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Thomas R. Karl, Wei-Chyung Wang, Michael E. Schlesinger, Richard W. Knight, and David Portman

Abstract

Important surface observations such as the daily maximum and minimum temperature, daily precipitation, and cloud ceilings often have localized characteristics that are difficult to reproduce with the current resolution and the physical parameterizations in state-of-the-art General Circulation climate Models (GCMs). Many of the difficulties can be partially attributed to mismatches in scale, local topography. regional geography and boundary conditions between models and surface-based observations. Here, we present a method, called climatological projection by model statistics (CPMS), to relate GCM grid-point flee-atmosphere statistics, the predictors, to these important local surface observations. The method can be viewed as a generalization of the model output statistics (MOS) and perfect prog (PP) procedures used in numerical weather prediction (NWP) models. It consists of the application of three statistical methods: 1) principle component analysis (FICA), 2) canonical correlation, and 3) inflated regression analysis. The PCA reduces the redundancy of the predictors The canonical correlation is used to develop simultaneous relationships between linear combinations of the predictors, the canonical variables, and the surface-based observations. Finally, inflated regression is used to relate the important canonical variables to each of the surface-based observed variables.

We demonstrate that even an early version of the Oregon State University two-level atmospheric GCM (with prescribed sea surface temperature) produces free-atmosphere statistics than can, when standardized using the model's internal means and variances (the MOS-like version of CPMS), closely approximate the observed local climate. When the model data are standardized by the observed free-atmosphere means and variances (the PP version of CPMS), however, the model does not reproduce the observed surface climate as well. Our results indicate that in the MOS-like version of CPMS the differences between the output of a ten-year GCM control run and the surface-based observations are often smaller than the differences between the observations of two ten-year periods. Such positive results suggest that GCMs may already contain important climatological information that can be used to infer the local climate.

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Chia-Chi Wang, Wei-Liang Lee, Yu-Luen Chen, and Huang-Hsiung Hsu

Abstract

The double intertropical convergence zone (ITCZ) bias in the eastern Pacific in the Community Earth System Model version 1 with Community Atmosphere Model version 5 (CESM1/CAM5) is diagnosed. In CAM5 standalone, the northern ITCZ is associated with inertial instability and the southern ITCZ is thermally forced. After air–sea coupling, the processes on both hemispheres are switched because the spatial pattern of sea surface temperature (SST) is changed.

Biases occur during boreal spring in both CAM5 and the ocean model. In CAM5 alone, weaker-than-observed equatorial easterly in the tropical eastern South Pacific leads to weaker evaporation and an increase in local SST. The shallow meridional circulation overly converges in the same region in the CAM5 standalone simulation, the planetary boundary layer and middle troposphere are too humid, and the large-scale subsidence is too weak at the middle levels. These biases may result from excessive shallow convection behavior in CAM5. The extra moisture would then fuel stronger convection and a higher precipitation rate in the southeastern Pacific.

In the ocean model, the South Equatorial Current is underestimated and the North Equatorial Countercurrent is located too close to the equator, causing a warm SST bias in the southeastern Pacific and a cold bias in the northeastern Pacific. These SST biases feed back to the atmosphere and further influence convection and the surface wind biases in the coupled simulation. When the convection in the tropical northeastern Pacific becomes thermally forced after coupling, the northern ITCZ is diminished due to colder SST, forming the so-called alternating ITCZ bias.

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Chao Wang, Liguang Wu, Jun Lu, Qingyuan Liu, Haikun Zhao, Wei Tian, and Jian Cao

Abstract

Understanding variations in tropical cyclone (TC) translation speed (TCS) is of great importance for islands and coastal regions since it is an important factor in determining TC-induced local damages. Investigating the long-term change in TCS was usually subject to substantial limitations in the quality of historical TC records, but here we investigated the interannual variability in TCS over the western North Pacific (WNP) Ocean by using reliable satellite TC records. It was found that both temporal changes in large-scale steering flow and TC track greatly contributed to interannual variability in the WNP TCS. In the peak season (July–September), TCS changes were closely related to temporal variations in large-scale steering flow, which was linked to the intensity of the western North Pacific subtropical high. However, for the late season (October–December), changes in TC track played a vital role in interannual variability in TCS while the impacts of temporal variations in large-scale steering were weak. The changes in TC track were mainly contributed by the El Niño–Southern Oscillation (ENSO)-induced zonal migrations in TC genesis locations, which make more or fewer TCs move to the subtropical WNP, thus leading to notable changes in the basinwide TCS because of the much greater large-scale steering in the subtropical WNP. The increased influence of TC track change on TCS in the late season was linked to the greater contrast between the subtropical and the tropical large-scale steering in the late season. These results have important implications for understanding current and future variations in TCS.

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Fuyao Wang, Yan Yu, Michael Notaro, Jiafu Mao, Xiaoying Shi, and Yaxing Wei

Abstract

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled control run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.

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Hainan Gong, Lin Wang, Wen Chen, Renguang Wu, Ke Wei, and Xuefeng Cui

Abstract

In this paper the model outputs from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) are used to examine the climatology and interannual variability of the East Asian winter monsoon (EAWM). The multimodel ensemble (MME) is able to reproduce reasonably well the circulation features of the EAWM. The simulated surface air temperature still suffers from a cold bias over East Asia, but this bias is reduced compared with CMIP phase 3 models. The intermodel spread is relatively small for the large-scale circulations, but is large for the lower-tropospheric meridional wind and precipitation along the East Asian coast. The interannual variability of the EAWM-related circulations can be captured by most of the models. A general bias is that the simulated variability is slightly weaker than in the observations. Based on a selected dynamic EAWM index, the patterns of the EAWM-related anomalies are well reproduced in MME although the simulated anomalies are slightly weaker than the observations. One general bias is that the northeasterly anomalies over East Asia cannot be captured to the south of 30°N. This bias may arise both from the inadequacies of the EAWM index and from the ability of models to capture the EAWM-related tropical–extratropical interactions. The ENSO–EAWM relationship is then evaluated and about half of the models can successfully capture the observed ENSO–EAWM relationship, including the significant negative correlation between Niño-3.4 and EAWM indices and the anomalous anticyclone (or cyclone) over the northwestern Pacific. The success of these models is attributed to the reasonable simulation of both ENSO’s spatial structure and its strength of interannual variability.

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Guoxing Chen, Wei-Chyung Wang, Lijun Tao, Huang-Hsiung Hsu, Chia-Ying Tu, and Chao-Tzuen Cheng

Abstract

This study used both observations and global climate model simulations to investigate the characteristics of winter extreme snowfall events along the coast (the Interstate 95 corridor) of the northeast United States where several mega-cities are located. Observational analyses indicate that, during 1980–2015, 110 events occurred when four coastal cities—Boston, New York City, Philadelphia, and Washington, D.C.—had either individually or collectively experienced daily snowfall exceeding the local 95th percentile thresholds. Boston had the most events, with a total of 69, followed by 40, 36, and 30 (moving southward) in the other three cities. The associated circulations at 200 and 850 hPa were categorized via K-means clustering. The resulting three composite circulations are characterized by the strength and location of the jet at 200 hPa and the coupled low pressure system at 850 hPa: a strong jet overlying the cities coupled with an inland trough, a weak and slightly southward shifted jet coupled with a cyclone at the coast, and a weak jet stream situated to the south of the cities coupled with a cyclone over the coastal oceans. Comparative analyses were also conducted using the GFDL High Resolution Atmospheric Model (HiRAM) simulation of the same period. Although the simulated extreme events do not provide one-to-one correspondence with observations, the characteristics nevertheless show consistency notably in total number of occurrences, intraseasonal and multiple-year variations, snow spatial coverage, and the associated circulation patterns. Possible future change in extreme snow events was also explored utilizing the HiRAM RCP8.5 (2075–2100) simulation. The analyses suggest that a warming global climate tends to decrease the extreme snowfall events but increase extreme rainfall events.

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