moreover, the impact of precipitation on Eg was investigated; measurements from dry days without precipitation revealed the actual Eg process, the relative errors in the cumulative Eg values derived at different depths demonstrated a positive relationship with infiltration recharge, and the errors related to precipitation induced 6.7%–8.3% Eg underestimations. These results contribute to a better understanding of evaporative losses from shallow groundwater and the typical Eg situation that occurs simultaneously with recharge, and they provide promising perspectives for corresponding integrated hydrologic modeling research.
Abstract
Shallow groundwater evaporation (Eg ) is a major component of the hydrological cycle, especially in semiarid and arid locations. Empirical methods are commonly used to estimate Eg . However, most of these methods can only weakly represent Eg variations along the soil depth and do not consider the energy driver. In this paper, a temperature coefficient was proposed and incorporated into two preferred empirical models to characterize the impacts of soil temperature and air temperature lags on Eg . The method was evaluated using in situ daily data obtained from nonweighing bare soil lysimeters. The results indicated that the models that considered the temperature gradient variable (T) conformed to the changes in the actual Eg values with depth more appropriately than the original models, accompanied by 4.3%–8.8% accuracy improvements overall. Shallow groundwater evaporation Eg was found to be influenced by the water table depth (H), T, and pan evaporation (E 0) in descending order, and strong interactions were found between H and T. Moreover, the impact of precipitation on Eg was investigated; measurements from dry days without precipitation revealed the actual Eg process, the relative errors in the cumulative Eg values derived at different depths demonstrated a positive relationship with infiltration recharge, and the errors related to precipitation induced 6.7%–8.3% Eg underestimations. These results contribute to a better understanding of evaporative losses from shallow groundwater and the typical Eg situation that occurs simultaneously with recharge, and they provide promising perspectives for corresponding integrated hydrologic modeling research.
Abstract
Land surface models (LSMs) rely on vegetation parameters for use in hydrological and energy balance analysis, monitoring, and forecasting. This study examines the influence that vegetation representation in the Noah-Multiparameterization (Noah-MP) LSM has on hydrological simulations across the diverse climate zones of western tropical South America (WTSA), with specific consideration of hydrological variability associated with El Niño–Southern Oscillation (ENSO). The influence of model representation of vegetation on simulated hydrology is evaluated through three simulation experiments that use 1) satellite-derived constant MODIS; 2) satellite-derived time-varying MODIS; and 3) the Noah-MP dynamic leaf model. We find substantial differences in vegetation fields between these simulations, with the Noah-MP dynamic leaf model diverging significantly from satellite-derived vegetation fields in many ecoregions. Impacts on simulated hydrology were, however, found to be modest across climate zones, except for select extreme events. Also, although impacts on hydrology under ENSO-induced variability were small, we find that the Noah-MP dynamic leaf model simulates a positive relationship between rainfall and vegetation in humid ecoregions of WTSA, where satellite observations may indicate the opposite. The relatively small sensitivity of simulated hydrology to vegetation scheme suggests that the performance of hydrological monitoring and forecasting in WTSA that uses Noah-MP is largely unaffected by the choice of vegetation scheme, such that using a simple climatological default is generally no worse than adopting more complicated options. The presence of some differences between the time-varying and constant MODIS simulations for hydrologic extremes, however, indicates that time-varying MODIS configuration might be more suitable for hydrological hazards applications.
Abstract
Land surface models (LSMs) rely on vegetation parameters for use in hydrological and energy balance analysis, monitoring, and forecasting. This study examines the influence that vegetation representation in the Noah-Multiparameterization (Noah-MP) LSM has on hydrological simulations across the diverse climate zones of western tropical South America (WTSA), with specific consideration of hydrological variability associated with El Niño–Southern Oscillation (ENSO). The influence of model representation of vegetation on simulated hydrology is evaluated through three simulation experiments that use 1) satellite-derived constant MODIS; 2) satellite-derived time-varying MODIS; and 3) the Noah-MP dynamic leaf model. We find substantial differences in vegetation fields between these simulations, with the Noah-MP dynamic leaf model diverging significantly from satellite-derived vegetation fields in many ecoregions. Impacts on simulated hydrology were, however, found to be modest across climate zones, except for select extreme events. Also, although impacts on hydrology under ENSO-induced variability were small, we find that the Noah-MP dynamic leaf model simulates a positive relationship between rainfall and vegetation in humid ecoregions of WTSA, where satellite observations may indicate the opposite. The relatively small sensitivity of simulated hydrology to vegetation scheme suggests that the performance of hydrological monitoring and forecasting in WTSA that uses Noah-MP is largely unaffected by the choice of vegetation scheme, such that using a simple climatological default is generally no worse than adopting more complicated options. The presence of some differences between the time-varying and constant MODIS simulations for hydrologic extremes, however, indicates that time-varying MODIS configuration might be more suitable for hydrological hazards applications.
Abstract
Past work has shown that a land surface model’s (LSM) implicit (not explicitly coded) relationships between soil moisture and both evapotranspiration (ET) and runoff largely determine the LSM’s hydrological behavior. Here we estimate the relationships that appear to be operating in the real world and compare them to those of the LSM component of a state-of-the-art Earth system model (ESM). The two sets of relationships are determined by calibrating them within a simple water balance model (WBM): once using stream gauge observations from small, unregulated rivers over the eastern half of the United States, and once using the runoffs generated by the LSM as part of a state-of-the-art atmospheric reanalysis. Hydrological simulations and subseasonal hydrological forecasts performed with the two calibrated versions of the WBM provide two key results. First, the version calibrated to the LSM-generated runoffs does successfully reproduce, to first order, the hydrological behavior of the full LSM within its ESM environment. Second, of the two WBM versions, the one calibrated to the observations reproduces more accurately a broad collection of fully independent streamflow observations as well as a similarly broad collection of in situ soil moisture measurements. Taken together, the two results suggest that the observations-calibrated ET and runoff efficiency functions do successfully represent, at least to some degree, soil moisture controls over hydrological variability in nature and can serve as potentially useful targets for further LSM development.
Significance Statement
For all their complexity, and for all the work that underlies their development, the land surface model components of Earth system models may be suboptimal in fundamental yet unstudied ways. Here we estimate how the joint control of soil moisture over evapotranspiration and runoff processes in nature differs from that built implicitly into a state-of-the-art land model. Validation exercises demonstrate how this difference appears to lead to reduced accuracy in the land model’s simulation and forecasting of such hydrological variables as streamflow and soil moisture. Our results indicate that the relationships estimated for nature could serve as a potentially valuable target for further land model development.
Abstract
Past work has shown that a land surface model’s (LSM) implicit (not explicitly coded) relationships between soil moisture and both evapotranspiration (ET) and runoff largely determine the LSM’s hydrological behavior. Here we estimate the relationships that appear to be operating in the real world and compare them to those of the LSM component of a state-of-the-art Earth system model (ESM). The two sets of relationships are determined by calibrating them within a simple water balance model (WBM): once using stream gauge observations from small, unregulated rivers over the eastern half of the United States, and once using the runoffs generated by the LSM as part of a state-of-the-art atmospheric reanalysis. Hydrological simulations and subseasonal hydrological forecasts performed with the two calibrated versions of the WBM provide two key results. First, the version calibrated to the LSM-generated runoffs does successfully reproduce, to first order, the hydrological behavior of the full LSM within its ESM environment. Second, of the two WBM versions, the one calibrated to the observations reproduces more accurately a broad collection of fully independent streamflow observations as well as a similarly broad collection of in situ soil moisture measurements. Taken together, the two results suggest that the observations-calibrated ET and runoff efficiency functions do successfully represent, at least to some degree, soil moisture controls over hydrological variability in nature and can serve as potentially useful targets for further LSM development.
Significance Statement
For all their complexity, and for all the work that underlies their development, the land surface model components of Earth system models may be suboptimal in fundamental yet unstudied ways. Here we estimate how the joint control of soil moisture over evapotranspiration and runoff processes in nature differs from that built implicitly into a state-of-the-art land model. Validation exercises demonstrate how this difference appears to lead to reduced accuracy in the land model’s simulation and forecasting of such hydrological variables as streamflow and soil moisture. Our results indicate that the relationships estimated for nature could serve as a potentially valuable target for further land model development.
Abstract
We introduce two variants of canonical correlation analysis (CCA) for model output statistics of GCM forecasts of daily rainfall. These approaches link the coarse-gridded GCM forecasts with the reference field through a projection onto highly correlated basis vectors to address the recurrent errors in daily rainfall forecasts due to spatial bias and subgrid variability. The first model, namely, sparse CCA (SCCA), includes the sparsity feature into the ordinary CCA to provide a reduced number of canonical coefficients. The second model (B-SCCA) employs the bagging approach to reduce the variance in the predictions due to the sample variability in the derived canonical series. The models are tested using simulated data imposed with a strong spatial bias, and then using subseasonal rainfall forecasts provided by the NASA GMAO GEOS model under the SubX project, as well as gridded rainfall data (MSWEP product) for the region of South Korea. A linear regression model is chosen as the baseline postprocessing algorithm and ordinary CCA is also evaluated against the proposed models. As for the simulated data, the SCCA model confirms its ability to address spatial bias in forecast fields compared with the baseline model. For the actual forecasts, the leading improvements of SCCA and B-SCCA over the baseline model are for the S 1 skill score, suggesting that these models offer a relative gain in reproducing the spatial gradient of the reference rainfall field, which is relevant in hydrological applications that require a sound representation of spatial variability. Our results also highlight the importance of prefiltering the input data before applying CCA in such settings.
Abstract
We introduce two variants of canonical correlation analysis (CCA) for model output statistics of GCM forecasts of daily rainfall. These approaches link the coarse-gridded GCM forecasts with the reference field through a projection onto highly correlated basis vectors to address the recurrent errors in daily rainfall forecasts due to spatial bias and subgrid variability. The first model, namely, sparse CCA (SCCA), includes the sparsity feature into the ordinary CCA to provide a reduced number of canonical coefficients. The second model (B-SCCA) employs the bagging approach to reduce the variance in the predictions due to the sample variability in the derived canonical series. The models are tested using simulated data imposed with a strong spatial bias, and then using subseasonal rainfall forecasts provided by the NASA GMAO GEOS model under the SubX project, as well as gridded rainfall data (MSWEP product) for the region of South Korea. A linear regression model is chosen as the baseline postprocessing algorithm and ordinary CCA is also evaluated against the proposed models. As for the simulated data, the SCCA model confirms its ability to address spatial bias in forecast fields compared with the baseline model. For the actual forecasts, the leading improvements of SCCA and B-SCCA over the baseline model are for the S 1 skill score, suggesting that these models offer a relative gain in reproducing the spatial gradient of the reference rainfall field, which is relevant in hydrological applications that require a sound representation of spatial variability. Our results also highlight the importance of prefiltering the input data before applying CCA in such settings.
Abstract
Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region.
Significance Statement
The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.
Abstract
Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region.
Significance Statement
The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.
Abstract
Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. Our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.
Abstract
Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. Our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.
Abstract
Tropical cyclones (TCs) and their associated precipitation can have devastating impacts on the areas affected, with outcomes ranging from mudslides to inland flash flooding. Previous studies have used a fixed radius around the TC to isolate storm-related precipitation. One previous study instead used a dynamic radius of 8 m s−1 winds, but the wind field of the TC can deteriorate or shift quickly after landfall or the onset of extratropical transition (ET). This study uses a dynamical radius derived from the 500-hPa geopotential height in and around the TC to define TC- and post-tropical cyclone (PTC)-related heavy precipitation, allowing for the analysis of precipitation with tropical origins after the official demise of the original TC. Climatologies are constructed, indicating a maximum in TC- and PTC-related heavy precipitation in the west North Pacific and a secondary maximum in the east North Pacific. PTC-related heavy precipitation accounts for as much as 40% of the annual heavy precipitation in the northwest portion of the west North Pacific basin and 3.13% of heavy precipitation globally. We observe that the major hurricane stage contributes on average 2.6% of the global TC- and PTC-related precipitation, while the less intense but more common tropical storm stages of the TC life cycle contribute 85.7% of this observed precipitation. This analysis framework can be further extended to assess model biases and climate projections of TC and PTC precipitation.
Abstract
Tropical cyclones (TCs) and their associated precipitation can have devastating impacts on the areas affected, with outcomes ranging from mudslides to inland flash flooding. Previous studies have used a fixed radius around the TC to isolate storm-related precipitation. One previous study instead used a dynamic radius of 8 m s−1 winds, but the wind field of the TC can deteriorate or shift quickly after landfall or the onset of extratropical transition (ET). This study uses a dynamical radius derived from the 500-hPa geopotential height in and around the TC to define TC- and post-tropical cyclone (PTC)-related heavy precipitation, allowing for the analysis of precipitation with tropical origins after the official demise of the original TC. Climatologies are constructed, indicating a maximum in TC- and PTC-related heavy precipitation in the west North Pacific and a secondary maximum in the east North Pacific. PTC-related heavy precipitation accounts for as much as 40% of the annual heavy precipitation in the northwest portion of the west North Pacific basin and 3.13% of heavy precipitation globally. We observe that the major hurricane stage contributes on average 2.6% of the global TC- and PTC-related precipitation, while the less intense but more common tropical storm stages of the TC life cycle contribute 85.7% of this observed precipitation. This analysis framework can be further extended to assess model biases and climate projections of TC and PTC precipitation.
Abstract
This study reconstructed historical monthly rainfall data at 24 observation stations in Sumatera, Indonesia, for 22 years (1879–1900) during the Dutch colonial era. The literature research was conducted in libraries in Indonesia and the Netherlands to collect meteorological data recorded in a series of Regenwaarnemingen in Nederlandsch-Indië, which have never been reconstructed or analyzed previously. The results showed that the six western coast stations (97°–103°E, 3°N–4°S) have abundant rainfall exceeding 3000 mm yr−1, and the 10 eastern coast stations and three stations east of the Barisan ridge obtained rainfall below 3050 mm yr−1. Abundant coastal rainfall appeared only along the western coast, but not in the eastern coast and east of the Barisan ridge. The El Niño events of 1888/89 had low impact on rainfall amounts during the dry season (May–October) at the six stations of the Southern Hemispheric climate type. A significant decrease in annual dry-season precipitation was observed in 1881, 1885, and 1896. When comparing monthly data for 1931–60 and 1971–2014 at the northernmost station Banda Aceh (5.5°N, 95.3°E) and the eastern coast station Bengkalis (1.5°N, 102.1°E), a noticeable decrease and increase occurred in June–December and January–May, respectively, throughout the last century. The recent El Niño events have resulted in a decline in rainfall and dry conditions in Sumatera, but the Indian Ocean dipole had much influence on the amount of rainfall in the late-nineteenth century.
Abstract
This study reconstructed historical monthly rainfall data at 24 observation stations in Sumatera, Indonesia, for 22 years (1879–1900) during the Dutch colonial era. The literature research was conducted in libraries in Indonesia and the Netherlands to collect meteorological data recorded in a series of Regenwaarnemingen in Nederlandsch-Indië, which have never been reconstructed or analyzed previously. The results showed that the six western coast stations (97°–103°E, 3°N–4°S) have abundant rainfall exceeding 3000 mm yr−1, and the 10 eastern coast stations and three stations east of the Barisan ridge obtained rainfall below 3050 mm yr−1. Abundant coastal rainfall appeared only along the western coast, but not in the eastern coast and east of the Barisan ridge. The El Niño events of 1888/89 had low impact on rainfall amounts during the dry season (May–October) at the six stations of the Southern Hemispheric climate type. A significant decrease in annual dry-season precipitation was observed in 1881, 1885, and 1896. When comparing monthly data for 1931–60 and 1971–2014 at the northernmost station Banda Aceh (5.5°N, 95.3°E) and the eastern coast station Bengkalis (1.5°N, 102.1°E), a noticeable decrease and increase occurred in June–December and January–May, respectively, throughout the last century. The recent El Niño events have resulted in a decline in rainfall and dry conditions in Sumatera, but the Indian Ocean dipole had much influence on the amount of rainfall in the late-nineteenth century.
