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Qian Zhou
,
Lei Chen
,
Wansuo Duan
,
Xu Wang
,
Ziqing Zu
,
Xiang Li
,
Shouwen Zhang
, and
Yunfei Zhang

Abstract

Using the latest operational version of the ENSO forecast system from the National Marine Environmental Forecasting Center (NMEFC) of China, ensemble forecasting experiments are performed for El Niño–Southern Oscillation (ENSO) events that occurred from 1997 to 2017 by generating initial perturbations of the conditional nonlinear optimal perturbation (CNOP) and climatically relevant singular vector (CSV) structures. It is shown that when the initial perturbation of the leading CSV structure in the ensemble forecast of the CSVs scheme is replaced by those of the CNOP structure, the resulted ensemble ENSO forecasts of the CNOP+CSVs scheme tend to possess a larger spread than the forecasts obtained with the CSVs scheme alone, leading to a better match between the root-mean-square error and the ensemble spread, a more reasonable Talagrand diagram, and an improved Brier skill score (BSS). All these results indicate that the ensemble forecasts generated by the CNOP+CSVs scheme can improve both the accuracy of ENSO forecasting and the reliability of the ensemble forecasting system. Therefore, ENSO ensemble forecasting should consider the effect of nonlinearity on the ensemble initial perturbations to achieve a much higher skill. It is expected that fully nonlinear ensemble initial perturbations can be sufficiently yielded to produce ensemble forecasts for ENSO, finally improving the ENSO forecast skill to the greatest possible extent. The CNOP will be a useful method to yield fully nonlinear optimal initial perturbations for ensemble forecasting.

Open access
Xuejin Wang
,
Baoqing Zhang
,
Feng Li
,
Xiang Li
,
Xuliang Li
,
Yibo Wang
,
Rui Shao
,
Jie Tian
, and
Chansheng He

Abstract

From 1998 to the present, the Chinese government has implemented numerous large-scale ecological programs to restore ecosystems and improve environmental protection in the agro-pastoral ecotone of northern China (APENC). However, it remains unclear how vegetation restoration modulates intraregional moisture cycles and changes regional water balance. To fill this gap, we first investigated the variation in precipitation (P) from the China Meteorological Forcing Dataset and evapotranspiration (ET) estimated using the Priestley–Taylor Jet Propulsion Laboratory model under two scenarios: dynamic vegetation (DV) and no dynamic vegetation (no-DV). We then used the dynamic recycling model to analyze the changes in precipitation recycling ratio (PRR). Finally, we examined how vegetation restoration modulates intraregional moisture recycling to change the regional water cycle in APENC. Results indicate P increased at an average rate of 4.42 mm yr−2 from 1995 to 2015. ET with DV exhibited a significant increase at a rate of 1.57, 3.58, 1.53, and 1.84 mm yr−2 in the four subregions, respectively, compared with no-DV, and the annual mean PRR values were 10.15%, 9.30%, 11.01%, and 12.76% in the four subregions, and significant increasing trends were found in the APENC during 1995–2015. Further analysis of regional moisture recycling shows that vegetation restoration does not increase local P directly, but has an indirect effect by enhancing moisture recycling process to produce more P by increasing PRR. Our findings show that large-scale ecological restoration programs have a positive effect on local moisture cycle and precipitation.

Full access
Yuntao Wei
,
Hong-Li Ren
,
Baoqiang Xiang
,
Yan Wang
,
Jie Wu
, and
Shuguang Wang

Abstract

The Madden–Julian oscillation (MJO) is the dominant intraseasonal wave phenomenon influencing extreme weather and climate worldwide. Realistic simulations and accurate predictions of MJO genesis are the cornerstones for successfully monitoring, forecasting, and managing meteorological disasters 3–4 weeks in advance. Nevertheless, the genesis processes and emerging precursor signals of an eastward-propagating MJO event remain largely uncertain. Here, we find that the MJO genesis processes observed in the past four decades exhibit remarkable diversity with different seasonality and can be classified objectively into four types, namely, a novel downstream origin from the westward-propagating intraseasonal oscillation (WPISO; 20.4%), localized breeding from the Indian Ocean suppressed convection (IOSC; 15.4%), an upstream succession of the preceding weakly dispersive (WD; 25.9%), and strongly dispersive (SD; 38.3%) MJO. These four types are associated with different oceanic background states, characterized by central Pacific cooling, southern Maritime Continent warming, eastern Pacific cooling, and central Pacific warming for the WPISO, IOSC, WD, and SD types, respectively. The SD type is also favored during the easterly phase of the stratospheric quasi-biennial oscillation. Diverse convective initiations possibly imply various kinds of propagations of MJO. The subseasonal reforecasts indicate robustly distinct prediction skills for the diverse MJO genesis. A window of opportunity for skillful week 3–4 prediction probably opens with the aid of the WPISO-type MJO precursor, which has increased the predictability of primary MJO onset by 1 week. These findings suggest that the diversified MJO genesis can be skillfully foreseen by monitoring unique precursor signals and can also serve as benchmarks for evaluating contemporary models’ modeling and predicting capabilities.

Open access
Yujun Wang
,
Hongbo Yang
,
Vanessa Hull
,
Jindong Zhang
,
Xiaodong Chen
,
Xiang Li
,
Zejun Zhang
,
Cheng Li
,
Fang Wang
,
Zhiqiang Zhao
,
Ying Tang
, and
Jianguo Liu

Abstract

The effects of various strategies aimed at simultaneously promoting environmental conservation and human development are closely related to sustainable development regionally and globally. However, although the effects of many such strategies have been evaluated by ecologists and sociologists separately, their ability to simultaneously meet these two anticipated goals (i.e., environmental conservation and human development) at the fine spatial scale remains unclear. To answer this fundamental but crucial question, incorporating household and forest change data, we concurrently estimated the ecological and socioeconomic effects of two world-renowned Payment for Ecosystem Services (PES) programs (i.e., the Nature Forest Conservation Program, the Grain to Green Program) and nature-based tourism in 30 protected areas across 8 provinces in China. Here we showed a trade-off between the ecological and economic effects of two PES programs, while synergistic effects exist in the ecological and economic benefits of tourism. Attributes of household and protected areas significantly influenced economic and environmental benefits as well. Our research provides new insights into the complex effects of PES programs and tourism, and crucial information to support their adequate and sustainable implementation in China and the rest of the world.

Significance Statement

This work answers a fundamental but crucial question, that is, whether the policies commonly advocated to incorporate environmental conservation and human development can yield positive effects both for conservation and economic development. Our evaluation is also timely to inform some shortness (i.e., negligible economic effects, or the lack of expected positive economic benefits) and provides new insights (e.g., the implication of households and protected-areas attributes in conservation and economic outcomes) of Payment for Ecosystem Services (PES) programs and the complex effects of instruments in the context of multiple policies, particularly given the upcoming 2030 deadline for achieving the Sustainable Development goals (SDGs). We expected that implications in this study can provide important lessons for these two instruments, other PES programs, and other conservation and development instruments to support their adequate and sustainable implementation in China and beyond and to contribute to the achievement of relevant SDGs in the remaining years.

Open access
Xiang Li
,
Dongliang Yuan
,
Zheng Wang
,
Yao Li
,
Corry Corvianawatie
,
Dewi Surinati
,
Asep Sandra
,
Ahmad Bayhaqi
,
Praditya Avianto
,
Edi Kusmanto
,
Dirham Dirhamsyah
, and
Zainal Arifin

Abstract

The ocean currents in the Halmahera Sea are studied using a subsurface mooring deployed in the Jailolo Strait from November 2015 to October 2017. The subtidal currents of the mooring measurements are characterized by a two-layer system, with the current variability below about 200 m in opposite phases to that in the upper layer. The mean along-strait velocity (ASV) is toward the Indonesian seas in the whole water column, producing an estimated mean transport of 2.44 ± 0.42 Sv (1 Sv ≡ 106 m3 s−1). The errors of the transport calculation based on the single mooring measurements are estimated to be less than 15% using simulations of high-resolution ocean models. A weak current is observed to flow northward during 2017 at the bottom of the strait. The ASV variability is found to be dominated by an annual cycle both in the upper and lower layers. The total transport, however, is dominated by semiannual variability because of the cancelation of the annual transports in the upper and lower layers. The variability of the transport is suggested to be driven by the pressure difference between the Pacific Ocean and the Indonesian seas, as evidenced by the agreement between the satellite pressure gradient and the two-layer transports. The transport of the Jailolo Strait during the 2015/16 super El Niño is found to be nearly the same as that during the 2016 La Niña, suggesting that the interannual variability of the transport is much smaller than the seasonal cycle.

Free access
Shuwen Tan
,
Larry J. Pratt
,
Dongliang Yuan
,
Xiang Li
,
Zheng Wang
,
Yao Li
,
Corry Corvianawatie
,
Dewi Surinati
,
Asep S. Budiman
, and
Ahmad Bayhaqi

Abstract

Hydrographic measurements recently acquired along the thalweg of the Lifamatola Passage combined with historical moored velocity measurements immediately downstream of the sill are used to study the hydraulics, transport, mixing, and entrainment in the dense overflow. The observations suggest that the mean overflow is nearly critical at the mooring site, suggesting that a weir formula may be appropriate for estimating the overflow transport. Our assessment suggests that the weir formulas corresponding to a rectangular, triangular, or parabolic cross section all result in transports very close to the observation, suggesting their potential usage in long-term monitoring of the overflow transport or parameterizing the transport in numerical models. Analyses also suggest that deep signals within the overflow layer are blocked by the shear flow from propagating upstream, whereas the shallow wave modes of the full-depth continuously stratified flow are able to propagate upstream from the Banda Sea into the Maluku Sea. Strong mixing is found immediately downstream of the sill crest, with Thorpe-scale-based estimates of the mean dissipation rate within the overflow up to 1.1 × 10−7 W kg−1 and the region-averaged diapycnal diffusivity within the downstream overflow in the range of 2.3 × 10−3 to 10.1 × 10−3 m2 s−1. Mixing in the Lifamatola Passage results in 0.6–1.2-Sv (1 Sv ≡ 106 m3 s−1) entrainment transport added to the overflow, enhancing the deep-water renewal in the Banda Sea. A bulk diffusivity coefficient estimated in the deep Banda Sea yields 1.6 × 10−3 ± 5 × 10−4 m2 s−1, with an associated downward turbulent heat flux of 9 W m−2.

Free access
Baoqiang Xiang
,
Ming Zhao
,
Xianan Jiang
,
Shian-Jiann Lin
,
Tim Li
,
Xiouhua Fu
, and
Gabriel Vecchi

Abstract

Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden–Julian oscillation (MJO) prediction skill in boreal wintertime (November–April) is evaluated by analyzing 11 years (2003–13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.

Full access
Steven M. Lazarus
,
Michael E. Splitt
,
Michael D. Lueken
,
Rahul Ramachandran
,
Xiang Li
,
Sunil Movva
,
Sara J. Graves
, and
Bradley T. Zavodsky

Abstract

Data reduction tools are developed and evaluated using a data analysis framework. Simple (nonadaptive) and intelligent (adaptive) thinning algorithms are applied to both synthetic and real data and the thinned datasets are ingested into an analysis system. The approach is motivated by the desire to better represent high-impact weather features (e.g., fronts, jets, cyclones, etc.) that are often poorly resolved in coarse-resolution forecast models and to efficiently generate a set of initial conditions that best describes the current state of the atmosphere. As a precursor to real-data applications, the algorithms are applied to one- and two-dimensional synthetic datasets. Information gleaned from the synthetic experiments is used to create a thinning algorithm that combines the best aspects of the intelligent methods (i.e., their ability to detect regions of interest) while reducing the impacts of spatial irregularities in the data. Both simple and intelligent thinning algorithms are then applied to Atmospheric Infrared Sounder (AIRS) temperature and moisture profiles. For a given retention rate, background, and observation error, the optimal 1D analyses (i.e., lowest MSE) tend to have observations that are near regions of large curvature and gradients. Observation error leads to the selection of spurious data in homogeneous regions of the intelligent algorithms. In the 2D experiments, simple thinning tends to perform better within the homogeneous data regions. Analyses produced using AIRS data demonstrate that observations selected via a combination of the simple and intelligent approaches reduce clustering, provide a more even distribution along the satellite swath edges, and, in general, have lower error and comparable computational requirements compared to standard operational thinning methodologies.

Full access
Sundar A. Christopher
,
Xiang Li
,
Ronald M. Welch
,
Jeffrey S. Reid
,
Peter V. Hobbs
,
Thomas F. Eck
, and
Brent Holben

Abstract

Using in situ measurements of aerosol optical properties and ground-based measurements of aerosol optical thickness (τ s ) during the Smoke, Clouds and Radiation—Brazil (SCAR-B) experiment, a four-stream broadband radiative transfer model is used to estimate the downward shortwave irradiance (DSWI) and top-of-atmosphere (TOA) shortwave aerosol radiative forcing (SWARF) in cloud-free regions dominated by smoke from biomass burning in Brazil. The calculated DSWI values are compared with broadband pyranometer measurements made at the surface. The results show that, for two days when near-coincident measurements of single-scattering albedo ω 0 and τ s are available, the root-mean-square errors between the measured and calculated DSWI for daytime data are within 30 W m−2. For five days during SCAR-B, however, when assumptions about ω 0 have to be made and also when τ s was significantly higher, the differences can be as large as 100 W m−2. At TOA, the SWARF per unit optical thickness ranges from −20 to −60 W m−2 over four major ecosystems in South America. The results show that τ s and ω 0 are the two most important parameters that affect DSWI calculations. For SWARF values, surface albedos also play an important role. It is shown that ω 0 must be known within 0.05 and τ s at 0.55 μm must be known to within 0.1 to estimate DSWI to within 20 W m−2. The methodology described in this paper could serve as a potential strategy for determining DSWI values in the presence of aerosols. The wavelength dependence of τ s and ω 0 over the entire shortwave spectrum is needed to improve radiative transfer calculations. If global retrievals of DSWI and SWARF from satellite measurements are to be performed in the presence of biomass-burning aerosols on a routine basis, a concerted effort should be made to develop methodologies for estimating ω 0 and τ s from satellite and ground-based measurements.

Full access
Shuyun Feng
,
Xihui Gu
,
Sijia Luo
,
Ruihan Liu
,
Aminjon Gulakhmadov
,
Louise J. Slater
,
Jianfeng Li
,
Xiang Zhang
, and
Dongdong Kong

Abstract

Drylands play an essential role in Earth’s environment and human systems. Although dryland expansion has been widely investigated in previous studies, there is a lack of quantitative evidence supporting human-induced changes in dryland extent. Here, using multiple observational datasets and model simulations from phase 6 of the Coupled Model Intercomparison Project, we employ both correlation-based and optimal fingerprinting approaches to conduct quantitative detection and attribution of dryland expansion. Our results show that spatial changes in atmospheric aridity (i.e., the aridity index defined by the ratio of precipitation to potential evapotranspiration) between the recent period 1990–2014 and the past period 1950–74 are unlikely to have been caused by greenhouse gas (GHG) emissions. However, it is very likely (at least 95% confidence level) that dryland expansion at the global scale was driven principally by GHG emissions. Over the period 1950–2014, global drylands expanded by 3.67% according to observations, and the dryland expansion attributed to GHG emissions is estimated as ∼4.5%. Drylands are projected to continue expanding, and their populations to increase until global warming reaches ∼3.5°C above preindustrial temperature under the middle- and high-emission scenarios. If warming exceeds ∼3.5°C, a reduction in population density would drive a decrease in dryland population. Our results for the first time provide quantitative evidence for the dominant effects of GHG emissions on global dryland expansion, which is helpful for anthropogenic climate change adaptation in drylands.

Significance Statement

In the past decades, global drylands have been reported to show changes in space and time, based on atmospheric aridity (i.e., aridity index defined by the ratio of precipitation to potential evapotranspiration). Using two detection and attribution methods, the spatial change patterns of atmospheric aridity between 1990–2014 and 1950–74 are unlikely to be driven by greenhouse gas (GHG) emissions, whereas the temporal expansion of global drylands (i.e., 3.67% from 1950 to 2014) is principally attributed to GHG emissions (contribution: ∼122%). Quantitative evidence from the detection and attribution analysis supports the dominant role of greenhouse gas emissions in global dryland expansion, which will increase the population suffering from water shortages under future warming unless climate adaptation is adopted.

Free access