Toward an Optimal Observational Array for Improving Two Flavors of El Niño Predictions in the Whole Pacific

This paper investigates the optimal observational array for improving the prediction of the El Niño-Southern Oscillation (ENSO) by exploring sensitive areas for target observations of two types of El Niño events in the whole Pacific. A target observation method based on the particle filter and pre-industrial control runs from six coupled model outputs in Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments are used to quantify the relative importance of the initial accuracy of sea surface temperature (SST) in different Pacific areas. The initial accuracy of the tropical Pacific, subtropical Pacific, and extratropical Pacific can all exert influences on both types of El Niño predictions. The relative importance of different areas changes along with different lead times of predictions. Tropical Pacific observations are crucial in decreasing the root mean square error of predictions of all lead times. Subtropical and extratropical observations play an important role in decreasing the prediction uncertainty, especially when the prediction is made before and throughout boreal spring. To consider different El Niño types and different start months for predictions, a quantitative frequency method based on frequency distribution is applied to determine the optimal observations of ENSO predictions. The final optimal observational array contains 31 grid points, including 21 grid points in the equatorial Pacific and 10 grid points in the north Pacific, suggesting the importance of the initial SST conditions for ENSO predictions not only in the tropical Pacific but also in the area outside the tropics. Furthermore, the predictions made by assimilating SST in sensitive areas have better prediction skills in the verification experiment, which can indicate the validity of the optimal observational array designed in this study.

Does disabling cloud radiative feedbacks change spatial patterns of surface greenhouse warming and cooling?

The processes controlling idealized warming and cooling patterns are examined in 150 year-long fully coupled Community Earth System Model version 1 (CESM1) experiments under abrupt CO2 forcing. By simulation end, 2xCO2 global warming was 20% larger than 0.5xCO2 global cooling. Not only was the absolute global effective radiative forcing ∼10% larger for 2xCO2 than for 0.5xCO2, global feedbacks were also less negative for 2xCO2 than for 0.5xCO2. Specifically, more positive shortwave cloud feedbacks led to more 2xCO2 global warming than 0.5xCO2 global cooling. Over high latitude oceans, differences between 2xCO2 warming and 0.5xCO2 cooling were amplified by familiar linked positive surface albedo and lapse rate feedbacks associated with sea ice change. At low latitudes, 2xCO2 warming exceeded 0.5xCO2 cooling almost everywhere. Tropical Pacific cloud feedbacks amplified: 1) more fast warming than fast cooling in the west, 2) slow pattern differences between 2xCO2 warming and 0.5xCO2 cooling in the east. Motivated to quantify cloud influence, a companion suite of experiments were run without cloud radiative feedbacks. Disabling cloud radiative feedbacks reduced the effective radiative forcing and surface temperature responses for both 2xCO2 and 0.5xCO2. Notably, 20% more global warming than global cooling occurred regardless of whether cloud feedbacks were enabled or disabled. This surprising consistency resulted from the cloud influence on non-cloud feedbacks and circulation. With the exception of the Tropical Pacific, disabling cloud feedbacks did little to change surface temperature response patterns including the large high-latitude responses driven by non-cloud feedbacks. The findings provide new insights into the regional processes controlling the response to greenhouse gas forcing, especially for clouds.

The dependence of mean climate state on shortwave absorption by water vapor

State-of-the-art climate models exhibit significant spread in the climatological value of atmospheric shortwave absorption (SWA). This study investigates both the possible causes and climatic impacts of this SWA inter-model spread. The inter-model spread of global-mean SWA largely originates from the inter-model difference in water vapor shortwave absorptivity. Hence, we alter the water vapor shortwave absorptivity in the Community Earth System Model, version 1, with Atmosphere Model, version 4 (CESM1-CAM4). Increasing the water vapor shortwave absorptivity leads to a reduction in global-mean precipitation and a La Niña-like cooling over the tropical Pacific. The global-mean atmospheric energy budget suggests that the precipitation is suppressed as a way to compensate for the increased SWA. The precipitation reduction is driven by the weakened surface winds, stabilized planetary boundary layer, and surface cooling. The La Niña-like cooling over the tropical Pacific is attributed to the zonal asymmetry of climatological evaporative damping efficiency and the low cloud enhancement over the eastern basin. Complementary fixed SSTs simulations suggest that the latter is more fundamental and that it primarily arises from atmospheric processes. Consistent with our experiments, the CMIP5/6 models with a higher global-mean SWA tend to exhibit the tropical Pacific toward a more La Niña-like mean state, highlighting the possible role of water vapor shortwave absorptivity for shaping the mean-state climate patterns.

A Dynamic Statistical Subseasonal Forecast Model for OLR Over Tropical Pacific Region

How to improve the subseasonal forecast skills of dynamic models has always been an important issue in atmospheric science and service. This study proposes a new dynamical-statistical forecast method and a stable components dynamic statistical forecast (STsDSF) for subseasonal outgoing long-wave radiation (OLR) over the tropical Pacific region in January-February from 2004 to 2008. Compared with 11 advanced multi-model ensemble (MME) daily forecasts, the STsDSF model was able to capture the change characteristics of OLR better when the lead time was beyond 30 days in 2005 and 2006. The average pattern correlation coefficients (PCC) of STsDSF are 0.24 and 0.16 in 2005 and 2006, while MME is 0.10 and 0.05, respectively. In addition, the average value of PCC of the STsDSF model in five years is higher than MME in 7–11 pentads. Although both the STsDSF model and MME show a similar temporal correlation coefficient (TCC) pattern over the tropical Pacific region, the STsDSF model error grows more slowly than the MME error during 8–12 pentads in January 2005. This phenomenon demonstrates that STsDSF can reduce dynamical model error in some situations. According to the comparison of subseasonal forecasts between STsDSF and MME in five years, STsDSF model skill depends strictly on the predictability of the dynamical model. The STsDSF model shows some advantages when the dynamical model could not forecast well above a certain level. In this study, the STsDSF model can be used as an effective reference for subseasonal forecast and could feasibly be used in real-time forecast business in the future.

Improving statistical prediction and revealing nonlinearity of ENSO using observations of ocean heat content in the tropical Pacific

It is well-known that the upper ocean heat content (OHC) variability in the tropical Pacific contains valuable information about dynamics of El Niño–Southern Oscillation (ENSO). Here we combine sea surface temperature (SST) and OHC indices derived from the gridded datasets to construct a phase space for data-driven ENSO models. Using a Bayesian optimization method, we construct linear as well as nonlinear models for these indices. We find that the joint SST-OHC optimal models yield significant benefits in predicting both the SST and OHC as compared with the separate SST or OHC models. It is shown that these models substantially reduces seasonal predictability barriers in each variable – the spring barrier in the SST index and the winter barrier in the OHC index. We also reveal the significant nonlinear relationships between the ENSO variables manifesting on interannual scales, which opens prospects for improving yearly ENSO forecasting.

Oceanic Rossby waves drive inter-annual predictability of net primary production in the central tropical Pacific

In the Pacific Ocean, off-equatorial Rossby waves, initiated by atmosphere-ocean interaction, modulate the inter-annual variability of the thermocline. In this study, we explore the resulting potential gain in predictability of central tropical Pacific primary production, which in this region strongly depends on the supply of macronutrients from below the thermocline. We use a decadal prediction system based on the Max Planck Institute Earth system model (MPI-ESM) to demonstrate that for the time period 1998-2014 properly initialized Rossby waves explain an increase in predictability of net primary productivity in the off-equatorial central tropical Pacific. We show that, for up to 5 years in advance, predictability of net primary productivity derived from the decadal prediction system is significantly larger than that derived from persistence alone, or an uninitialized historical simulation. The predicted signal can be explained by the following mechanism: off-equatorial Rossby waves are initiated in the eastern Pacific and travel towards the central tropical Pacific on a time scale of 2 to 6 years. On their arrival the Rossby waves modify the depths of both thermocline and nutricline, which is fundamental to the availability of nutrients in the euphotic layer. Local upwelling transports nutrients from below the nutricline into the euphotic zone, effectively transferring the Rossby wave signal to the near-surface ocean. While we show that skillful prediction of central off-equatorial tropical Pacific net primary productivity is possible, we open the door for establishing predictive systems for food web and ecosystem services in that region.