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L. R. Leung and S. J. Ghan

Abstract

A model nesting approach has been used to simulate the regional climate over the Pacific Northwest. The present-day global climatology is first simulated using the NCAR Community Climate Model (CCM3) driven by observed sea surface temperature and sea ice distribution at T42 (2.8°) resolution. This large-scale simulation is used to provide lateral boundary conditions for driving the Pacific Northwest National Laboratory Regional Climate Model (RCM). One notable feature of the RCM is the use of subgrid parameterizations of orographic precipitation and vegetation cover, in which subgrid variations of surface elevation and vegetation are aggregated to a limited number of elevation–vegetation classes. An airflow model and a thermodynamic model are used to parameterize the orographic uplift/descent as air parcels cross over mountain barriers or valleys.

The 7-yr climatologies as simulated by CCM3 and RCM are evaluated and compared in terms of large-scale spatial patterns and regional means. Biases are found in the simulation of large-scale circulations, which also affect the regional model simulation. Therefore, the regional simulation is not very different from the CCM3 simulation in terms of large-scale features. However, the regional model greatly improves the simulation of precipitation, surface temperature, and snow cover at the local scales. This is shown by improvements in the spatial correlation between the observations and simulations. The RCM simulation is further evaluated using station observations of surface temperature and precipitation to compare the simulated and observed relationships between surface temperature–precipitation and altitude. The model is found to correctly capture the surface temperature–precipitation variations as functions of surface topography over different mountain ranges, and under different climate regimes.

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L. R. Leung and S. J. Ghan

Abstract

Global climate change due to increasing concentrations of greenhouse gases has stimulated numerous studies and discussions about its possible impacts on water resources. Climate scenarios generated by climate models at spatial resolutions ranging from about 50 km to 400 km may not provide enough spatial specificity for use in impact assessment. In Parts I and II of this paper, the spatial specificity issue is addressed by examining what information on mesoscale and small-scale spatial features can be gained by using a regional climate model with a subgrid parameterization of orographic precipitation and land surface cover, driven by a general circulation model. Numerical experiments have been performed to simulate the present-day climatology and the climate conditions corresponding to a doubling of atmospheric CO2 concentration. This paper describes and contrasts the large-scale and mesoscale features of the greenhouse warming climate signals simulated by the general circulation model and regional climate model over the Pacific Northwest.

Results indicate that changes in the large-scale circulation exhibit strong seasonal variability. There is an average warming of about 2°C, and precipitation generally increases over the Pacific Northwest and decreases over California. The precipitation signal over the Pacific Northwest is only statistically significant during spring, when both the change in the large-scale circulation and increase in water vapor enhance the moisture convergence toward the north Pacific coast. The combined effects of surface temperature and precipitation changes are such that snow cover is reduced by up to 50% on average, causing large changes in the seasonal runoff. This paper also describes the high spatial resolution (1.5 km) climate signals simulated by the regional climate model. Reductions in snow cover of 50%–90% are found in areas near the snow line of the control simulation. Analyses of the variations of the climate signals with surface elevation ranging from sea level to 4000 m over two mountain ranges in the Pacific Northwest show that because of changes in the alitude of the freezing level, strong elevation dependency is found in the surface temperature, rainfall, snowfall, snow cover, and runoff signals.

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L. R. Leung and S. J. Ghan

Abstract

Previous development of the Pacific Northwest National Laboratory’s regional climate model has focused on representing orographic precipitation using a subgrid parameterization where subgrid variations of surface elevation are aggregated to a limited number of elevation classes. An airflow model and a thermodynamic model are used to parameterize the orographic uplift/descent as air parcels cross over mountain barriers or valleys. This paper describes further testing and evaluation of this subgrid parameterization. Building upon this modeling framework, a subgrid vegetation scheme has been developed based on statistical relationships between surface elevation and vegetation. By analyzing high-resolution elevation and vegetation data, a dominant land cover is defined for each elevation band of each model grid cell to account for the subgrid heterogeneity in vegetation. When larger lakes are present, they are distinguished from land within elevation bands and a lake model is used to simulate the thermodynamic properties. The use of the high-resolution vegetation data and the subgrid vegetation scheme has resulted in an improvement in the model’s representation of surface cover over the western United States. Simulation using the new vegetation scheme yields a 1°C cooling when compared with a simulation where vegetation was derived from a 30-min global vegetation dataset without subgrid vegetation treatment; this cooling helps to reduce the warm bias previously found in the regional climate model. A 3-yr simulation with the subgrid parameterization in the climate model is compared with observations.

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Lai-Yung Leung and Gerald R. North

Abstract

This paper introduces the use of information theory in characterizing climate predictability. Specifically, the concepts of entropy and transinformation are employed. Entropy measures the amount of uncertainty in our knowledge of the state of the climate system. Transinformation represents the information gained about an anomaly at any time t with knowledge of the size of the initial anomaly. It has many desirable properties that can be used as a measure of the predictability of the climate system. These concepts when applied to climate predictability are illustrated through a simple stochastic climate model (an energy balance model forced by noise). The transinformation is found to depict the degradation of information about an anomaly despite the fact that we have perfect knowledge of the initial state. Its usefulness, especially when generalized to other climate models, is discussed.

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Lai-Yung Leung and Gerald R. North

Abstract

Atmospheric variability an a zonally symmetric planet in the absence of external forcing anomalies is studied. With idealized boundary conditions such as the absence of ocean and topography, and by using perpetual equinox solar forcing, a 15-year long stationary time series of the atmosphere is simulated with the NCAR Community Climate Model (CCM0). This provides sufficient time samples for realistic study of the properties of the atmosphere. Zonally averaged and space-time statistics for the surface air temperature field on this planet are presented. Such statistics can serve as noise climatologies for climate sensitivity experiments, allowing the effects of changes of external forcing on the atmosphere to be asssessed.

In search of a simple statistical model for atmospheric variability, the space-time spectra obtained from the CCM simulation are fitted statistically with a stochastic energy balance model. The space-time spectra for three zonal wavenumbers are found to be fitted satisfactorily by the stochastic model with only five parameters (a heat diffusion coefficient, a constant zonal advection speed, a radiative damping constant and two parameters for blue spatial noise amplitudes). The estimated parameters agree with previously obtained values. This suggests that useful statistics for large-scale atmospheric variability may be obtained from simple statistical models. With the method of analysis provided in this study, the ability of the stochastic model for describing atmospheric variability on a more realistic planet (including geography and seasonal cycle) can be tested. This may involve comparing space-time statistics from the stochastic model with observed quantities and by using empirical orthogonal functions as a basis set for expansion.

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M. Hoerling, J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager

Central Great Plains precipitation deficits during May–August 2012 were the most severe since at least 1895, eclipsing the Dust Bowl summers of 1934 and 1936. Drought developed suddenly in May, following near-normal precipitation during winter and early spring. Its proximate causes were a reduction in atmospheric moisture transport into the Great Plains from the Gulf of Mexico. Processes that generally provide air mass lift and condensation were mostly absent, including a lack of frontal cyclones in late spring followed by suppressed deep convection in the summer owing to large-scale subsidence and atmospheric stabilization.

Seasonal forecasts did not predict the summer 2012 central Great Plains drought development, which therefore arrived without early warning. Climate simulations and empirical analysis suggest that ocean surface temperatures together with changes in greenhouse gases did not induce a substantial reduction in sum mertime precipitation over the central Great Plains during 2012. Yet, diagnosis of the retrospective climate simulations also reveals a regime shift toward warmer and drier summertime Great Plains conditions during the recent decade, most probably due to natural decadal variability. As a consequence, the probability of the severe summer Great Plains drought occurring may have increased in the last decade compared to the 1980s and 1990s, and the so-called tail risk for severe drought may have been heightened in summer 2012. Such an extreme drought event was nonetheless still found to be a rare occurrence within the spread of 2012 climate model simulations. The implications of this study's findings for U.S. seasonal drought forecasting are discussed.

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Karthik Balaguru, Gregory R. Foltz, L. Ruby Leung, Samson M. Hagos, and David R. Judi

Abstract

Sea surface temperature (SST) and tropical cyclone heat potential (TCHP) are metrics used to incorporate the ocean’s influence on hurricane intensification into the National Hurricane Center’s Statistical Hurricane Intensity Prediction Scheme (SHIPS). While both SST and TCHP serve as useful measures of the upper-ocean heat content, they do not accurately represent ocean stratification effects. Here, it is shown that replacing SST within the SHIPS framework with a dynamic temperature T dy, which accounts for the oceanic negative feedback to the hurricane’s intensity arising from storm-induced vertical mixing and sea surface cooling, improves the model performance. While the model with SST and TCHP explains about 41% of the variance in 36-h intensity changes, replacing SST with T dy increases the variance explained to nearly 44%. These results suggest that representation of the oceanic feedback, even through relatively simple formulations such as T dy, may improve the performance of statistical hurricane intensity prediction models such as SHIPS.

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Gerald R. North, Kuor-Jier Joseph Yip, Lai-Yung Leung, and Robert M. Chervin

Abstract

The concept of “forced” and “free” variations of large-scale surface temperature is examined by analyzing several long runs of the Community Climate Model (CCMO) with idealized boundary conditions and forcing. 1) The planet is all land with uniform sea-level topography and fixed soil moisture. 2) The planetary surface and prescribed ozone are reflection symmetric across the equator and there is no generation of snow. 3) The obliquity is set to zero so that the climate is for a perpetual equinox solar insolation (i.e., sun fixed over the equator). After examining some relevant aspects of the undisturbed climate (surface temperature field) such as temporal and spatial autocorrelations and the corresponding spectra, two types of changes in external forcing are imposed to study the model response: 1) sinusoidal changes of the solar constant (5%, 10%, 90%, and 40% amplitudes) at periods of 15 and 30 days (the latter is the autocorrelation time for the global average surface temperature) and 20% at 60 days and 2) insertion of steady heat sources (points and zonal bands) of variable strength at the surface. Then the temporal spectra of large scales for the periodically forced climate and the ensemble-averaged influence functions are examined for the point source disturbed climates. In each class of experiments the response of ensemble-averaged amplitudes was found to be proportional to the amplitude of the forcing. These results suggest that the lowest moments of the surface temperature field have a particularly simple dependence on forcing. Furthermore, the apparent finiteness of the variance spectrum at low frequencies suggests that estimates of long-term statistics are stable in this type of atmospheric general circulation model.

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Karthik Balaguru, Gregory R. Foltz, L. Ruby Leung, John Kaplan, Wenwei Xu, Nicolas Reul, and Bertrand Chapron

Abstract

Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.

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N. Goldenson, L. R. Leung, C. M. Bitz, and E. Blanchard-Wrigglesworth

Abstract

In the coastal mountains of western North America, most extreme precipitation is associated with atmospheric rivers (ARs), narrow bands of moisture originating in the tropics. Here we quantify how interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales (MPAS) with physics from the Community Atmosphere Model, version 5 [CAM5 (MPAS-CAM5)], using prescribed sea surface temperatures. In the global variable-resolution domain, regional refinement (at ~30 km) is applied to our region of interest and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Range, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada, winters with more ARs are associated with greater snowpack. The slope of the linear regression of observed snow water equivalent (SWE) on reanalysis-based AR count has the same sign as that arrived at using the model, but is statistically significant in observations only for California. In spring, internal variance plays an important role in determining whether atmospheric river days appear to be associated with greater or less snowpack. The cumulative (winter through spring) number of atmospheric river days, on the other hand, has a relationship with spring snowpack, which is consistent across ensemble members. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers in the model for both regions and in California consistently in observations.

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