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Abstract
A statistical analysis of tropical upper-tropospheric trough (TUTT) cells over the western North Pacific Ocean (WNP) during 2006 to 2015 is performed using the NCEP Final reanalysis. A total of 369 TUTT-cell events or 6836 TUTT cells are identified, with a peak frequency in July. Most TUTT cells form to the east of 150°E and then move southwestward with a mean speed of 6.6 m s−1 and a mean life span of 4.4 days. About 75% of the TUTT cells have radii of <500 km with 200-hPa central heights of <1239.4 dam. In general, TUTT cells exhibit negative height anomalies above 450 hPa, with their peak amplitudes at 200 hPa, pronounced cold anomalies in the 650–200-hPa layer, and significant cyclonic vorticity in the 550–125-hPa layer. A comparison of the composite TUTT cells among the eastern, central, and western WNP areas shows the generation of an intense cold-cored vortex as a result of the southward penetration of a midlatitude trough into a climatological TUTT over the eastern WNP region. The TUTT cell with pronounced rotation is cut off from the midlatitude westerlies after moving to the central WNP region, where it enters its mature phase, under the influence of northeasterly flow. The TUTT cell weakens in rotation and shrinks in size, diminishing within the TUTT after arriving at the western WNP region. Results suggest that, although most TUTT cells may diminish before reaching the western WNP, their vertical influences may extend to the surface layer and last longer than their signals at 200 hPa.
Abstract
A statistical analysis of tropical upper-tropospheric trough (TUTT) cells over the western North Pacific Ocean (WNP) during 2006 to 2015 is performed using the NCEP Final reanalysis. A total of 369 TUTT-cell events or 6836 TUTT cells are identified, with a peak frequency in July. Most TUTT cells form to the east of 150°E and then move southwestward with a mean speed of 6.6 m s−1 and a mean life span of 4.4 days. About 75% of the TUTT cells have radii of <500 km with 200-hPa central heights of <1239.4 dam. In general, TUTT cells exhibit negative height anomalies above 450 hPa, with their peak amplitudes at 200 hPa, pronounced cold anomalies in the 650–200-hPa layer, and significant cyclonic vorticity in the 550–125-hPa layer. A comparison of the composite TUTT cells among the eastern, central, and western WNP areas shows the generation of an intense cold-cored vortex as a result of the southward penetration of a midlatitude trough into a climatological TUTT over the eastern WNP region. The TUTT cell with pronounced rotation is cut off from the midlatitude westerlies after moving to the central WNP region, where it enters its mature phase, under the influence of northeasterly flow. The TUTT cell weakens in rotation and shrinks in size, diminishing within the TUTT after arriving at the western WNP region. Results suggest that, although most TUTT cells may diminish before reaching the western WNP, their vertical influences may extend to the surface layer and last longer than their signals at 200 hPa.
Abstract
Turbulent mixing in the planetary boundary layer (PBL) governs the vertical exchange of heat, moisture, momentum, trace gases, and aerosols in the surface–atmosphere interface. The PBL height (PBLH) represents the maximum height of the free atmosphere that is directly influenced by Earth’s surface. This study uses a multidata synthesis approach from an ensemble of multiple global datasets of radiosonde observations, reanalysis products, and climate model simulations to examine the spatial patterns of long-term PBLH trends over land between 60°S and 60°N for the period 1979–2019. By considering both the sign and statistical significance of trends, we identify large-scale regions where the change signal is robust and consistent to increase our confidence in the obtained results. Despite differences in the magnitude and sign of PBLH trends over many areas, all datasets reveal a consensus on increasing PBLH over the enormous and very dry Sahara Desert and Arabian Peninsula (SDAP) and declining PBLH in India. At the global scale, the changes in PBLH are significantly correlated positively with the changes in surface heating and negatively with the changes in surface moisture, consistent with theory and previous findings in the literature. The rising PBLH is in good agreement with increasing sensible heat and surface temperature and decreasing relative humidity over the SDAP associated with desert amplification, while the declining PBLH resonates well with increasing relative humidity and latent heat and decreasing sensible heat and surface warming in India. The PBLH changes agree with radiosonde soundings over the SDAP but cannot be validated over India due to lack of good-quality radiosonde observations.
Abstract
Turbulent mixing in the planetary boundary layer (PBL) governs the vertical exchange of heat, moisture, momentum, trace gases, and aerosols in the surface–atmosphere interface. The PBL height (PBLH) represents the maximum height of the free atmosphere that is directly influenced by Earth’s surface. This study uses a multidata synthesis approach from an ensemble of multiple global datasets of radiosonde observations, reanalysis products, and climate model simulations to examine the spatial patterns of long-term PBLH trends over land between 60°S and 60°N for the period 1979–2019. By considering both the sign and statistical significance of trends, we identify large-scale regions where the change signal is robust and consistent to increase our confidence in the obtained results. Despite differences in the magnitude and sign of PBLH trends over many areas, all datasets reveal a consensus on increasing PBLH over the enormous and very dry Sahara Desert and Arabian Peninsula (SDAP) and declining PBLH in India. At the global scale, the changes in PBLH are significantly correlated positively with the changes in surface heating and negatively with the changes in surface moisture, consistent with theory and previous findings in the literature. The rising PBLH is in good agreement with increasing sensible heat and surface temperature and decreasing relative humidity over the SDAP associated with desert amplification, while the declining PBLH resonates well with increasing relative humidity and latent heat and decreasing sensible heat and surface warming in India. The PBLH changes agree with radiosonde soundings over the SDAP but cannot be validated over India due to lack of good-quality radiosonde observations.
Abstract
A high-resolution (3–8 km) regional oceanic general circulation model is utilized to understand the sea surface temperature (SST) variability of Ningaloo Niño in the southeast Indian Ocean (SEIO). The model reproduces eight Ningaloo Niño events with good fidelity and reveals complicated spatial structures. Mesoscale noises are seen in the warming signature and confirmed by satellite microwave SST data. Model experiments are carried out to quantitatively evaluate the effects of key processes. The results reveal that the surface turbulent heat flux (primarily latent heat flux) is the most important process (contribution > 68%) in driving and damping the SST warming for most events, while the roles of the Indonesian Throughflow (~15%) and local wind forcing are secondary. A suitable air temperature warming is essential to reproducing the reduced surface latent heat loss during the growth of SST warming (~66%), whereas the effect of the increased air humidity is negligibly small (1%). The established SST warming in the mature phase causes increased latent heat loss that initiates the decay of warming. A 20-member ensemble simulation is performed for the 2010/11 super Ningaloo Niño, which confirms the strong influence of ocean internal processes in the redistribution of SST warming signatures. Oceanic eddies can dramatically modulate the magnitudes of local SST warming, particularly in offshore areas where the “signal-to-noise” ratio is low, raising a caution for evaluating the predictability of Ningaloo Niño and its environmental consequences.
Abstract
A high-resolution (3–8 km) regional oceanic general circulation model is utilized to understand the sea surface temperature (SST) variability of Ningaloo Niño in the southeast Indian Ocean (SEIO). The model reproduces eight Ningaloo Niño events with good fidelity and reveals complicated spatial structures. Mesoscale noises are seen in the warming signature and confirmed by satellite microwave SST data. Model experiments are carried out to quantitatively evaluate the effects of key processes. The results reveal that the surface turbulent heat flux (primarily latent heat flux) is the most important process (contribution > 68%) in driving and damping the SST warming for most events, while the roles of the Indonesian Throughflow (~15%) and local wind forcing are secondary. A suitable air temperature warming is essential to reproducing the reduced surface latent heat loss during the growth of SST warming (~66%), whereas the effect of the increased air humidity is negligibly small (1%). The established SST warming in the mature phase causes increased latent heat loss that initiates the decay of warming. A 20-member ensemble simulation is performed for the 2010/11 super Ningaloo Niño, which confirms the strong influence of ocean internal processes in the redistribution of SST warming signatures. Oceanic eddies can dramatically modulate the magnitudes of local SST warming, particularly in offshore areas where the “signal-to-noise” ratio is low, raising a caution for evaluating the predictability of Ningaloo Niño and its environmental consequences.
Abstract
Mesoscale eddies, ubiquitous in the global ocean, play a key role in the climate system by stirring and mixing key tracers. Estimating, understanding, and predicting eddy diffusivity is of great significance for designing suitable eddy parameterization schemes for coarse-resolution climate models. This is because climate model results are sensitive to the choice of eddy diffusivity magnitudes. Using 24-yr satellite altimeter data and a Lagrangian approach, we estimate time-dependent global surface cross-stream eddy diffusivities. We found that eddy diffusivity has nonnegligible temporal variability, and the regionally averaged eddy diffusivity is significantly correlated with the climate indices, including the North Pacific Gyre Oscillation, Atlantic multidecadal oscillation, El Niño–Southern Oscillation, Pacific decadal oscillation, and dipole mode index. We also found that, compared to the suppressed mixing length theory, random forest (RF) is more effective in capturing the temporal variability of regionally averaged eddy diffusivity. Our results indicate the need for using time-dependent eddy mixing coefficients in climate models and demonstrate the advantage of RF in predicting mixing temporal variability.
Significance Statement
Mixing induced by ocean eddies can greatly modulate the ocean circulation and climate variability. Steady eddy mixing coefficients are often specified in coarse-resolution climate models. However, using satellite observations, we show that the eddy mixing rate has significant temporal variability at the global ocean surface. The regional temporal variability of eddy mixing is linked with large-scale climate variability (e.g., North Pacific Gyre Oscillation and Atlantic multidecadal oscillation). We found that random forest, a user-friendly machine learning algorithm, is a better tool to predict the mixing temporal variability than the conventional mixing theory. This study suggests the possibility of improving climate model performance by using time-dependent eddy mixing coefficients inferred from machine learning methods.
Abstract
Mesoscale eddies, ubiquitous in the global ocean, play a key role in the climate system by stirring and mixing key tracers. Estimating, understanding, and predicting eddy diffusivity is of great significance for designing suitable eddy parameterization schemes for coarse-resolution climate models. This is because climate model results are sensitive to the choice of eddy diffusivity magnitudes. Using 24-yr satellite altimeter data and a Lagrangian approach, we estimate time-dependent global surface cross-stream eddy diffusivities. We found that eddy diffusivity has nonnegligible temporal variability, and the regionally averaged eddy diffusivity is significantly correlated with the climate indices, including the North Pacific Gyre Oscillation, Atlantic multidecadal oscillation, El Niño–Southern Oscillation, Pacific decadal oscillation, and dipole mode index. We also found that, compared to the suppressed mixing length theory, random forest (RF) is more effective in capturing the temporal variability of regionally averaged eddy diffusivity. Our results indicate the need for using time-dependent eddy mixing coefficients in climate models and demonstrate the advantage of RF in predicting mixing temporal variability.
Significance Statement
Mixing induced by ocean eddies can greatly modulate the ocean circulation and climate variability. Steady eddy mixing coefficients are often specified in coarse-resolution climate models. However, using satellite observations, we show that the eddy mixing rate has significant temporal variability at the global ocean surface. The regional temporal variability of eddy mixing is linked with large-scale climate variability (e.g., North Pacific Gyre Oscillation and Atlantic multidecadal oscillation). We found that random forest, a user-friendly machine learning algorithm, is a better tool to predict the mixing temporal variability than the conventional mixing theory. This study suggests the possibility of improving climate model performance by using time-dependent eddy mixing coefficients inferred from machine learning methods.
Abstract
To effectively reduce model bias and improve assimilation quality, we adopt a hybrid adaptive approach of ensemble adjustment Kalman filter (EAKF) and multigrid analysis (MGA), called EAKF-MGA, to implement parameter optimization as follows. For each assimilation cycle, observations are used to adjust the prior ensembles of both state variables and parameters using the EAKF without inflation. Then, the MGA is adaptively triggered to extract multiscale information from the observational residual to innovate the ensemble mean of the state once again. Results of biased twin experiments consisting of a barotropic spectral model and idealized observation systems show that the proposed EAKF-MGA is insensitive to state variance inflation and localization during the parameter optimization process, compared with the EAKF with adaptive inflation. We also find that computational efficiency is another important advantage of the EAKF-MGA for both state estimation and parameter estimation since extremely small ensemble size is allowed, while the EAKF with adaptive inflation does not work anymore. In essence, the EAKF-MGA is designed to estimate and correct systematic errors jointly with model’s state variables. Through alleviating biases, including the model bias caused by the biased parameter and the analysis bias resulting from the sampling noise given the limited ensemble size, it can be guaranteed that the analysis in the EAKF-MGA will be proceeded onward with the standard assumption of the unbiased model background field in modern data assimilation theory to be met.
Abstract
To effectively reduce model bias and improve assimilation quality, we adopt a hybrid adaptive approach of ensemble adjustment Kalman filter (EAKF) and multigrid analysis (MGA), called EAKF-MGA, to implement parameter optimization as follows. For each assimilation cycle, observations are used to adjust the prior ensembles of both state variables and parameters using the EAKF without inflation. Then, the MGA is adaptively triggered to extract multiscale information from the observational residual to innovate the ensemble mean of the state once again. Results of biased twin experiments consisting of a barotropic spectral model and idealized observation systems show that the proposed EAKF-MGA is insensitive to state variance inflation and localization during the parameter optimization process, compared with the EAKF with adaptive inflation. We also find that computational efficiency is another important advantage of the EAKF-MGA for both state estimation and parameter estimation since extremely small ensemble size is allowed, while the EAKF with adaptive inflation does not work anymore. In essence, the EAKF-MGA is designed to estimate and correct systematic errors jointly with model’s state variables. Through alleviating biases, including the model bias caused by the biased parameter and the analysis bias resulting from the sampling noise given the limited ensemble size, it can be guaranteed that the analysis in the EAKF-MGA will be proceeded onward with the standard assumption of the unbiased model background field in modern data assimilation theory to be met.
Abstract
This study proposes a statistical regression scheme to forecast tropical cyclone (TC) intensity at 12, 24, 36, 48, 60, and 72 h in the northwestern Pacific region. This study utilizes best track data from the Shanghai Typhoon Institute (STI), China, and the Joint Typhoon Warning Center (JTWC), United States, from 2000 to 2015. In addition to conventional factors involving climatology and persistence, this study pays close attention to the land effect on TC intensity change by considering a new factor involving the ratio of seawater area to land area (SL ratio) in the statistical regression model. TC intensity changes are investigated over the entire life-span, over the open ocean, near the coast, and after landfall. Data from 2000 to 2011 are used for model calibration, and data from 2012 to 2015 are used for model validation. The results show that the intensity change during the previous 12 h (DVMAX), the potential future intensity change (POT), and the area-averaged (200–800 km) wind shear at 1000–300 hPa (SHRD) are the most significant predictors of the intensity change for TCs over the open ocean and near the coast. Intensity forecasting for TCs near the coast and over land is improved with the addition of the SL ratio compared with that of the models that do not consider the SL ratio. As this study has considered the TC intensity change over the entire TC life-span, the proposed models are valuable and practical for forecasting TC intensity change over the open ocean, near the coast, and after landfall.
Abstract
This study proposes a statistical regression scheme to forecast tropical cyclone (TC) intensity at 12, 24, 36, 48, 60, and 72 h in the northwestern Pacific region. This study utilizes best track data from the Shanghai Typhoon Institute (STI), China, and the Joint Typhoon Warning Center (JTWC), United States, from 2000 to 2015. In addition to conventional factors involving climatology and persistence, this study pays close attention to the land effect on TC intensity change by considering a new factor involving the ratio of seawater area to land area (SL ratio) in the statistical regression model. TC intensity changes are investigated over the entire life-span, over the open ocean, near the coast, and after landfall. Data from 2000 to 2011 are used for model calibration, and data from 2012 to 2015 are used for model validation. The results show that the intensity change during the previous 12 h (DVMAX), the potential future intensity change (POT), and the area-averaged (200–800 km) wind shear at 1000–300 hPa (SHRD) are the most significant predictors of the intensity change for TCs over the open ocean and near the coast. Intensity forecasting for TCs near the coast and over land is improved with the addition of the SL ratio compared with that of the models that do not consider the SL ratio. As this study has considered the TC intensity change over the entire TC life-span, the proposed models are valuable and practical for forecasting TC intensity change over the open ocean, near the coast, and after landfall.
Abstract
Although the fixed covariance localization in the ensemble Kalman filter (EnKF) can significantly increase the reliability of background error covariance, it has been demonstrated that extreme impact radii can cause the EnKF to lose some useful information. Tuning an optimal impact radius, on the other hand, is always difficult for a general circulation model. The EnKF multiscale analysis (MSA) approach was presented to make up for the above-mentioned drawback of the fixed localization. As a follow-up, this study presents an adaptive compensatory approach to further improve the performance of the EnKF-MSA. The new method adaptively triggers a multigrid analysis (MGA) to extract multiscale information from the observational residual after the EnKF without inflation is completed at each analysis step. Within a biased twin experiment framework consisting of a barotropic spectral model and an idealized observing system, the performance of the adaptive method is examined. Results show that the MGA reduces the computational cost of the MSA by 93%. On the assimilation quality, the adaptive method has an incremental improvement over the EnKF-MSA. That is, the adaptive EnKF-MGA reduces to the EnKF without inflation, which is better than the EnKF-MSA, for moderate impact radii. The proposed scheme works for a broader range of impact radii than the standard EnKF (i.e., the EnKF with inflation). For extreme impact radii, the adaptive EnKF-MGA can produce smaller assimilation errors than the standard EnKF and shorten the spinup period by 53%. In addition, the computational cost of the MGA is negligible relative to that of the standard EnKF.
Abstract
Although the fixed covariance localization in the ensemble Kalman filter (EnKF) can significantly increase the reliability of background error covariance, it has been demonstrated that extreme impact radii can cause the EnKF to lose some useful information. Tuning an optimal impact radius, on the other hand, is always difficult for a general circulation model. The EnKF multiscale analysis (MSA) approach was presented to make up for the above-mentioned drawback of the fixed localization. As a follow-up, this study presents an adaptive compensatory approach to further improve the performance of the EnKF-MSA. The new method adaptively triggers a multigrid analysis (MGA) to extract multiscale information from the observational residual after the EnKF without inflation is completed at each analysis step. Within a biased twin experiment framework consisting of a barotropic spectral model and an idealized observing system, the performance of the adaptive method is examined. Results show that the MGA reduces the computational cost of the MSA by 93%. On the assimilation quality, the adaptive method has an incremental improvement over the EnKF-MSA. That is, the adaptive EnKF-MGA reduces to the EnKF without inflation, which is better than the EnKF-MSA, for moderate impact radii. The proposed scheme works for a broader range of impact radii than the standard EnKF (i.e., the EnKF with inflation). For extreme impact radii, the adaptive EnKF-MGA can produce smaller assimilation errors than the standard EnKF and shorten the spinup period by 53%. In addition, the computational cost of the MGA is negligible relative to that of the standard EnKF.
Abstract
A prognostic closure is introduced to, and evaluated in, NOAA’s Unified Forecast System. The closure addresses aspects that are not commonly represented in traditional cumulus convection parameterizations, and it departs from the previous assumptions of a negligible subgrid area coverage and statistical quasi-equilibrium at steady state, the latter of which becomes invalid at higher resolution. The new parameterization introduces a prognostic evolution of the convective updraft area fraction based on a moisture budget, and, together with the buoyancy-driven updraft vertical velocity, it completes the cloud-base mass flux. In addition, the new closure addresses stochasticity and includes a representation of subgrid convective organization using cellular automata as well as scale-adaptive considerations. The new cumulus convection closure shows potential for improved Madden–Julian oscillation (MJO) prediction. In our simulations we observe better propagation, amplitude, and phase of the MJO in a case study relative to the control simulation. This improvement can be partly attributed to a closer coupling between low-level moisture flux convergence and precipitation as revealed by a space–time coherence spectrum. In addition, we find that enhanced organization feedback representation and stochastic effects, represented using cellular automata, further enhance the amplitude and propagation of the MJO, and they provide realistic uncertainty estimates of convectively coupled equatorial waves at seasonal time scales. The scale-adaptive behavior of the scheme is also studied by running the global model with 25-, 13-, 9-, and 3-km grid spacing. It is found that the convective area fraction and the convective updraft velocity are both scale adaptive, leading to a reduction of subgrid convective precipitation in the higher-resolution simulations.
Abstract
A prognostic closure is introduced to, and evaluated in, NOAA’s Unified Forecast System. The closure addresses aspects that are not commonly represented in traditional cumulus convection parameterizations, and it departs from the previous assumptions of a negligible subgrid area coverage and statistical quasi-equilibrium at steady state, the latter of which becomes invalid at higher resolution. The new parameterization introduces a prognostic evolution of the convective updraft area fraction based on a moisture budget, and, together with the buoyancy-driven updraft vertical velocity, it completes the cloud-base mass flux. In addition, the new closure addresses stochasticity and includes a representation of subgrid convective organization using cellular automata as well as scale-adaptive considerations. The new cumulus convection closure shows potential for improved Madden–Julian oscillation (MJO) prediction. In our simulations we observe better propagation, amplitude, and phase of the MJO in a case study relative to the control simulation. This improvement can be partly attributed to a closer coupling between low-level moisture flux convergence and precipitation as revealed by a space–time coherence spectrum. In addition, we find that enhanced organization feedback representation and stochastic effects, represented using cellular automata, further enhance the amplitude and propagation of the MJO, and they provide realistic uncertainty estimates of convectively coupled equatorial waves at seasonal time scales. The scale-adaptive behavior of the scheme is also studied by running the global model with 25-, 13-, 9-, and 3-km grid spacing. It is found that the convective area fraction and the convective updraft velocity are both scale adaptive, leading to a reduction of subgrid convective precipitation in the higher-resolution simulations.
Abstract
The Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing experiments. The improvements in forecast skill of the Northern Hemisphere 2-m temperature and precipitation for weeks 3 and 4 are marginal, especially for North America. The bias-corrected CFSv2-predicted SST experiment generally delivers superior performance with statistically significant improvement in spatially and temporally aggregated 2-m temperature RPSSs over North America. Improved representation of the SST forcing (AMIP) increased the forecast skill for MJO indices up through week 2, but there is no significant improvement of the MJO forecast skill for weeks 3 and 4. These results are obtained over a short period with weak MJO activity and are also subject to internal model weaknesses in representing the MJO. Additional studies covering longer periods with upgraded model physics are warranted.
Abstract
The Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing experiments. The improvements in forecast skill of the Northern Hemisphere 2-m temperature and precipitation for weeks 3 and 4 are marginal, especially for North America. The bias-corrected CFSv2-predicted SST experiment generally delivers superior performance with statistically significant improvement in spatially and temporally aggregated 2-m temperature RPSSs over North America. Improved representation of the SST forcing (AMIP) increased the forecast skill for MJO indices up through week 2, but there is no significant improvement of the MJO forecast skill for weeks 3 and 4. These results are obtained over a short period with weak MJO activity and are also subject to internal model weaknesses in representing the MJO. Additional studies covering longer periods with upgraded model physics are warranted.
Abstract
The National Centers for Environmental Prediction have generated an 18-yr (1999–2016) subseasonal (weeks 3 and 4) reforecast to support the Climate Prediction Center’s operational mission. To create this reforecast, the subseasonal experiment version of the GEFS was run every Wednesday, initialized at 0000 UTC with 11 members. The Climate Forecast System Reanalysis (CFSR) and Global Data Assimilation System (GDAS) served as the initial analyses for 1999–2010 and 2011–16, respectively. The analysis of 2-m temperature error demonstrates that the model has a strong warm bias over the Northern Hemisphere (NH) and North America (NA) during the warm season. During the boreal winter, the 2-m temperature errors over NA exhibit large interannual and intraseasonal variability. For NA and the NH, weeks 3 and 4 errors are mostly saturated, with initial conditions having a negligible impact. Week 2 errors (day 11) are ~88.6% and 86.6% of their saturated levels, respectively. The 1999–2015 reforecast biases were used to calibrate the 2-m temperature forecasts in 2016, which reduces (increases) the systematic error (forecast skill) for NA, the NH, the Southern Hemisphere, and the tropics, with a maximum benefit for NA during the warm season. Overall, analysis adjustment for the CFSR period makes bias characteristics more consistent with the GDAS period over the NH and tropics and substantially improves the corresponding forecast skill levels. The calibration of the forecast using week 2 bias provides similar skill to using weeks 3 and 4 bias, promising the feasibility of using week 2 bias to calibrate the weeks 3 and 4 forecast. Our results also demonstrate that 10-yr reforecasts are an optimal training period. This is particularly beneficial considering limited computing resources.
Abstract
The National Centers for Environmental Prediction have generated an 18-yr (1999–2016) subseasonal (weeks 3 and 4) reforecast to support the Climate Prediction Center’s operational mission. To create this reforecast, the subseasonal experiment version of the GEFS was run every Wednesday, initialized at 0000 UTC with 11 members. The Climate Forecast System Reanalysis (CFSR) and Global Data Assimilation System (GDAS) served as the initial analyses for 1999–2010 and 2011–16, respectively. The analysis of 2-m temperature error demonstrates that the model has a strong warm bias over the Northern Hemisphere (NH) and North America (NA) during the warm season. During the boreal winter, the 2-m temperature errors over NA exhibit large interannual and intraseasonal variability. For NA and the NH, weeks 3 and 4 errors are mostly saturated, with initial conditions having a negligible impact. Week 2 errors (day 11) are ~88.6% and 86.6% of their saturated levels, respectively. The 1999–2015 reforecast biases were used to calibrate the 2-m temperature forecasts in 2016, which reduces (increases) the systematic error (forecast skill) for NA, the NH, the Southern Hemisphere, and the tropics, with a maximum benefit for NA during the warm season. Overall, analysis adjustment for the CFSR period makes bias characteristics more consistent with the GDAS period over the NH and tropics and substantially improves the corresponding forecast skill levels. The calibration of the forecast using week 2 bias provides similar skill to using weeks 3 and 4 bias, promising the feasibility of using week 2 bias to calibrate the weeks 3 and 4 forecast. Our results also demonstrate that 10-yr reforecasts are an optimal training period. This is particularly beneficial considering limited computing resources.
