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Zhengyu Liu, M. Notaro, J. Kutzbach, and Naizhuang Liu

Abstract

The feedback between global vegetation greenness and surface air temperature and precipitation is assessed using remote sensing observations of monthly fraction of photosynthetically active radiation (FPAR) for 1982 to 2000 with a 2.5° grid resolution. Lead/lag correlations are used to infer vegetation–climate interactions. Furthermore, a statistical method is used to quantify the efficiency of vegetation feedback on climate in the observations. This feedback analysis provides a first quantitative assessment of global vegetation feedback on climate. In northern mid- and high latitudes, vegetation variability is found to be driven predominantly by temperature; in the meantime, vegetation also exerts a strong positive feedback on temperature with the feedback accounting for over 10%–25% of the total monthly temperature variance. The strongest positive feedback occurs in the boreal regions of southern Canada/northern United States, northern Europe, and southern Siberia, where the feedback efficiency exceeds 1°C (0.1 FPAR)−1. Over most of the Tropics and subtropics (outside the equatorial rain belt), vegetation is driven primarily by precipitation. However, little vegetation feedback is found on local precipitation when averaged year-round, with the feedback explained variance usually accounting for less than 5% of the total precipitation variance. Nevertheless, in a few isolated small regions such as Northeast Brazil, East Africa, East Asia, and northern Australia, there appears to be some positive vegetation feedback on local precipitation, with the feedback efficiency over 1 cm month−1 (0.1 FPAR)−1. Further studies suggest a significant seasonal variation of the vegetation feedback in some regions. A preliminary analysis also seems to suggest an enhanced intensity of the vegetation feedback, especially on precipitation, at longer time scales and over a larger grid box area. Limitations and implications of the assessment of vegetation feedback are also discussed. The assessed vegetation feedback is shown to be valuable for the evaluation of vegetation–climate feedback in coupled climate–vegetation models.

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Guang-Shan Chen, Michael Notaro, Zhengyu Liu, and Yongqiang Liu

Abstract

Afforestation has been proposed as a climate change mitigation strategy by sequestrating atmospheric carbon dioxide. With the goal of increasing carbon sequestration, a Congressional project has been planned to afforest about 18 million acres by 2020 in the Southeast United States (SEUS), the Great Lake states, and the Corn Belt states. However, biophysical feedbacks of afforestation have the potential to counter the beneficial climatic consequences of carbon sequestration. To assess the potential biophysical effects of afforestation over the SEUS, the authors designed a set of initial value ensemble experiments and long-term quasi-equilibrium experiments in a fully coupled Community Climate System Model, version 3.5 (CCSM3.5). Model results show that afforestation over the SEUS not only has a local cooling effect in boreal summer [June–August (JJA)] at short and long time scales but also induces remote warming over adjacent regions of the SEUS at long time scales. Precipitation, in response to afforestation, increases over the SEUS (local effect) and decreases over adjacent regions (remote effect) in JJA. The local surface cooling and increase in precipitation over SEUS in JJA are hydrologically driven by the changes in evapotranspiration and latent heat flux. The remote surface warming and decrease in precipitation over adjacent regions are adiabatically induced by anomalous subsidence. Our results suggest that the planned afforestation efforts should be developed carefully by taking account of short-term (local) and long-term (remote) biophysical effects of afforestation.

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Zhengyu Liu, Joseph Pedlosky, David Marshall, and Tornster Warncke

Abstract

The model developed by Pedlosky and Young is used to investigate the feedback of a Rhines–Young pool on a ventilated thermocline. It is found that the potential vorticity gradient in a ventilated layer is reduced due to the nonlinear coupling with a deep Rhines–Young pool. Physically, this occurs because part of the Sverdrup transport is carried by the deep pool. As a result the subduction velocity, and in turn, the potential vorticity gradient of the subducted water, is decreased.

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Zhihong Jiang, Hao Yang, Zhengyu Liu, Yanzhu Wu, and Na Wen

Abstract

This study investigates the influence of different sea surface temperature (SST) modes on the winter temperature in China using the generalized equilibrium feedback assessment (GEFA). It is found that the second EOF mode of winter temperature in China during 1958–2010 shows a typical northeast–southwest (NE–SW) pattern, which is a major spatial mode of Chinese winter temperature at interannual scales. The winter temperature of the NE–SW pattern is forced mainly by SST modes in the tropical Pacific and Atlantic. For 2009/10, the tropical Pacific El Niño mode and tropical Atlantic tripole mode have the largest contribution to the response. The physical mechanism of the cold northeast–warm southwest (CNE–WSW) pattern is also explained in terms of GEFA of the responses of the atmospheric circulation. The northerly flow at the low level transports cold air to northern and northeastern China, resulting in a lower temperature there. Meanwhile, the anomaly meridional wind advects warm air from the southern oceans to southwestern China, leading to warming there.

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Fuyao Wang, Michael Notaro, Zhengyu Liu, and Guangshan Chen

Abstract

The observed local and nonlocal influences of vegetation on the atmosphere across North America are quantified after first removing the oceanic impact. The interaction between vegetation and the atmosphere is dominated by forcing from the atmosphere, making it difficult to extract the forcing from vegetation. Furthermore, the atmosphere is not only influenced by vegetation but also the oceans, so in order to extract the vegetation impact, the oceanic forcing must first be excluded. This study identified significant vegetation impact in two climatically and ecologically unique regions: the North American monsoon region (NAMR) and the North American boreal forest (NABF). A multivariate statistical method, a generalized equilibrium feedback assessment, is applied to extract vegetation influence on the atmosphere. The statistical method is validated using a dynamical experiment for the NAMR in a fully coupled climate model, the Community Climate System Model, version 3.5 (CCSM3.5).

The observed influence of NAMR vegetation on the atmosphere peaks in June–August and is primarily attributed to both roughness and hydrological feedbacks. Elevated vegetation amount increases evapotranspiration and surface roughness, which leads to a local decline in sea level pressure and generates an atmospheric teleconnection response. This atmospheric response leads to moister and cooler (drier and warmer) conditions over the western and central United States (Gulf states). The observed influence of the NABF on the atmosphere peaks in March–May, related to a thermal feedback. Enhanced vegetation greenness increases the air temperature locally. The atmosphere tends to form a positive Pacific–North American (PNA)-like pattern, and this anomalous atmospheric circulation and associated moisture advection lead to moister (drier) conditions in the western (eastern) United States.

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Xuefeng Zhang, Shaoqing Zhang, Zhengyu Liu, Xinrong Wu, and Guijun Han

Abstract

Imperfect physical parameterization schemes in a coupled climate model are an important source of model biases that adversely impact climate prediction. However, how observational information should be used to optimize physical parameterizations through parameter estimation has not been fully studied. Using an intermediate coupled ocean–atmosphere model, the authors investigate parameter optimization when the assimilation model contains biased physics within a biased assimilation experiment framework. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation model and the “truth” model that is used to generate simulated observations. While the stochastic physics, implemented by initially perturbing the physical parameters, can significantly enhance the ensemble spread and improve the representation of the model ensemble, the parameter estimation is able to mitigate the model biases induced by the biased physics. Furthermore, better results for climate estimation and prediction can be obtained when only the most influential physical parameters are optimized and allowed to vary geographically. In addition, the parameter optimization with the biased model physics improves the performance of the climate estimation and prediction in the deep ocean significantly, even if there is no direct observational constraint on the low-frequency component of the state variables. These results provide some insight into decadal predictions in a coupled ocean–atmosphere general circulation model that includes imperfect physical schemes that are initialized from the climate observing system.

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Zhengyu Liu, Haijun Yang, Chengfei He, and Yingying Zhao

Abstract

The response of the atmospheric energy (heat) transport (AHT) to a perturbation oceanic heat transport (OHT) is studied theoretically in a zonal mean energy balance model, with the focus on the effect of climate feedback, especially its spatial variation, on Bjerknes compensation (BJC). It is found that the BJC depends critically on climate feedback. For a stable climate, in which negative climate feedback is dominant, the AHT always compensates the OHT in the opposite direction. Furthermore, if local climate feedback is negative everywhere, the AHT will be weaker than the OHT (undercompensation) because of the damping on the surface oceanic heating through the top-of-atmosphere energy loss. One novel finding is that the compensation magnitude depends on the spatial scale of the forcing and is bounded between a minimum at the global scale and a maximum (of perfect compensation) at small scales. Most interestingly, the BJC is affected significantly by the spatial variation of the feedback, particularly a local positive climate feedback. As such, a regional positive feedback can lead to a compensating AHT greater than the perturbation OHT (overcompensation). This occurs because the positive feedback enhances the local temperature response, the anomalous temperature gradient, and, in turn, the AHT. Finally, the poleward latent heat transport leads to a temperature response with a polar amplification accompanied by a polar steepening of temperature gradient but does not change the BJC significantly. Potential applications of this BJC theory to more complex climate model studies are also discussed.

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Yishuai Jin, Zhengyu Liu, Chengfei He, and Yuchu Zhao

Abstract

The mechanism of the seasonal persistence barrier (SPB) is studied in the framework of an autoregressive (AR) model. In contrast to the seasonal variance, whose minimum is modulated mainly by the minimum growth rate or noise forcing, the SPB is caused primarily by the declining growth rate or increasing noise forcing, instead of the minimum/maximum of the growth rate or noise forcing. In other words, the SPB is caused by the declining signal-to-noise ratio (SNR) rather than the weakest SNR. In a weakly damped system, the phase of the SPB is delayed from that of declining SNR by about a season. The mechanism is further applied to explain the observed SST variability in the tropical and North Pacific. For the tropical Pacific, the spring SPB could be caused by the decreasing growth rate from September to March and weak annual mean damping rate, instead of the minimum growth rate in spring. Over the North Pacific, the increasing noise forcing from March to June may lead to the summer SPB. Our mechanism provides a null hypothesis for understanding the SPB of climate variability.

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Guijun Han, Xinrong Wu, Shaoqing Zhang, Zhengyu Liu, and Wei Li

Abstract

Coupled data assimilation uses a coupled model consisting of multiple time-scale media to extract information from observations that are available in one or more media. Because of the instantaneous exchanges of information among the coupled media, coupled data assimilation is expected to produce self-consistent and physically balanced coupled state estimates and optimal initialization for coupled model predictions. It is also expected that applying coupling error covariance between two media into observational adjustments in these media can provide direct observational impacts crossing the media and thereby improve the assimilation quality. However, because of the different time scales of variability in different media, accurately evaluating the error covariance between two variables residing in different media is usually very difficult. Using an ensemble filter together with a simple coupled model consisting of a Lorenz atmosphere and a pycnocline ocean model, which characterizes the interaction of multiple time-scale media in the climate system, the impact of the accuracy of coupling error covariance on the quality of coupled data assimilation is studied. Results show that it requires a large ensemble size to improve the assimilation quality by applying coupling error covariance in an ensemble coupled data assimilation system, and the poorly estimated coupling error covariance may otherwise degrade the assimilation quality. It is also found that a fast-varying medium has more difficulty being improved using observations in slow-varying media by applying coupling error covariance because the linear regression from the observational increment in slow-varying media has difficulty representing the high-frequency information of the fast-varying medium.

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Xiuhua Zhu, Klaus Fraedrich, Zhengyu Liu, and Richard Blender

Abstract

Climate forecast skills are evaluated for surface temperature time series at grid points of a millennium control simulation from a state-of-the-art global circulation model [ECHAM5–Max Planck Institute Ocean Model (MPI-OM)]. First, climate predictability is diagnosed in terms of potentially predictable variance fractions and the fluctuation power-law exponent (using detrended fluctuation analysis). Long-term memory (LTM) with a fluctuation exponent (or Hurst exponent) close to 0.9 occurs mainly in high-latitude oceans, which are also characterized by high potential predictability. Next, explicit prediction experiments for various time steps are conducted on a gridpoint basis using an autocorrelation predictor. In regions with LTM, prediction skills are beyond that expected from red noise persistence—exceptions occur in some areas in the southern oceans and over the Northern Hemisphere continents. Extending the predictability analysis to the fully forced simulation shows a large improvement in prediction skills.

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