Abstract

Model calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash–Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among subbasins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments.

Abstract

After the quick decaying of the 2015 super El Niño, the predicted La Niña unexpectedly failed to materialize to the anticipated standard in 2016. Diagnostic analyses, as well as numerical experiments, showed that this ENSO evolution of the 2015 super El Niño and the hindered 2016 La Niña may be essentially caused by sea surface temperature anomalies (SSTAs) in the subtropical Pacific. The self-sustaining SSTAs in the subtropical Pacific tend to weaken the trade winds during boreal spring–summer, leading to anomalous westerlies along the equatorial region over a period of more than one season. Such long-lasting wind anomalies provide an essential requirement for ENSO formation, particularly before a positive Bjerknes feedback is thoroughly built up between the oceanic and atmospheric states. Besides the 2015 super El Niño and the hindered La Niña in 2016, there were several other El Niño and La Niña events that cannot be explained only by the oceanic heat content in the equatorial Pacific. However, the questions related to those eccentric El Niño and La Niña events can be well explained by suitable SSTAs in the subtropical Pacific. Thus, the leading SSTAs in the subtropical Pacific can be treated as an independent indicator for ENSO prediction, on the basis of the oceanic heat content inherent in the equatorial region. Because ENSO events have become more uncertain under the background of global warming and the Pacific decadal oscillation during recent decades, thorough investigation of the role of the subtropical Pacific in ENSO formation is urgently needed.

Abstract

Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management.

Abstract

This paper is about the statistical correction of systematic errors in dynamical sea surface temperature (SST) prediction systems using linear regression approaches. The typically short histories of model forecasts create difficulties in developing regression-based corrections. The roles of sample size, predictive skill, and systematic error are examined in evaluating the benefit of a linear correction. It is found that with the typical 20 yr of available model SST forecast data, corrections are worth performing when there are substantial deviations in forecast amplitude from that determined by correlation with observations. The closer the amplitude of the uncorrected forecasts is to the optimum squared error-minimizing amplitude, the less likely is a correction to improve skill. In addition to there being less “room for improvement,” this rule is related to the expected degradation in out-of-sample skill caused by sampling error in the estimate of the regression coefficient underlying the correction.

Application of multivariate [canonical correlation analysis (CCA)] correction to three dynamical SST prediction models having 20 yr of data demonstrates improvement in the cross-validated skills of tropical Pacific SST forecasts through reduction of systematic errors in pattern structure. Additional beneficial correction of errors orthogonal to the CCA modes is achieved on a per-gridpoint basis for features having smaller spatial scale. Until such time that dynamical models become freer of systematic errors, statistical corrections such as those shown here can make dynamical SST predictions more skillful, retaining their nonlinear physics while also calibrating their outputs to more closely match observations.

Abstract

An empirical model for the temperature of subsurface water entrained into the ocean mixed layer (T_e ) is presented and evaluated to improve sea surface temperature anomaly (SSTA) simulations in an intermediate ocean model (IOM) of the tropical Pacific. An inverse modeling approach is adopted to estimate T_e from an SSTA equation using observed SST and simulated upper-ocean currents. A relationship between T_e and sea surface height (SSH) anomalies is then obtained by utilizing a singular value decomposition (SVD) of their covariance. This empirical scheme is able to better parameterize T_e anomalies than other local schemes and quite realistically depicts interannual variability of T_e , including a nonlocal phase lag relation of T_e variations relative to SSH anomalies over the central equatorial Pacific. An improved T_e parameterization naturally leads to better depiction of the subsurface effect on SST variability by the mean upwelling of subsurface temperature anomalies. As a result, SSTA simulations are significantly improved in the equatorial Pacific; a comparison with other schemes indicates that systematic errors of the simulated SSTAs are significantly small—apparently due to the optimized empirical T_e parameterization. Cross validation and comparisons with other model simulations are made to illustrate the robustness and effectiveness of the scheme. In particular it is demonstrated that the empirical T_e model constructed from one historical period can be successfully used to improve SSTA simulations in another.

Abstract

Freshwater flux (FWF) forcing–induced feedback has not been represented adequately in many coupled ocean–atmosphere models of the tropical Pacific. Previously, various approximations have been made in representing the FWF forcing in climate modeling. In this article, using a hybrid coupled model (HCM), sensitivity experiments are performed to examine the extent to which this forcing and related feedback effects can contribute to tropical biases in interannual simulations of the tropical Pacific. The total FWF into the ocean, represented by precipitation (P) minus evaporation (E), (P − E), is separated into its climatological part and interannual anomaly part: FWF_Total = (P − E)_clim + FWF_inter. The former can be prescribed (seasonally varying); the latter can be captured using an empirical model linking with large-scale sea surface temperature (SST) variability. Four cases are considered with different FWF_inter specifications: interannual (P − E) forcing [FWF_inter = (P − E)_inter], interannual P forcing (FWF_inter = P _inter), interannual E forcing (FWF_inter = −E _inter), and climatological (P − E) forcing (FWF_inter = 0.0), respectively. The HCM-based experiments indicate that different FWF_inter approximations can modulate interannual variability in a substantial way. The HCM with the interannual (P − E) forcing, in which a positive SST − (P − E)_inter feedback is included explicitly, has a reasonably realistic simulation of interannual variability. When FWF_inter is approximated in some ways, the simulated interannual variability can be modulated significantly: it is weakened with the climatological (P − E) forcing and is even more damped with the interannual E forcing, but is exaggerated with the interannual P forcing. Quantitatively, taking the interannual (P − E) forcing run as a reference, the Niño-3 SST variance can be reduced by about 12% and 26% in the climatological (P − E) forcing run and interannual E forcing run, respectively, but overestimated by 11% in the P _inter forcing run. It is demonstrated that FWF can be a clear bias source for coupled model simulations in the tropical Pacific.

Abstract

A reduced-gravity, primitive-equation, upper-ocean general circulation model is used to study the mean water pathways in the North Pacific subtropical and tropical ocean. The model features an explicit physical representation of the surface mixed layer, realistic basin geometry, observed wind and heat flux forcing, and a horizontal grid-stretching technique and a vertical sigma coordinate to obtain a realistic simulation of the subtropical/tropical circulation. Velocity fields, and isopycnal and trajectory analyses are used to understand the mean flow of mixed layer and thermocline waters between the subtropics and Tropics.

Subtropical/tropical water pathways are not simply direct meridional routes; the existence of vigorous zonal current systems obviously complicates the picture. In the surface mixed layer, upwelled equatorial waters flow into the subtropical gyre mainly through the midlatitude western boundary current (the model Kuroshio). There is additionally an interior ocean pathway, through the Subtropical Countercurrent (an eastward flow across the middle of the subtropical gyre), that directly feeds subtropical subduction sites. Below the mixed layer, the water pathways in the subtropical thermocline essentially reflect the anticyclonic gyre circulation where we find that the model subtropical gyre separates into two circulation centers. The surface circulation also features a double-cell pattern, with the poleward cell centered at about 30°N and the equatorward component contained between 15° and 25°N. In addition, thermocline waters that can be traced to subtropical subduction sites move toward the Tropics almost zonally across the basin, succeeding in flowing toward the equator only along relatively narrow north–south conduits. The low-latitude western boundary currents serve as the main southward circuit for the subducted subtropical thermocline water. However, the model does find a direct flow of thermocline water into the Tropics through the ocean interior, confined to the far western Pacific (away from the low-latitude western boundary currents) across 10°N. This interior pathway is found just to the west of a recirculating gyre in and just below the mixed layer in the northeastern Tropics. This equatorward interior flow and a flow that can be traced directly to the western boundary are then swept eastward by the deeper branches of the North Equatorial Countercurrent, finally penetrating to the equator in the central and eastern Pacific. Most of these results are consistent with available observations and recently published theoretical and idealized numerical experiments, although the interior pathway of subtropical thermocline water into the Tropics found in this experiment is not apparent in other published numerical simulations.

Potential vorticity dynamics are useful in explaining the pathways taken by subtropical thermocline water as it flows into the Tropics. In particular, a large-scale zonally oriented “island” of homogenous potential vorticity, whose signature is determined by thin isopycnal layers in the central tropical Pacific along about 10°N, is dynamically linked to a circulation that does not flow directly from the subtropics to the Tropics. This large-scale potential vorticity feature helps to explain the circuitous pathways of the subducted subtropical thermocline waters as they approach the equator. Consequently, waters must first flow westward to the western boundary north of these closed potential vorticity contours and then mostly move southward through the low-latitude western boundary currents, flow eastward with the North Equatorial Countercurrent, and finally equatorward to join the Equatorial Undercurrent in the thermocline.

Abstract

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parameter estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Overall, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.

Abstract

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, an adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.