Zdenko Heyvaert, S. Scherrer, M. Bechtold, A. Gruber, W. Dorigo, Sujay V. Kumar, G. De Lannoy
In this study, soil moisture retrievals of the combined active-passive ESA CCI soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-year study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. (1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. (2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parametrization if the observations are rescaled monthly. (3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.
{"title":"Impact of design factors for ESA CCI satellite soil moisture data assimilation over Europe","authors":"Zdenko Heyvaert, S. Scherrer, M. Bechtold, A. Gruber, W. Dorigo, Sujay V. Kumar, G. De Lannoy","doi":"10.1175/jhm-d-22-0141.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0141.1","url":null,"abstract":"\u0000In this study, soil moisture retrievals of the combined active-passive ESA CCI soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-year study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. (1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. (2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parametrization if the observations are rescaled monthly. (3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"1 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83130064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiaying Zhang, K. Guan, R. Fu, B. Peng, Siyu Zhao, Y. Zhuang
Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.
{"title":"Evaluating Seasonal Climate Forecasts from Dynamical Models over South America","authors":"Jiaying Zhang, K. Guan, R. Fu, B. Peng, Siyu Zhao, Y. Zhuang","doi":"10.1175/jhm-d-22-0156.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0156.1","url":null,"abstract":"\u0000Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management.\u0000\u0000\u0000Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.\u0000","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"540 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80226709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Tropical islands are simultaneously some of the most biodiverse and vulnerable places on Earth. Water resources help maintain the delicate balance on which the ecosystems and the population of tropical islands rely. Hydrogen and oxygen isotope analyses are a powerful tool in the study of the water cycle on tropical islands, although the scarcity of long-term and high-frequency data makes interpretation challenging. Here, a new dataset is presented based on weekly collection of rainfall H and O isotopic composition on the island of O‘ahu, Hawai‘i, beginning from July 2019 and still ongoing. The data show considerable differences in isotopic ratios produced by different weather systems, with Kona lows and upper-level lows having the lowest δ 2 H and δ 18 O values, and trade-wind showers the highest. The data also show significant spatial variability, with some sites being characterized by higher isotope ratios than others. The amount effect is not observed consistently at all sites. Deuterium excess shows a marked seasonal cycle, which is attributed to the different origin and history of the air masses that are responsible for rainfall in the winter and summer months. The local meteoric water line and a comparison of this dataset with a long-term historical record illustrate strong interannual variability and the need to establish a long-term precipitation isotope monitoring network for Hawai‘i. Significance Statement The isotopic composition of water is often used in the study of island water resources, but the scarcity of high-frequency datasets makes the interpretation of data difficult. The purpose of this study is to investigate the isotopic composition of rainfall on a mountainous island in the subtropics. Based on weekly data collection on O‘ahu, Hawai‘i, the results improve our understanding of the isotopic composition of rainfall due to different weather systems, like trade-wind showers or cold fronts, as well as its spatial and temporal variability. These results could inform the interpretation of data from other mountainous islands in similar climate zones.
{"title":"The Isotopic Composition of Rainfall on a Subtropical Mountainous Island","authors":"Giuseppe Torri, Alison D. Nugent, Brian N. Popp","doi":"10.1175/jhm-d-21-0204.1","DOIUrl":"https://doi.org/10.1175/jhm-d-21-0204.1","url":null,"abstract":"Abstract Tropical islands are simultaneously some of the most biodiverse and vulnerable places on Earth. Water resources help maintain the delicate balance on which the ecosystems and the population of tropical islands rely. Hydrogen and oxygen isotope analyses are a powerful tool in the study of the water cycle on tropical islands, although the scarcity of long-term and high-frequency data makes interpretation challenging. Here, a new dataset is presented based on weekly collection of rainfall H and O isotopic composition on the island of O‘ahu, Hawai‘i, beginning from July 2019 and still ongoing. The data show considerable differences in isotopic ratios produced by different weather systems, with Kona lows and upper-level lows having the lowest δ 2 H and δ 18 O values, and trade-wind showers the highest. The data also show significant spatial variability, with some sites being characterized by higher isotope ratios than others. The amount effect is not observed consistently at all sites. Deuterium excess shows a marked seasonal cycle, which is attributed to the different origin and history of the air masses that are responsible for rainfall in the winter and summer months. The local meteoric water line and a comparison of this dataset with a long-term historical record illustrate strong interannual variability and the need to establish a long-term precipitation isotope monitoring network for Hawai‘i. Significance Statement The isotopic composition of water is often used in the study of island water resources, but the scarcity of high-frequency datasets makes the interpretation of data difficult. The purpose of this study is to investigate the isotopic composition of rainfall on a mountainous island in the subtropics. Based on weekly data collection on O‘ahu, Hawai‘i, the results improve our understanding of the isotopic composition of rainfall due to different weather systems, like trade-wind showers or cold fronts, as well as its spatial and temporal variability. These results could inform the interpretation of data from other mountainous islands in similar climate zones.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136085277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Turkana Jet in northern Kenya is shown to modulate the climate of southwest Ethiopia’s Omo River Valley using in situ hydrometeorological data, satellite measurements, and atmospheric reanalyses from decadal to diurnal time scales. Temporal statistics from lowland (2.5°–5°N, 35°–38°E) and highland (6°–9°N, 35°–38°E) areas show that 850-hPa westward airflow over Lake Turkana is stronger in March and October but is weakened when western Indian Ocean sea temperatures become warmer than usual at intervals of 2–7 years. A case study on 24 March 2019 reveals how a stronger Turkana Jet induces warming and drying of the Omo Valley. A second case study on 27 September 2018 reveals Hadley cell subsidence over the southern flank of the Turkana Jet. We demonstrate how nocturnal airflow draining off the mountains joins the channelized jet. Satellite and atmospheric reanalyses exhibit realistic diurnal cycles in the east Omo mountains, but some products have incorrect phase and warm bias. Omo Valley soil moisture and runoff exhibit little trend in historical records and model projections; however, unpredictable multiyear wet and dry spells and a growing demand for water are ongoing concerns.
{"title":"Turkana Low-Level Jet Influence on Southwest Ethiopia Climate","authors":"M. Jury, T. T. Minda","doi":"10.1175/jhm-d-22-0134.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0134.1","url":null,"abstract":"\u0000The Turkana Jet in northern Kenya is shown to modulate the climate of southwest Ethiopia’s Omo River Valley using in situ hydrometeorological data, satellite measurements, and atmospheric reanalyses from decadal to diurnal time scales. Temporal statistics from lowland (2.5°–5°N, 35°–38°E) and highland (6°–9°N, 35°–38°E) areas show that 850-hPa westward airflow over Lake Turkana is stronger in March and October but is weakened when western Indian Ocean sea temperatures become warmer than usual at intervals of 2–7 years. A case study on 24 March 2019 reveals how a stronger Turkana Jet induces warming and drying of the Omo Valley. A second case study on 27 September 2018 reveals Hadley cell subsidence over the southern flank of the Turkana Jet. We demonstrate how nocturnal airflow draining off the mountains joins the channelized jet. Satellite and atmospheric reanalyses exhibit realistic diurnal cycles in the east Omo mountains, but some products have incorrect phase and warm bias. Omo Valley soil moisture and runoff exhibit little trend in historical records and model projections; however, unpredictable multiyear wet and dry spells and a growing demand for water are ongoing concerns.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"112 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90521714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Zhao, D. Hudak, P. Rodriguez, E. Mekis, Dominique Brunet, Ellen Eckert, S. Melo
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-year period versus nineteen high quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to timescale, meteorological season, PMW source, QI, and land surface type. Results indicate that: (1) the cold season’s ( Nov - Apr ) larger relative bias can be mitigated via backward morphing; (2) IMERG 6-hour precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index - 1 (FBI-1); (3) the performance of five PMW is affected by the season to different degrees; (4) in terms of some metrics, skills do not always enhance with increasing QI; (5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products.
{"title":"Assessment of IMERG v06 satellite precipitation products in the Canadian Great Lakes region","authors":"B. Zhao, D. Hudak, P. Rodriguez, E. Mekis, Dominique Brunet, Ellen Eckert, S. Melo","doi":"10.1175/jhm-d-22-0214.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0214.1","url":null,"abstract":"\u0000The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-year period versus nineteen high quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to timescale, meteorological season, PMW source, QI, and land surface type. Results indicate that: (1) the cold season’s ( Nov - Apr ) larger relative bias can be mitigated via backward morphing; (2) IMERG 6-hour precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index - 1 (FBI-1); (3) the performance of five PMW is affected by the season to different degrees; (4) in terms of some metrics, skills do not always enhance with increasing QI; (5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"315 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82904616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Which processes control the mean amounts of precipitation received by tropical land and ocean? Do large-scale constraints exist on the ratio between the two? We address these questions using a conceptual box model based on water balance equations. With empirical but physically motivated parametrizations of the water balance components, we construct a set of coupled differential equations which describe the dynamical behavior of the water vapor content over land and ocean as well as the land’s soil moisture content. For a closed model configuration with one ocean and one land box, we compute equilibrium solutions across the parameter space and analyze their sensitivity to parameter choices. The precipitation ratio χ, defined as the ratio between mean land and ocean precipitation rates, quantifies the land-sea precipitation contrast. We find that χ is bounded between zero and one as long as the presence of land does not affect the relationship between water vapor path and precipitation. However, for the tested parameter values, 95% of the obtained χ values are even larger than 0.75. The sensitivity analysis reveals that χ is primarily controlled by the efficiency of atmospheric moisture transport rather than by land surface parameters. We further investigate under which conditions precipitation enhancement over land (χ > 1) would be possible. An open model configuration with an island between two ocean boxes and nonzero external advection into the domain can yield χ values larger than one, but only for a small subset of parameter choices, characterized by small land fractions and a sufficiently large moisture influx through the windward boundary.
{"title":"Constraints on the Ratio between Tropical Land and Ocean Precipitation Derived from a Conceptual Water Balance Model","authors":"Luca Schmidt, C. Hohenegger","doi":"10.1175/jhm-d-22-0162.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0162.1","url":null,"abstract":"\u0000Which processes control the mean amounts of precipitation received by tropical land and ocean? Do large-scale constraints exist on the ratio between the two? We address these questions using a conceptual box model based on water balance equations. With empirical but physically motivated parametrizations of the water balance components, we construct a set of coupled differential equations which describe the dynamical behavior of the water vapor content over land and ocean as well as the land’s soil moisture content. For a closed model configuration with one ocean and one land box, we compute equilibrium solutions across the parameter space and analyze their sensitivity to parameter choices. The precipitation ratio χ, defined as the ratio between mean land and ocean precipitation rates, quantifies the land-sea precipitation contrast. We find that χ is bounded between zero and one as long as the presence of land does not affect the relationship between water vapor path and precipitation. However, for the tested parameter values, 95% of the obtained χ values are even larger than 0.75. The sensitivity analysis reveals that χ is primarily controlled by the efficiency of atmospheric moisture transport rather than by land surface parameters. We further investigate under which conditions precipitation enhancement over land (χ > 1) would be possible. An open model configuration with an island between two ocean boxes and nonzero external advection into the domain can yield χ values larger than one, but only for a small subset of parameter choices, characterized by small land fractions and a sufficiently large moisture influx through the windward boundary.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"15 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78875097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A new set of CMIP6 data downscaled using the Localized Constructed Analogs (LOCA) statistical method has been produced, covering central Mexico through Southern Canada at 6 km resolution. Output from 27 CMIP6 Earth System Models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training data set that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over Southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (~25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (~15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. 1-in-100 yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30-40 years in the southeastern U.S. and Pacific Northwest by end of century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to LOCA-downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.
{"title":"Future Increases in North American Extreme Precipitation in CMIP6 downscaled with LOCA","authors":"D. Pierce, D. Cayan, D. Feldman, M. Risser","doi":"10.1175/jhm-d-22-0194.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0194.1","url":null,"abstract":"\u0000A new set of CMIP6 data downscaled using the Localized Constructed Analogs (LOCA) statistical method has been produced, covering central Mexico through Southern Canada at 6 km resolution. Output from 27 CMIP6 Earth System Models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training data set that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over Southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (~25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (~15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. 1-in-100 yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30-40 years in the southeastern U.S. and Pacific Northwest by end of century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to LOCA-downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"16 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88775620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
IMERG provides the state-of-the-art satellite-based precipitation estimates that combine observations from multiple satellite platforms. This study evaluates IMERG products by examining hydrologic simulations of streamflow at a range of spatial scales. The main objective of this study is to assess the predictive utility of the near real-time product (IMERG-Early). The assessment also includes the IMERG-Final product that is not available in real time. The authors used MRMS precipitation estimates and USGS streamflow observation data as references for the precipitation and streamflow evaluations during a five-year period (2016–2020). The precipitation evaluation results show that IMERG-Early yields significant overestimations, particularly during warm months, with higher variability in its conditional distributions, whereas the performance of IMERG-Final seems unbiased. The authors performed hydrologic simulations using the Iowa Flood Center’s Hillslope Link Model with three precipitation forcing products i.e., MRMS, IMERG-Early, and IMERG Final. The simulation results reveal that IMERG-Early leads to high hit and false alarm rates due to its overestimation in precipitation and has almost no skill, as measured by the overall performance metric KGE, in streamflow prediction regarding basin scales ranging from 10 to 30,000 km2. This indicates that the product requires a bias correction before it is useful for real-time flood prediction. The streamflow prediction performance of IMERG-Final seems comparable to that of MRMS at spatial scales greater than 100 km2. This scale limitation is attributable to IMERG’s product spatial resolution that is inadequate to capture the small-scale variability of precipitation.
{"title":"Hydrologic Assessment of IMERG Products Across Spatial Scales over Iowa","authors":"B. Seo, F. Quintero, W. Krajewski","doi":"10.1175/jhm-d-22-0129.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0129.1","url":null,"abstract":"\u0000IMERG provides the state-of-the-art satellite-based precipitation estimates that combine observations from multiple satellite platforms. This study evaluates IMERG products by examining hydrologic simulations of streamflow at a range of spatial scales. The main objective of this study is to assess the predictive utility of the near real-time product (IMERG-Early). The assessment also includes the IMERG-Final product that is not available in real time. The authors used MRMS precipitation estimates and USGS streamflow observation data as references for the precipitation and streamflow evaluations during a five-year period (2016–2020). The precipitation evaluation results show that IMERG-Early yields significant overestimations, particularly during warm months, with higher variability in its conditional distributions, whereas the performance of IMERG-Final seems unbiased. The authors performed hydrologic simulations using the Iowa Flood Center’s Hillslope Link Model with three precipitation forcing products i.e., MRMS, IMERG-Early, and IMERG Final. The simulation results reveal that IMERG-Early leads to high hit and false alarm rates due to its overestimation in precipitation and has almost no skill, as measured by the overall performance metric KGE, in streamflow prediction regarding basin scales ranging from 10 to 30,000 km2. This indicates that the product requires a bias correction before it is useful for real-time flood prediction. The streamflow prediction performance of IMERG-Final seems comparable to that of MRMS at spatial scales greater than 100 km2. This scale limitation is attributable to IMERG’s product spatial resolution that is inadequate to capture the small-scale variability of precipitation.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"21 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91273706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Qin, Sien Li, Kun Yang, Lu Zhang, Lei Cheng, Pan Liu, D. She
In partial plastic mulch-covered croplands, the complicated co-existence of bare soil surface, mulched soil surface, and dynamically changing canopy surface results in challenges in accurately estimating field surface albedo (α) and its components (bare soil surface albedo, αb; mulched soil surface albedo, αm; and canopy surface albedo, αc) during the whole growth period. To accurately estimate α, αb, αm, and αc, and to quantify the three surfaces’ contributions to field shortwave radiation reflections (Fb, Fm, Fc), (1) a modified two-stream (MTS) approximation solution that considered the effect of plastic mulch has been proposed to accurately estimate α; (2) dynamic variations of αb, αm, and αc, and Fb, Fm, Fc have been characterized. Therein, αb and αm were determined from corresponding parameterization schemes, αc is determined using mulched irrigated croplands surface albedo (MICA) relationship between α and αb, αm, and αc that established in this study. Results indicated that: (1) compared with measurements, considering the effect of plastic mulch will significantly improve estimation of α when ground surface is not fully covered by crop canopy, while not will underestimate α by a mean value of 0.061 in the early growth period; (2) mean values of α, αb, αm, and αc during the whole growth period were 0.198, 0.174, 0.308, and 0.160, respectively, while the corresponding Fb, Fm, and Fc were 0.08, 0.42, and 0.50, respectively.
{"title":"A method for estimating surface albedo and its components for partial plastic mulched croplands","authors":"S. Qin, Sien Li, Kun Yang, Lu Zhang, Lei Cheng, Pan Liu, D. She","doi":"10.1175/jhm-d-22-0088.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0088.1","url":null,"abstract":"\u0000In partial plastic mulch-covered croplands, the complicated co-existence of bare soil surface, mulched soil surface, and dynamically changing canopy surface results in challenges in accurately estimating field surface albedo (α) and its components (bare soil surface albedo, αb; mulched soil surface albedo, αm; and canopy surface albedo, αc) during the whole growth period. To accurately estimate α, αb, αm, and αc, and to quantify the three surfaces’ contributions to field shortwave radiation reflections (Fb, Fm, Fc), (1) a modified two-stream (MTS) approximation solution that considered the effect of plastic mulch has been proposed to accurately estimate α; (2) dynamic variations of αb, αm, and αc, and Fb, Fm, Fc have been characterized. Therein, αb and αm were determined from corresponding parameterization schemes, αc is determined using mulched irrigated croplands surface albedo (MICA) relationship between α and αb, αm, and αc that established in this study. Results indicated that: (1) compared with measurements, considering the effect of plastic mulch will significantly improve estimation of α when ground surface is not fully covered by crop canopy, while not will underestimate α by a mean value of 0.061 in the early growth period; (2) mean values of α, αb, αm, and αc during the whole growth period were 0.198, 0.174, 0.308, and 0.160, respectively, while the corresponding Fb, Fm, and Fc were 0.08, 0.42, and 0.50, respectively.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"20 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86795180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Habiba Kallel, A. Thiboult, M. Mackay, D. Nadeau, F. Anctil
Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multi-year direct observations of turbulent heat fluxes collected on and around the 85-km2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model which cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error= −0.3 W m−2 and mean error= −2.7 W m−2 for latent and sensible heat fluxes). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020.
考虑到内陆水体与周围陆地的显著差异，在气象和气候模式中准确模拟内陆水体与大气之间的相互作用至关重要。地表热通量的建模仍然是一个挑战，因为可用于验证的直接观测很少，特别是在高纬度地区。本文对加拿大小湖模型(CSLM)进行了详细的评价，该模型是一种一维混合层动态湖泊模型，用于再现加拿大东部亚北极储层的地表能量收支和热分层。该分析得到了在85平方公里的Romaine-2水电站水库(50.7°N, 63.2°W)上和周围收集的湍流热通量的多年直接观测的支持，这两个通量塔:一个在岸上全年运行，另一个在无冰条件下在木筏上运行。CSLM模拟了水体的热状态，包括冰的形成和雪的物理，在离线模式下运行，并在2018年6月25日至2021年6月8日期间受到当地天气观测的影响。尽管CSLM模型的一维性质不能解释与水库运行相关的能量输入和输出，但观测值和模拟值的比较证实了CSLM可以合理地再现水库的湍流热通量和温度行为。潜热通量和感热通量的平均误差为- 0.3 W m−2，平均误差为- 2.7 W m−2，在融冰后的头几个月达到最佳效果。该模型对强风事件反应过度，导致随后对水温的估计不准确，最终导致提前冻结。该模型在2019年和2020年将经校正的年蒸汽量分别高估了5%和16%。
{"title":"Modeling heat and water exchanges between the atmosphere and an 85-km2 dimictic subarctic reservoir using the 1D Canadian Small Lake Model","authors":"Habiba Kallel, A. Thiboult, M. Mackay, D. Nadeau, F. Anctil","doi":"10.1175/jhm-d-22-0132.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0132.1","url":null,"abstract":"\u0000Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multi-year direct observations of turbulent heat fluxes collected on and around the 85-km2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model which cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error= −0.3 W m−2 and mean error= −2.7 W m−2 for latent and sensible heat fluxes). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"1 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75218885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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