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S. D. Schubert, Y. Chang, H. Wang, R. D. Koster, and A. M. Molod

Abstract

We outline a framework for identifying the geographical sources of biases in climate models. By forcing the model with time-averaged short-term analysis increments [tendency bias corrections (TBCs)] over well-defined regions, we can quantify how the associated reduced tendency errors in these regions manifest themselves both locally and remotely through large-scale teleconnections. Companion experiments in which the model is fully corrected [constrained to remain close to the analysis at each time step, termed replay (RPL)] in the various regions provide an upper bound to the local and remote TBC impacts. An example is given based on MERRA-2 and the NASA/GMAO GEOS AGCM used to generate MERRA-2. The results highlight the ability of the approach to isolate the geographical sources of some of the long-standing boreal summer biases of the GEOS model, including a stunted North Pacific summer jet, a dry bias in the U.S. Great Plains, and a warm bias over most of the Northern Hemisphere land. In particular, we show that the TBC over a region that encompasses Tibet has by far the largest impact (compared with all other regions) on the NH summer jets and related variables, leading to significant improvements in the simulation of North American temperature and, to a lesser degree, precipitation. It is further shown that the results of the regional TBC experiments are for the most part linear in the summer hemisphere, allowing a robust interpretation of the results.

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N. Freychet, S. F. B. Tett, G. C. Hegerl, and J. Wang

Abstract

Large-scale and persistent heat waves affecting central-eastern China are investigated in 40 different simulations of sea surface temperature driven global atmospheric models. The different models are compared with results from reanalysis and ground station datasets. It is found that the dynamics of heat-wave events is well reproduced by the models. However, they tend to produce too-persistent heat-wave events (lasting more than 20 days), and several hypotheses were tested to explain this bias. The daily variability of the temperatures or the seasonal signal did not explain the persistence. However, interannual variability of the temperatures in the models, and especially the sharp transition in the mid-1990s, has a large impact on the duration of heat waves. A filtering method was applied to select the models closest to the observations in terms of events persistence. The selected models do not show a significant difference from the other models for the long-term trends. Thus, the bias on the duration of the events does not impact the reliability of the model positive trends, which is mainly controlled by the changes in mean temperatures.

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Aihui Wang, Xubin Zeng, Samuel S. P. Shen, Qing-Cun Zeng, and Robert E. Dickinson

Abstract

This paper intends to investigate the time scales of land surface hydrology and enhance the understanding of the hydrological cycle between the atmosphere, vegetation, and soil. A three-layer model for land surface hydrology is developed to study the temporal variation and vertical structure of water reservoirs in the vegetation–soil system in response to precipitation forcing. The model is an extension of the existing one-layer bucket model. A new time scale is derived, and it better represents the response time scale of soil moisture in the root zone than the previously derived inherent time scale (i.e., the ratio of the field capacity to the potential evaporation). It is found that different water reservoirs of the vegetation–soil system have different time scales. Precipitation forcing is mainly concentrated on short time scales with small low-frequency components, but it can cause long time-scale disturbances in the soil moisture of root zone. This time scale increases with soil depth, but it can be reduced significantly under wetter conditions. Although the time scale of total water content in the vertical column in the three-layer model is similar to that of the one-layer bucket model, the time scale of evapotranspiration is very different. This suggests the need to consider the vertical structure in land surface hydrology reservoirs and in climate study.

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Gan Zhang, Zhuo Wang, Timothy J. Dunkerton, Melinda S. Peng, and Gudrun Magnusdottir

Abstract

With warm sea surface temperature (SST) anomalies in the tropical Atlantic and cold SST anomalies in the east Pacific, the unusually quiet hurricane season in 2013 was a surprise to the hurricane community. The authors’ analyses suggest that the substantially suppressed Atlantic tropical cyclone (TC) activity in August 2013 can be attributed to frequent breaking of midlatitude Rossby waves, which led to the equatorward intrusion of cold and dry extratropical air. The resultant mid- to upper-tropospheric dryness and strong vertical wind shear hindered TC development. Using the empirical orthogonal function analysis, the active Rossby wave breaking in August 2013 was found to be associated with a recurrent mode of the midlatitude jet stream over the North Atlantic, which represents the variability of the intensity and zonal extent of the jet. This mode is significantly correlated with Atlantic hurricane frequency. The correlation coefficient is comparable to the correlation of Atlantic hurricane frequency with the main development region (MDR) relative SST and higher than that with the Niño-3.4 index. This study highlights the extratropical impacts on Atlantic TC activity, which may have important implications for the seasonal predictability of Atlantic TCs.

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Xuguang Wang, Thomas M. Hamill, Jeffrey S. Whitaker, and Craig H. Bishop

Abstract

A hybrid ensemble transform Kalman filter (ETKF)–optimum interpolation (OI) analysis scheme is described and compared with an ensemble square root filter (EnSRF) analysis scheme. A two-layer primitive equation model was used under perfect-model assumptions. A simplified observation network was used, and the OI method utilized a static background error covariance constructed from a large inventory of historical forecast errors. The hybrid scheme updated the ensemble mean using a hybridized ensemble and static background-error covariance. The ensemble perturbations in the hybrid scheme were updated by the ETKF scheme. The EnSRF ran parallel data assimilation cycles for each member and serially assimilated the observations. The EnSRF background-error covariance was estimated fully from the ensemble.

For 50-member ensembles, the analyses from the hybrid scheme were as accurate or nearly as accurate as those from the EnSRF, depending on the norm. For 20-member ensembles, the analyses from the hybrid scheme were more accurate than analyses from the EnSRF under certain norms. Both hybrid and EnSRF analyses were more accurate than the analyses from the OI. Further reducing the ensemble size to five members, the EnSRF exhibited filter divergence, whereas the analyses from the hybrid scheme were still better than those updated by the OI. Additionally, the hybrid scheme was less prone to spurious gravity wave activity than the EnSRF, especially when the ensemble size was small. Maximal growth in the ETKF ensemble perturbation space exceeded that in the EnSRF ensemble perturbation space. The relationship of the ETKF ensemble variance to the analysis error variance, a measure of a spread–skill relationship, was similar to that of the EnSRF ensemble. The hybrid scheme can be implemented in a reasonably straightforward manner in the operational variational frameworks, and the computational cost of the hybrid is expected to be much less than the EnSRF in the operational settings.

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Ming Cai, Chul-Su Shin, H. M. van den Dool, Wanqiu Wang, S. Saha, and A. Kumar

Abstract

This paper analyzes long-term surface air temperature trends in a 25-yr (1982–2006) dataset of retrospective seasonal climate predictions made by the NCEP Climate Forecast System (CFS), a model that has its atmospheric greenhouse gases fixed at the 1988 concentration level. Although the CFS seasonal forecasts tend to follow the observed interannual variability very closely, there exists a noticeable time-dependent discrepancy between the CFS forecasts and observations, with a warm model bias before 1988 and a cold bias afterward except for a short-lived warm bias during 1992–94. The trend from warm to cold biases is likely caused by not including the observed increase in the anthropogenic greenhouse gases in the CFS, whereas the warm bias in 1992–94 reflects the absence of the anomalous aerosols released by the 1991 Mount Pinatubo eruption. Skill analysis of the CFS seasonal climate predictions with and without the warming trend suggests that the 1997–98 El Niño event contributes significantly to the record-breaking global warmth in 1998 whereas the record-breaking warm decade since 2000 is mainly due to the effects of the increased greenhouse gases. Implications for operational seasonal prediction will be discussed.

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Kristi R. Arsenault, Grey S. Nearing, Shugong Wang, Soni Yatheendradas, and Christa D. Peters-Lidard

Abstract

The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for the dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include the dynamic simulation of vegetation and carbon to 43 primary user-specified parameters. This is done using 32 years’ worth of data from 10 international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes.

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Xuguang Wang, Thomas M. Hamill, Jeffrey S. Whitaker, and Craig H. Bishop

Abstract

A hybrid analysis scheme is compared with an ensemble square root filter (EnSRF) analysis scheme in the presence of model errors as a follow-up to a previous perfect-model comparison. In the hybrid scheme, the ensemble perturbations are updated by the ensemble transform Kalman filter (ETKF) and the ensemble mean is updated with a hybrid ensemble and static background-error covariance. The experiments were conducted with a two-layer primitive equation model. The true state was a T127 simulation. Data assimilation experiments were conducted at T31 resolution (3168 complex spectral coefficients), assimilating imperfect observations drawn from the T127 nature run. By design, the magnitude of the truncation error was large, which provided a test on the ability of both schemes to deal with model error. Additive noise was used to parameterize model errors in the background ensemble for both schemes. In the first set of experiments, additive noise was drawn from a large inventory of historical forecast errors; in the second set of experiments, additive noise was drawn from a large inventory of differences between forecasts and analyses. The static covariance was computed correspondingly from the two inventories. The hybrid analysis was statistically significantly more accurate than the EnSRF analysis. The improvement of the hybrid over the EnSRF was smaller when differences of forecasts and analyses were used to form the random noise and the static covariance. The EnSRF analysis was more sensitive to the size of the ensemble than the hybrid. A series of tests was conducted to understand why the EnSRF performed worse than the hybrid. It was shown that the inferior performance of the EnSRF was likely due to the sampling error in the estimation of the model-error covariance in the mean update and the less-balanced EnSRF initial conditions resulting from the extra localizations used in the EnSRF.

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Mu-King Tsay, Ting-I. Wang, R. S. Lawrence, G. R. Ochs, and R. B. Fritz

Abstract

In a cooperative field study of the planetary boundary layer, three optical wind sensors were placed around a 300 m meteorological tower in a 450 m equilateral triangle 3–4 m above the terrain. It was found that the convergence measured by the three-sensor system correlates well with in situ measurements of vertical wind by anemometers located on the tower at heights up to 300 m during the occurrence of thermal plumes. By analyzing the correlation between the optically measured convergence and the vertical wind measurements made on the tower, the inversion layer, if below the top of the tower, can usually be located in the early morning when thermal plumes are active. The space-averaged horizontal wind vectors measured by the optical system have good, though not perfect, agreement with the tower measurements at the lowest layer (10 m above the ground), and with the measurements of a nearby network of surface anemometers. A comparison of the optically measured convergence with the direction of the surface horizontal wind indicates some effect of irregular terrain.

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G. S. Kent, E. R. Williams, P-H. Wang, M. P. McCormick, and K. M. Skeens

Abstract

Data from the Stratospheric Aerosol and Gas Experiment II (SAGE II) solar occultation satellite instrument have been used to study the properties of tropical cloud over the altitude range 10.5–18.5 km. By virtue of its limb viewing measurement geometry, SAGE II has good vertical resolution and sensitivity to subvisual cloud not detectable by most other satellite instruments. The geographical distribution and temporal variation of the cloud occurrence have been examined over all longitudes on timescales from less than 1 day to that of the El Niño-Southern Oscillation (ENSO) cycle. Significant variations in cloud occurrence are found on each of these scales and have been compared with the underlying surface temperature changes. Maximum cloud occurs over the warm pool region of the Pacific Ocean, with secondary maxima over the South American and Central African landmasses, where the percentage of cloud occurrence in the upper troposphere can exceed 75%. Cloud occurrence at all altitudes within the Tropics, over both land and ocean, increases with the underlying surface temperature at a rate of approximately 13%°C−1. Extrapolated threshold temperatures for the formation of cloud are about 5°C lower than those found from nadir viewing observations. This difference is believed to be a consequence of the averaging process and the inclusion of outliers in the dataset. ENSO cycle changes in cloud occurrence are observed, not only over the Tropics but also over the subtropics, indicating a difference in the meridional Hadley circulation between ENSO warm and cold years. Sunrise–sunset cloud differences indicate that large-scale variations, whose form resembles that of the Hadley and Walker circulations, are present, with a timescale of 1 day or less. The global distribution of upper-tropospheric ice and its positive correlation with surface temperature on all timescales are generally consistent with the behavior of lightning and the global electrical circuit.

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