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Abstract
This study examines the primary atmospheric controls over winter precipitation variability in the Great Lakes basin and the potential for seasonal prediction. We employ partial least squares (PLS) regression to identify the primary modes of joint variability between winter precipitation over each of the Great Lakes and concurrent anomalies in midlevel atmospheric flow. We find that the first identified pattern (PLS1) is related to El Niño–Southern Oscillation (ENSO), while the other patterns represent unique anomalies in atmospheric flow that govern precipitation gradients over the basin, with limited seasonal predictability. Nonlinearities are found in the relationship between a sea surface temperature (SST)-based index for ENSO and PLS1 with respect to the phase, strength, and type of ENSO event. An examination of the ENSO-related propagating wave train that drives variability of PLS1 precipitation reveals that seasonally lagged tropical Pacific convection, as measured by remotely sensed outgoing longwave radiation (OLR), is more strongly and linearly related to Great Lakes winter precipitation than SST-based ENSO indices. Cross-validated linear regressions based on October OLR signals explain 20%–32% of the out-of-sample precipitation variability in the Great Lakes basin. We conclude with a deeper assessment of the underlying relationship between patterns of OLR anomalies in the western equatorial Pacific and Great Lakes winter precipitation. Results show that precipitation response to El Niño is similar regardless of OLR intensity in the tropical Pacific, but for La Niña events, the precipitation response is stronger under weak tropical OLR anomalies. The potential for further improvements in ENSO-based seasonal forecasts are discussed.
Abstract
This study examines the primary atmospheric controls over winter precipitation variability in the Great Lakes basin and the potential for seasonal prediction. We employ partial least squares (PLS) regression to identify the primary modes of joint variability between winter precipitation over each of the Great Lakes and concurrent anomalies in midlevel atmospheric flow. We find that the first identified pattern (PLS1) is related to El Niño–Southern Oscillation (ENSO), while the other patterns represent unique anomalies in atmospheric flow that govern precipitation gradients over the basin, with limited seasonal predictability. Nonlinearities are found in the relationship between a sea surface temperature (SST)-based index for ENSO and PLS1 with respect to the phase, strength, and type of ENSO event. An examination of the ENSO-related propagating wave train that drives variability of PLS1 precipitation reveals that seasonally lagged tropical Pacific convection, as measured by remotely sensed outgoing longwave radiation (OLR), is more strongly and linearly related to Great Lakes winter precipitation than SST-based ENSO indices. Cross-validated linear regressions based on October OLR signals explain 20%–32% of the out-of-sample precipitation variability in the Great Lakes basin. We conclude with a deeper assessment of the underlying relationship between patterns of OLR anomalies in the western equatorial Pacific and Great Lakes winter precipitation. Results show that precipitation response to El Niño is similar regardless of OLR intensity in the tropical Pacific, but for La Niña events, the precipitation response is stronger under weak tropical OLR anomalies. The potential for further improvements in ENSO-based seasonal forecasts are discussed.
Abstract
Tropical moisture exports (TMEs) may play an important role in extreme precipitation. An analysis of the spatiotemporal structure of precipitation associated with TMEs for the eastern United States at seasonal and daily time scales is presented. TME-based precipitation is characterized based on the change in specific humidity along TME tracks delineated in a Lagrangian analysis of the ERA-Interim dataset. The empirical orthogonal functions (EOFs) of seasonal TME-based precipitation are analyzed separately for each season to identify the dominant modes of interannual variability. Loading patterns for the first EOF show a distinct seasonal cycle in the core region of TME-based precipitation across the eastern United States, while the second EOF describes a northwest–southeast oscillation in the center of TME-induced precipitation occurrence. The EOFs for TMEs are compared against EOFs of gauged flood count data, which exhibit similar spatial structures. Correlations between TME EOFs, geopotential heights, and sea surface temperatures suggest a strong connection between TME-based precipitation, the Pacific–North American (PNA) pattern, Pacific decadal oscillation (PDO), and the Intra-Americas Sea patterns for much of the calendar year. Daily TME-based and total precipitation is projected onto the leading seasonal EOFs to examine the characteristics of upper-quantile daily events. The daily analysis suggests that the PNA can potentially provide information regarding heavy TME-based precipitation at a lead time of 1–10 days or more in most seasons and total precipitation in the winter. The potential for subseasonal, seasonal, and decadal forecasts or conditional simulations of precipitation in the study region is discussed.
Abstract
Tropical moisture exports (TMEs) may play an important role in extreme precipitation. An analysis of the spatiotemporal structure of precipitation associated with TMEs for the eastern United States at seasonal and daily time scales is presented. TME-based precipitation is characterized based on the change in specific humidity along TME tracks delineated in a Lagrangian analysis of the ERA-Interim dataset. The empirical orthogonal functions (EOFs) of seasonal TME-based precipitation are analyzed separately for each season to identify the dominant modes of interannual variability. Loading patterns for the first EOF show a distinct seasonal cycle in the core region of TME-based precipitation across the eastern United States, while the second EOF describes a northwest–southeast oscillation in the center of TME-induced precipitation occurrence. The EOFs for TMEs are compared against EOFs of gauged flood count data, which exhibit similar spatial structures. Correlations between TME EOFs, geopotential heights, and sea surface temperatures suggest a strong connection between TME-based precipitation, the Pacific–North American (PNA) pattern, Pacific decadal oscillation (PDO), and the Intra-Americas Sea patterns for much of the calendar year. Daily TME-based and total precipitation is projected onto the leading seasonal EOFs to examine the characteristics of upper-quantile daily events. The daily analysis suggests that the PNA can potentially provide information regarding heavy TME-based precipitation at a lead time of 1–10 days or more in most seasons and total precipitation in the winter. The potential for subseasonal, seasonal, and decadal forecasts or conditional simulations of precipitation in the study region is discussed.
Abstract
This study examines the joint spatiotemporal variability of summertime climate linked to renewable energy sources (precipitation and streamflow, wind speeds, and insolation) and energy demand drivers (temperature, relative humidity, and a heat index) across the contiguous United States (CONUS) between 1948 and 2015. Canonical correlation analysis is used to identify the primary modes of joint variability between wind speeds and precipitation and related patterns of the other hydrometeorological variables. The first two modes exhibit a pan-U.S. dipole with lobes in the eastern and central CONUS. Composite analysis shows that these modes are directly related to the displacement of the western ridge of the North Atlantic subtropical high (NASH), suggesting that a single, large-scale feature of atmospheric circulation drives much of the large-scale climate covariability related to summertime renewable energy supply and demand across the CONUS. The impacts of this climate feature on the U.S. energy system are shown more directly by examining changes in surface climate variables at existing and potential sites of renewable energy infrastructure and locations of high energy demand. Also, different phases of the NASH are related to concurrent and lagged modes of oceanic and atmospheric climate variability in the Pacific and Atlantic Ocean basins, with results suggesting that springtime climate over both oceans may provide some potential to predict summer variability in the NASH and its associated surface climate. The implications of these findings for the impacts of climate variability and change on integrated renewable energy systems over the CONUS are discussed.
Abstract
This study examines the joint spatiotemporal variability of summertime climate linked to renewable energy sources (precipitation and streamflow, wind speeds, and insolation) and energy demand drivers (temperature, relative humidity, and a heat index) across the contiguous United States (CONUS) between 1948 and 2015. Canonical correlation analysis is used to identify the primary modes of joint variability between wind speeds and precipitation and related patterns of the other hydrometeorological variables. The first two modes exhibit a pan-U.S. dipole with lobes in the eastern and central CONUS. Composite analysis shows that these modes are directly related to the displacement of the western ridge of the North Atlantic subtropical high (NASH), suggesting that a single, large-scale feature of atmospheric circulation drives much of the large-scale climate covariability related to summertime renewable energy supply and demand across the CONUS. The impacts of this climate feature on the U.S. energy system are shown more directly by examining changes in surface climate variables at existing and potential sites of renewable energy infrastructure and locations of high energy demand. Also, different phases of the NASH are related to concurrent and lagged modes of oceanic and atmospheric climate variability in the Pacific and Atlantic Ocean basins, with results suggesting that springtime climate over both oceans may provide some potential to predict summer variability in the NASH and its associated surface climate. The implications of these findings for the impacts of climate variability and change on integrated renewable energy systems over the CONUS are discussed.
Abstract
This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using nonparametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA.
Abstract
This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using nonparametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA.
Abstract
This study examines space–time patterns of summer daily rainfall variability across the Northeast United States, with a focus on historical trends and the potential for long-lead predictability. A hidden Markov model based on daily data is used to define six weather states that represent distinct patterns of rainfall across the region, and composites are used to examine atmospheric circulation during each state. The states represent the occurrence of region-wide dry and wet conditions associated with a large-scale ridge and trough over the Northeast, respectively, as well as inland and coastal storm tracks. There is a positive trend in the frequency of the weather state associated with heavy, regionwide rainfall, which is mirrored by a decreasing trend in the frequency of stationary ridges and regionwide dry conditions. The frequency of state occurrences is also examined for historical Northeast droughts. Two primary drought types emerge that are characterized by region-wide dry conditions linked to a persistent ridge and an eastward-shifted storm track associated with light precipitation along the coastline. Finally, composites of May sea surface temperature anomalies (SSTAs) prior to summers with high and low frequencies of each weather state are used to assess long-lead predictability. These composites are compared against similar composites based on regional anomalies in low streamflow conditions [June–August 7-day low flows (SDLFs)]. Results indicate that springtime SSTs, particularly those in the Caribbean Sea and tropical North Atlantic Ocean, provide some predictability for summers with above-average precipitation and SDLFs, but SSTs provide little information on the occurrence of drought conditions across the Northeast.
Abstract
This study examines space–time patterns of summer daily rainfall variability across the Northeast United States, with a focus on historical trends and the potential for long-lead predictability. A hidden Markov model based on daily data is used to define six weather states that represent distinct patterns of rainfall across the region, and composites are used to examine atmospheric circulation during each state. The states represent the occurrence of region-wide dry and wet conditions associated with a large-scale ridge and trough over the Northeast, respectively, as well as inland and coastal storm tracks. There is a positive trend in the frequency of the weather state associated with heavy, regionwide rainfall, which is mirrored by a decreasing trend in the frequency of stationary ridges and regionwide dry conditions. The frequency of state occurrences is also examined for historical Northeast droughts. Two primary drought types emerge that are characterized by region-wide dry conditions linked to a persistent ridge and an eastward-shifted storm track associated with light precipitation along the coastline. Finally, composites of May sea surface temperature anomalies (SSTAs) prior to summers with high and low frequencies of each weather state are used to assess long-lead predictability. These composites are compared against similar composites based on regional anomalies in low streamflow conditions [June–August 7-day low flows (SDLFs)]. Results indicate that springtime SSTs, particularly those in the Caribbean Sea and tropical North Atlantic Ocean, provide some predictability for summers with above-average precipitation and SDLFs, but SSTs provide little information on the occurrence of drought conditions across the Northeast.
Abstract
We investigate the predictability of East African short rains at long (up to 12 month) lead times by relating seasonal rainfall anomalies to climate anomalies associated with the predominant Walker circulation, including sea surface temperatures (SST), geopotential heights, zonal and meridional winds, and vertical velocities. The underlying teleconnections are examined using a regularized regression model that shows two periods of high model skill (0–3-month lead and 7–9-month lead) with similar spatial patterns of predictability. We observe large-scale circulation anomalies consistent with the Walker circulation at short lead times (0–3 months) and dipoles of SST and height anomalies over the Mascarene high region at longer lead times (7–9 months). These two patterns are linked in time by anticyclonic winds in the dipole region associated with a perturbed meridional circulation (4–6-month lead). Overall, these results suggest that there is potential to extend forecast lead times beyond a few months for drought impact mitigation applications.
Abstract
We investigate the predictability of East African short rains at long (up to 12 month) lead times by relating seasonal rainfall anomalies to climate anomalies associated with the predominant Walker circulation, including sea surface temperatures (SST), geopotential heights, zonal and meridional winds, and vertical velocities. The underlying teleconnections are examined using a regularized regression model that shows two periods of high model skill (0–3-month lead and 7–9-month lead) with similar spatial patterns of predictability. We observe large-scale circulation anomalies consistent with the Walker circulation at short lead times (0–3 months) and dipoles of SST and height anomalies over the Mascarene high region at longer lead times (7–9 months). These two patterns are linked in time by anticyclonic winds in the dipole region associated with a perturbed meridional circulation (4–6-month lead). Overall, these results suggest that there is potential to extend forecast lead times beyond a few months for drought impact mitigation applications.
Abstract
Forecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic-scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500-hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic-scale features, especially at long (5–15 days) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific–North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations.
Abstract
Forecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic-scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500-hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic-scale features, especially at long (5–15 days) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific–North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations.
Abstract
This study investigates how extreme precipitation scales with dewpoint temperature across the northeastern United States, both in the observational record (1948–2020) and in a set of downscaled climate projections in the state of Massachusetts (2006–99). Spatiotemporal relationships between dewpoint temperature and extreme precipitation are assessed, and extreme precipitation–temperature scaling rates are evaluated on annual and seasonal scales using nonstationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% °C−1, but this varies by season, with most nonzero scaling rates in summer and fall and the largest rates (∼7.3% °C−1) in the summer. Dewpoint temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between −2.5% and 6.2% °C−1 at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dewpoint temperature over the twenty-first century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% °C−1). Overall, the observations suggest that extreme daily precipitation in the Northeast only thermodynamic scales with dewpoint temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.
Significance Statement
A warmer climate will likely result in the intensification of extreme precipitation, with the potential to enhance flood and stormwater risk. However, the relationship between extreme precipitation and temperature (i.e., the precipitation–temperature scaling rate) remains uncertain, particularly at regional scales, inhibiting societal adaptation to extreme events. Using observations and climate projections of daily precipitation and dewpoint temperature across the northeastern United States, we demonstrate that extreme daily precipitation does indeed scale with dewpoint temperature, but the rate of scaling varies by season, with the strongest relationship in the warm season.
Abstract
This study investigates how extreme precipitation scales with dewpoint temperature across the northeastern United States, both in the observational record (1948–2020) and in a set of downscaled climate projections in the state of Massachusetts (2006–99). Spatiotemporal relationships between dewpoint temperature and extreme precipitation are assessed, and extreme precipitation–temperature scaling rates are evaluated on annual and seasonal scales using nonstationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% °C−1, but this varies by season, with most nonzero scaling rates in summer and fall and the largest rates (∼7.3% °C−1) in the summer. Dewpoint temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between −2.5% and 6.2% °C−1 at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dewpoint temperature over the twenty-first century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% °C−1). Overall, the observations suggest that extreme daily precipitation in the Northeast only thermodynamic scales with dewpoint temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.
Significance Statement
A warmer climate will likely result in the intensification of extreme precipitation, with the potential to enhance flood and stormwater risk. However, the relationship between extreme precipitation and temperature (i.e., the precipitation–temperature scaling rate) remains uncertain, particularly at regional scales, inhibiting societal adaptation to extreme events. Using observations and climate projections of daily precipitation and dewpoint temperature across the northeastern United States, we demonstrate that extreme daily precipitation does indeed scale with dewpoint temperature, but the rate of scaling varies by season, with the strongest relationship in the warm season.
Abstract
The literature has established dozens of potential predictive indices (PIs) of anomalous warm season precipitation in the Midwestern United States that could have utility in subseasonal to seasonal (S2S) forecasts. This analysis posits that these predictive indices relate to one of three “modes of action” that work in tandem to drive anomalous hydroclimatic circulation into the continental interior. These include contributions from 1) geostrophic mass flux, 2) ageostrophic mass flux, and 3) atmospheric moisture supply, and represent semi-independent, interactive forcings on S2S precipitation variability. This study aggregates 24 PIs from the literature that are related to the three modes of action. Using an interpretable machine learning algorithm that accounts for nonlinear and interactive responses in a noisy predictive space, we evaluate the relative importance of PIs in predicting S2S precipitation anomalies from March to September. Physical mechanisms driving PI skill are confirmed using composite analysis of atmospheric fields related to the three modes of action. In general, PIs associated with ageostrophic mass flux anomalies are important in early summer, while PIs associated with Atlantic-sourced atmospheric moisture supply are important in late summer. At a 2-month lead, PIs associated with continental-scale thermodynamic processes are more important relative to PIs associated with local convective phenomena. PIs representing geostrophic mass flux anomalies are also critical throughout the warm season, in real time and at a 1–2-month lag, but particularly during transitional months (spring/fall). Several new PIs describing zonal and meridional asymmetry in hemispherical thermal gradients emerge as highly important, with implications for both S2S forecasting and climate change.
Abstract
The literature has established dozens of potential predictive indices (PIs) of anomalous warm season precipitation in the Midwestern United States that could have utility in subseasonal to seasonal (S2S) forecasts. This analysis posits that these predictive indices relate to one of three “modes of action” that work in tandem to drive anomalous hydroclimatic circulation into the continental interior. These include contributions from 1) geostrophic mass flux, 2) ageostrophic mass flux, and 3) atmospheric moisture supply, and represent semi-independent, interactive forcings on S2S precipitation variability. This study aggregates 24 PIs from the literature that are related to the three modes of action. Using an interpretable machine learning algorithm that accounts for nonlinear and interactive responses in a noisy predictive space, we evaluate the relative importance of PIs in predicting S2S precipitation anomalies from March to September. Physical mechanisms driving PI skill are confirmed using composite analysis of atmospheric fields related to the three modes of action. In general, PIs associated with ageostrophic mass flux anomalies are important in early summer, while PIs associated with Atlantic-sourced atmospheric moisture supply are important in late summer. At a 2-month lead, PIs associated with continental-scale thermodynamic processes are more important relative to PIs associated with local convective phenomena. PIs representing geostrophic mass flux anomalies are also critical throughout the warm season, in real time and at a 1–2-month lag, but particularly during transitional months (spring/fall). Several new PIs describing zonal and meridional asymmetry in hemispherical thermal gradients emerge as highly important, with implications for both S2S forecasting and climate change.
Abstract
Accurate gridded estimates of evapotranspiration (ET) are essential to the analysis of terrestrial water budgets. In this study, ET estimates from three gridded energy balance–based products (ETEB) with independent model formations and data forcings are evaluated for their ability to capture long-term climatology and interannual variability in ET derived from a terrestrial water budget (ETWB) for 671 gauged basins across the contiguous United States. All three ETEB products have low spatial bias and accurately capture interannual variability of ETWB in the central United States, where ETEB and ancillary estimates of change in total surface water storage (ΔTWS) from the GRACE satellite project appear to close terrestrial water budgets. In humid regions, ETEB products exhibit higher long-term bias, and the covariability of ETEB and ETWB decreases significantly. Several factors related to either failure of ETWB, such as errors in ΔTWS and precipitation, or failure of ETEB, such as treatment of snowfall and horizontal heat advection, explain some of these discrepancies. These results mirror and build on conclusions from other studies: on interannual time scales, ΔTWS and error in precipitation estimates are nonnegligible uncertainties in ET estimates based on a terrestrial water budget, and this confounds their comparison to energy balance ET models. However, there is also evidence that in at least some regions, climate and landscape features may also influence the accuracy and long-term bias of ET estimates from energy balance models, and these potential errors should be considered when using these gridded products in hydrologic applications.
Abstract
Accurate gridded estimates of evapotranspiration (ET) are essential to the analysis of terrestrial water budgets. In this study, ET estimates from three gridded energy balance–based products (ETEB) with independent model formations and data forcings are evaluated for their ability to capture long-term climatology and interannual variability in ET derived from a terrestrial water budget (ETWB) for 671 gauged basins across the contiguous United States. All three ETEB products have low spatial bias and accurately capture interannual variability of ETWB in the central United States, where ETEB and ancillary estimates of change in total surface water storage (ΔTWS) from the GRACE satellite project appear to close terrestrial water budgets. In humid regions, ETEB products exhibit higher long-term bias, and the covariability of ETEB and ETWB decreases significantly. Several factors related to either failure of ETWB, such as errors in ΔTWS and precipitation, or failure of ETEB, such as treatment of snowfall and horizontal heat advection, explain some of these discrepancies. These results mirror and build on conclusions from other studies: on interannual time scales, ΔTWS and error in precipitation estimates are nonnegligible uncertainties in ET estimates based on a terrestrial water budget, and this confounds their comparison to energy balance ET models. However, there is also evidence that in at least some regions, climate and landscape features may also influence the accuracy and long-term bias of ET estimates from energy balance models, and these potential errors should be considered when using these gridded products in hydrologic applications.
