Abstract
The subseasonal variability of winter air temperature in China during 2021/22 underwent significant changes, showing warm, warm, and cold anomalies during 2–23 December 2021 (P1), 1–27 January 2022 (P2), and 28 January–24 February 2022 (P3). The strong (weak) zonal circulation over East Asia led to positive (negative) surface air temperature anomalies (SATAs) during P1 and P2 (P3). The position of the Siberian high affected the distribution of the warmest center of SATA over northeastern and northwestern China in P1 and P2, respectively. Further investigations indicated that intraseasonal components (10–90 days) primarily drove the warm-to-cold transition in China during P2 and P3, contributing to 79.5% of the variance in SATA in winter 2021/22. Strong (weak) East Asian intraseasonal zonal circulations and positive (negative) meridional wind anomalies over China–Lake Baikal led to warm (cold) anomalies over China during P2 (P3). East Asian circulation alternations from P2 to P3 were associated with a shift in intraseasonal geopotential height anomalies over the North Atlantic region from positive to negative in the mid- to high troposphere through the propagation of north and south branch wave trains. The reversal of the North Atlantic geopotential height anomalies between P2 and P3 was modulated by intraseasonal higher-latitude SST anomalies over the North Atlantic and the location of intraseasonal stratospheric polar vortex. Furthermore, the intensified south branch wave train from the Indian Peninsula to China in the mid- to high troposphere was associated with active convection over the tropical western Indian Ocean during P3. These processes could be verified by using the linear baroclinic model.
Abstract
The subseasonal variability of winter air temperature in China during 2021/22 underwent significant changes, showing warm, warm, and cold anomalies during 2–23 December 2021 (P1), 1–27 January 2022 (P2), and 28 January–24 February 2022 (P3). The strong (weak) zonal circulation over East Asia led to positive (negative) surface air temperature anomalies (SATAs) during P1 and P2 (P3). The position of the Siberian high affected the distribution of the warmest center of SATA over northeastern and northwestern China in P1 and P2, respectively. Further investigations indicated that intraseasonal components (10–90 days) primarily drove the warm-to-cold transition in China during P2 and P3, contributing to 79.5% of the variance in SATA in winter 2021/22. Strong (weak) East Asian intraseasonal zonal circulations and positive (negative) meridional wind anomalies over China–Lake Baikal led to warm (cold) anomalies over China during P2 (P3). East Asian circulation alternations from P2 to P3 were associated with a shift in intraseasonal geopotential height anomalies over the North Atlantic region from positive to negative in the mid- to high troposphere through the propagation of north and south branch wave trains. The reversal of the North Atlantic geopotential height anomalies between P2 and P3 was modulated by intraseasonal higher-latitude SST anomalies over the North Atlantic and the location of intraseasonal stratospheric polar vortex. Furthermore, the intensified south branch wave train from the Indian Peninsula to China in the mid- to high troposphere was associated with active convection over the tropical western Indian Ocean during P3. These processes could be verified by using the linear baroclinic model.
Abstract
This study adopts a curvature dynamics approach to understand and predict the trajectory of an idealized depth-averaged barotropic outflow onto a slope in shallow water. A novel equation for streamwise curvature dynamics was derived from the barotropic vorticity equation and applied to a momentum jet subject to bottom friction, topographic slope, and planetary rotation. The terms in the curvature dynamics equation have a natural geometric interpretation whereby each physical process can influence the flow direction. It is shown that a weakly spreading jet onto a steep slope admits the formulation of a 1D ordinary differential equation system in a streamline coordinate system, yielding an integrable ordinary differential equation system that predicts the kinematical behavior of the jet. The 1D model was compared with a set of high-resolution idealized depth-averaged circulation model simulations where bottom friction, planetary rotation, and bottom slope were varied. Favorable performance of the 1D reduced physics model was found, especially in the near field of the outflow. The effect of nonlinear processes such as topographic stretching and bottom torque on the fate of the jet outflow is explained using curvature dynamics. Even in the tropics, planetary rotation can have a surprisingly strong influence on the near-field deflection of an intermediate-scale jet, provided that it flows across steep topography.
Abstract
This study adopts a curvature dynamics approach to understand and predict the trajectory of an idealized depth-averaged barotropic outflow onto a slope in shallow water. A novel equation for streamwise curvature dynamics was derived from the barotropic vorticity equation and applied to a momentum jet subject to bottom friction, topographic slope, and planetary rotation. The terms in the curvature dynamics equation have a natural geometric interpretation whereby each physical process can influence the flow direction. It is shown that a weakly spreading jet onto a steep slope admits the formulation of a 1D ordinary differential equation system in a streamline coordinate system, yielding an integrable ordinary differential equation system that predicts the kinematical behavior of the jet. The 1D model was compared with a set of high-resolution idealized depth-averaged circulation model simulations where bottom friction, planetary rotation, and bottom slope were varied. Favorable performance of the 1D reduced physics model was found, especially in the near field of the outflow. The effect of nonlinear processes such as topographic stretching and bottom torque on the fate of the jet outflow is explained using curvature dynamics. Even in the tropics, planetary rotation can have a surprisingly strong influence on the near-field deflection of an intermediate-scale jet, provided that it flows across steep topography.
Abstract
The export of the North Atlantic Deep Water (NADW) from the subpolar North Atlantic is known to affect the variability in the lower limb of the Atlantic meridional overturning circulation (AMOC). However, the respective impact from the transport in the upper (UNADW) and lower NADW (LNADW) layers, and from the various transport branches through the boundary and interior flows, on the subpolar overturning variability remains elusive. To address this, the spatiotemporal characteristics of the circulation of NADW throughout the eastern subpolar basins are examined, mainly based on the 2014-2020 observations from the transatlantic OSNAP (Overturning in the Subpolar North Atlantic Program) array. It reveals that the time-mean transport within the overturning’s lower limb across the eastern subpolar gyre (−13.0 ± 0.5 Sv) mostly occurs in the LNADW layer (−9.4 Sv or 72% of the mean), while the lower limb variability is mainly concentrated in the UNADW layer (57% of the total variance). This analysis further demonstrates a dominant role in the lower limb variability by coherent intra-seasonal changes across the region that result from a basin-wide barotropic response to changing wind fields. By comparison, there is just a weak seasonal cycle in the flows along the western boundary of the basins, in response to the surface buoyancy-induced water mass transformation.
Abstract
The export of the North Atlantic Deep Water (NADW) from the subpolar North Atlantic is known to affect the variability in the lower limb of the Atlantic meridional overturning circulation (AMOC). However, the respective impact from the transport in the upper (UNADW) and lower NADW (LNADW) layers, and from the various transport branches through the boundary and interior flows, on the subpolar overturning variability remains elusive. To address this, the spatiotemporal characteristics of the circulation of NADW throughout the eastern subpolar basins are examined, mainly based on the 2014-2020 observations from the transatlantic OSNAP (Overturning in the Subpolar North Atlantic Program) array. It reveals that the time-mean transport within the overturning’s lower limb across the eastern subpolar gyre (−13.0 ± 0.5 Sv) mostly occurs in the LNADW layer (−9.4 Sv or 72% of the mean), while the lower limb variability is mainly concentrated in the UNADW layer (57% of the total variance). This analysis further demonstrates a dominant role in the lower limb variability by coherent intra-seasonal changes across the region that result from a basin-wide barotropic response to changing wind fields. By comparison, there is just a weak seasonal cycle in the flows along the western boundary of the basins, in response to the surface buoyancy-induced water mass transformation.
Abstract
Extreme precipitation is expected to pose a more severe threat to human society in the future. This work assessed the historical performance and future changes in extreme precipitation and related atmospheric conditions in a large ensemble climate prediction dataset, the database for Policy Decision-making for Future climate change (d4PDF), over East Asia. Compared with the Tropical Rainfall Measuring Mission (TRMM) and fifth major global reanalysis produced by ECMWF (ERA5) datasets, the historical climate in d4PDF represents favorably the precipitation characteristics and the atmospheric conditions, although some differences are notable in the moisture, vertical motion, and cloud water fields. The future climate projection indicates that both the frequency and intensity of heavy precipitation events over East Asia increase compared with those in the present climate. However, when comparing the atmospheric conditions in the historical and future climates for the same precipitation intensity range, the future climate indicates smaller relative humidity, weaker ascent, less cloud water content, and smaller temperature lapse rate, which negatively affect generating extreme precipitation events. The comparison of the precipitation intensity at the same amount of precipitable water between the historical and future climates indicates that extreme precipitation is weaker in the future because of the more stabilized troposphere in the future. The general increase in extreme precipitation under future climate is primarily due to the enhanced increase in precipitable water in the higher temperature ranges, which counteracts the negative conditions of the stabilized troposphere.
Significance Statement
Extreme precipitation can have disastrous effects on human lives, economy, and ecosystems and is anticipated to significantly increase in both intensity and frequency under future climate. The purpose of this study is to investigate the mechanism for the future change of extreme precipitation. We examined the relationship between future changes in extreme precipitation and changes in the related atmospheric conditions. It is important for reducing uncertainties in future projections of extreme precipitation. Our results highlight that the future atmospheric condition is unfavorable for generating future extreme precipitation events in terms of stability and humidity changes. The increase in the column moisture content is the primary factor for the increase of extreme precipitation, which counteracts the negative conditions.
Abstract
Extreme precipitation is expected to pose a more severe threat to human society in the future. This work assessed the historical performance and future changes in extreme precipitation and related atmospheric conditions in a large ensemble climate prediction dataset, the database for Policy Decision-making for Future climate change (d4PDF), over East Asia. Compared with the Tropical Rainfall Measuring Mission (TRMM) and fifth major global reanalysis produced by ECMWF (ERA5) datasets, the historical climate in d4PDF represents favorably the precipitation characteristics and the atmospheric conditions, although some differences are notable in the moisture, vertical motion, and cloud water fields. The future climate projection indicates that both the frequency and intensity of heavy precipitation events over East Asia increase compared with those in the present climate. However, when comparing the atmospheric conditions in the historical and future climates for the same precipitation intensity range, the future climate indicates smaller relative humidity, weaker ascent, less cloud water content, and smaller temperature lapse rate, which negatively affect generating extreme precipitation events. The comparison of the precipitation intensity at the same amount of precipitable water between the historical and future climates indicates that extreme precipitation is weaker in the future because of the more stabilized troposphere in the future. The general increase in extreme precipitation under future climate is primarily due to the enhanced increase in precipitable water in the higher temperature ranges, which counteracts the negative conditions of the stabilized troposphere.
Significance Statement
Extreme precipitation can have disastrous effects on human lives, economy, and ecosystems and is anticipated to significantly increase in both intensity and frequency under future climate. The purpose of this study is to investigate the mechanism for the future change of extreme precipitation. We examined the relationship between future changes in extreme precipitation and changes in the related atmospheric conditions. It is important for reducing uncertainties in future projections of extreme precipitation. Our results highlight that the future atmospheric condition is unfavorable for generating future extreme precipitation events in terms of stability and humidity changes. The increase in the column moisture content is the primary factor for the increase of extreme precipitation, which counteracts the negative conditions.
Abstract
A skill baseline for five-day, 34-, 50-, and 64-knot (1 kt = 0.514 m s−1) tropical cyclone (TC) wind radii forecasts is described. The Markov Model CLiper (MMCL) generates a sequence of 12-h forecasts out to a forecast length limited only by the length of the forecast track and intensity. The model employs a climatology of TC size based on infrared satellite imagery, a Markov chain, and a basin-specific drift. MMCL uses the initial wind radii and initial forecast track and intensity as input. Unlike the previously developed wind radii climatology and persistence model (DRCL) that reverts to a climatological size and shape after approximately 48 h, MMCL retains more of its initial size and asymmetry and is likely more palatable for use in operational forecasting. MMCL runs operationally in the western North Pacific basin, the North Indian Ocean, and the Southern Hemisphere for the Joint Typhoon Warning Center (JTWC) in Pearl Harbor, Hawaii. This work also describes the development of Atlantic and eastern North Pacific versions of MMCL. MMCL’s formulation allows unlimited extension of forecast lead time without reverting to a generic climatological size and shape. Independent forecast comparisons between MMCL and DRCL for the 2020–2022 seasons demonstrates that MMCL’s mean absolute errors are generally smaller and biases are closer to zero in North Atlantic, and eastern North Pacific basins, and in the Southern Hemisphere. This validation includes a few example forecasts and demonstrates that MMCL can be used both as a baseline for assessing wind radii forecast skill and operational use.
Abstract
A skill baseline for five-day, 34-, 50-, and 64-knot (1 kt = 0.514 m s−1) tropical cyclone (TC) wind radii forecasts is described. The Markov Model CLiper (MMCL) generates a sequence of 12-h forecasts out to a forecast length limited only by the length of the forecast track and intensity. The model employs a climatology of TC size based on infrared satellite imagery, a Markov chain, and a basin-specific drift. MMCL uses the initial wind radii and initial forecast track and intensity as input. Unlike the previously developed wind radii climatology and persistence model (DRCL) that reverts to a climatological size and shape after approximately 48 h, MMCL retains more of its initial size and asymmetry and is likely more palatable for use in operational forecasting. MMCL runs operationally in the western North Pacific basin, the North Indian Ocean, and the Southern Hemisphere for the Joint Typhoon Warning Center (JTWC) in Pearl Harbor, Hawaii. This work also describes the development of Atlantic and eastern North Pacific versions of MMCL. MMCL’s formulation allows unlimited extension of forecast lead time without reverting to a generic climatological size and shape. Independent forecast comparisons between MMCL and DRCL for the 2020–2022 seasons demonstrates that MMCL’s mean absolute errors are generally smaller and biases are closer to zero in North Atlantic, and eastern North Pacific basins, and in the Southern Hemisphere. This validation includes a few example forecasts and demonstrates that MMCL can be used both as a baseline for assessing wind radii forecast skill and operational use.
Abstract
We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. The resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves the capture of SIF seasonal and interannual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather, the original noisy retrievals are needed as the target for the NN training process.
Significance Statement
The purpose of this study is to document and demonstrate a machine learning algorithm that is used to effectively reduce noise and artifacts in a satellite data product, solar-induced fluorescence (SIF) from chlorophyll. This is important because SIF retrievals are typically noisy, and the noise limits their ability to be used for diagnosing plant health and productivity. Our results show substantial improvement in SIF retrievals that may lead to new applications. Our approach can be similarly applied to other noisy satellite data products.
Abstract
We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. The resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves the capture of SIF seasonal and interannual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather, the original noisy retrievals are needed as the target for the NN training process.
Significance Statement
The purpose of this study is to document and demonstrate a machine learning algorithm that is used to effectively reduce noise and artifacts in a satellite data product, solar-induced fluorescence (SIF) from chlorophyll. This is important because SIF retrievals are typically noisy, and the noise limits their ability to be used for diagnosing plant health and productivity. Our results show substantial improvement in SIF retrievals that may lead to new applications. Our approach can be similarly applied to other noisy satellite data products.
Abstract
Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPEs) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of “heavy” precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, flash flood guidance (FFG) exceedances provide the best correspondence with observed flash floods, although the best correspondence is often found for exceedances of ratios of FFG above or below unity. In the interior western United States, NOAA Atlas 14 derived recurrence interval thresholds (for the southwestern United States) and static thresholds (for the northern and central Rockies) provide better correspondence. The 6-h QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the West Coast and southwestern United States. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern United States. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds.
Significance Statement
Flash floods result from heavy rainfall, but it is difficult to know exactly how much rain will cause a flash flood in a particular location. Furthermore, different precipitation datasets can show very different amounts of precipitation, even from the same storm. This study examines how well different precipitation datasets and model forecasts, used by forecasters to warn the public of flash flooding, represent heavy rainfall leading to flash flooding around the United States. We found that different datasets have dramatically different numbers of heavy rainfall events and that high-resolution model forecasts of heavy rain correspond with observed flash flood events about as well as precipitation datasets based on rain gauge and radar in some regions of the country with few observations.
Abstract
Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPEs) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of “heavy” precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, flash flood guidance (FFG) exceedances provide the best correspondence with observed flash floods, although the best correspondence is often found for exceedances of ratios of FFG above or below unity. In the interior western United States, NOAA Atlas 14 derived recurrence interval thresholds (for the southwestern United States) and static thresholds (for the northern and central Rockies) provide better correspondence. The 6-h QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the West Coast and southwestern United States. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern United States. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds.
Significance Statement
Flash floods result from heavy rainfall, but it is difficult to know exactly how much rain will cause a flash flood in a particular location. Furthermore, different precipitation datasets can show very different amounts of precipitation, even from the same storm. This study examines how well different precipitation datasets and model forecasts, used by forecasters to warn the public of flash flooding, represent heavy rainfall leading to flash flooding around the United States. We found that different datasets have dramatically different numbers of heavy rainfall events and that high-resolution model forecasts of heavy rain correspond with observed flash flood events about as well as precipitation datasets based on rain gauge and radar in some regions of the country with few observations.
Abstract
Summertime compound dry and hot events pose severe threats to agricultural production and human health, especially those with a long duration. Considering the joint evolution of such hazards in space and time, spatiotemporal compound long-duration dry and hot (SLDDH) events in China during 1961–2022 are identified with a process-oriented method. Here, we investigate the associated large-scale atmospheric circulation patterns and the physical processes causing their precipitation and temperature anomalies. In all regions of China, this persistent dry–hot compound extreme is accompanied by anomalous high pressure systems along with enhanced descending motion, increased net surface solar radiation, and decreased water vapor flux convergence. Moisture budget diagnosis shows that precipitation deficits during the SLDDH events are produced primarily by the suppressed vertical moisture advection associated with the dynamical contribution of anomalous subsidence, while the thermodynamic process due to the anomaly in atmospheric moisture content makes a small contribution. Horizontal temperature advection generally plays a negative role in sustaining SLDDH events, while it helps trigger the events in North China. In most regions, adiabatic warming due to abnormal subsidence plays a dominant role in determining the near-surface high temperatures during the long-lasting warm and dry periods, whereas diabatic heating has a cooling or small effect therein. However, in some northern areas such as North China and northern Xinjiang, hot extremes during SLDDH events arise from a combination of diabatic heating and adiabatic warming. This study thus quantifies and reveals the crucial factors leading to the severity of compound dry and hot events.
Abstract
Summertime compound dry and hot events pose severe threats to agricultural production and human health, especially those with a long duration. Considering the joint evolution of such hazards in space and time, spatiotemporal compound long-duration dry and hot (SLDDH) events in China during 1961–2022 are identified with a process-oriented method. Here, we investigate the associated large-scale atmospheric circulation patterns and the physical processes causing their precipitation and temperature anomalies. In all regions of China, this persistent dry–hot compound extreme is accompanied by anomalous high pressure systems along with enhanced descending motion, increased net surface solar radiation, and decreased water vapor flux convergence. Moisture budget diagnosis shows that precipitation deficits during the SLDDH events are produced primarily by the suppressed vertical moisture advection associated with the dynamical contribution of anomalous subsidence, while the thermodynamic process due to the anomaly in atmospheric moisture content makes a small contribution. Horizontal temperature advection generally plays a negative role in sustaining SLDDH events, while it helps trigger the events in North China. In most regions, adiabatic warming due to abnormal subsidence plays a dominant role in determining the near-surface high temperatures during the long-lasting warm and dry periods, whereas diabatic heating has a cooling or small effect therein. However, in some northern areas such as North China and northern Xinjiang, hot extremes during SLDDH events arise from a combination of diabatic heating and adiabatic warming. This study thus quantifies and reveals the crucial factors leading to the severity of compound dry and hot events.
Abstract
Density-driven steric seawater changes are a leading-order contributor to global mean sea level rise. However, inter-model differences in the magnitude and spatial patterns of steric sea level rise exist at regional scales and often emerge during the spin-up and pre-industrial control integrations of climate models. Steric sea level results from an eddy-permitting climate model, GFDL-CM4, are compared with a lower resolution counterpart, GFDL-ESM4. The results from both models are examined through basin-scale heat budgets and watermass analysis, and we compare the patterns of ocean heat uptake, redistribution, and sea level differ in ocean-only (i.e. OMIP) and coupled climate configurations. After correcting for model drift, both GFDL-CM4 and GFDL-ESM4 simulate nearly equivalent ocean heat content change and global sea level rise during the historical period. However, the GFDL-CM4 model exhibits as much as a 40% increase in surface ocean heat uptake in the Southern Ocean and subsequent increases in horizontal export to other ocean basins after bias correction. The results suggest regional differences in the processes governing Southern Ocean heat export, such as the formation of AAIW, SPMW, and gyre transport between the two models, and that sea level changes in these models cannot be fully bias-corrected. Since the process-level differences between the two models are evident in the preindustrial control simulations of both models, these results suggest that the control simulations are important for identifying and correcting sea-level related model biases.
Abstract
Density-driven steric seawater changes are a leading-order contributor to global mean sea level rise. However, inter-model differences in the magnitude and spatial patterns of steric sea level rise exist at regional scales and often emerge during the spin-up and pre-industrial control integrations of climate models. Steric sea level results from an eddy-permitting climate model, GFDL-CM4, are compared with a lower resolution counterpart, GFDL-ESM4. The results from both models are examined through basin-scale heat budgets and watermass analysis, and we compare the patterns of ocean heat uptake, redistribution, and sea level differ in ocean-only (i.e. OMIP) and coupled climate configurations. After correcting for model drift, both GFDL-CM4 and GFDL-ESM4 simulate nearly equivalent ocean heat content change and global sea level rise during the historical period. However, the GFDL-CM4 model exhibits as much as a 40% increase in surface ocean heat uptake in the Southern Ocean and subsequent increases in horizontal export to other ocean basins after bias correction. The results suggest regional differences in the processes governing Southern Ocean heat export, such as the formation of AAIW, SPMW, and gyre transport between the two models, and that sea level changes in these models cannot be fully bias-corrected. Since the process-level differences between the two models are evident in the preindustrial control simulations of both models, these results suggest that the control simulations are important for identifying and correcting sea-level related model biases.
Abstract
A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.
Abstract
A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.
