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Jiangfeng Wei, Paul A. Dirmeyer, and Zhichang Guo

Abstract

The Global Land–Atmosphere Coupling Experiment (GLACE) built a framework to estimate the strength of the land–atmosphere interaction across many weather and climate models. Within this framework, GLACE-type experiments are performed with a single atmospheric model coupled to three different land models. The precipitation time series is decomposed into three frequency bands to investigate the large-scale connection between external forcing, precipitation variability and predictability, and land–atmosphere coupling strength. It is found that coupling to different land models or prescribing subsurface soil moisture does not change the global pattern of precipitation predictability and variability too much. However, the regional impact of soil moisture can be highlighted by calculating the land–atmosphere coupling strength, which shows very different patterns for the three models. The estimated precipitation predictability and land–atmosphere coupling strength is mainly associated with the low-frequency component of precipitation (periods beyond 3 weeks). Based on these findings, the land–atmosphere coupling strength is conceptually decomposed into the impact of low-frequency external forcing and the impact of soil moisture. Because most models participating in GLACE have overestimated the low-frequency component of precipitation, a calibration to the GLACE-estimated land–atmosphere coupling strength is performed. The calibrated coupling strength is generally weaker, but the global pattern does not change much. This study provides an important clarification of land–atmosphere coupling strength and increases the understanding of the land–atmosphere interaction.

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Zhichang Guo, David H. Bromwich, and Keith M. Hines

Abstract

The impacts of the El Niño–Southern Oscillation (ENSO) on the Antarctic region are of special importance in evaluating the variability and change of the climate system in high southern latitudes. In this study, the ENSO signal in modeled precipitation over West Antarctica since 1979 is evaluated using forecast precipitation from several meteorological analyses and reanalyses. Additionally, a dynamical retrieval method (DRM) for precipitation is applied. Over the last two decades, the Southern Oscillation index (SOI) has an overall anticorrelation with precipitation over the West Antarctic sector bounded by 75°–90°S, 120°W–180° while it is positively correlated with precipitation over the South Atlantic sector bounded by 65°–75°S, 30°–60°W.

Decadal variations are found as the relationship between the SOI and West Antarctic precipitation is stronger in the 1990s than that in the 1980s. The polar front jet stream, West Antarctic precipitation, and the SOI show a well-ordered correspondence during the 1990s as the jet zonal speed is negatively correlated to the SOI and positively correlated to West Antarctic precipitation. These relationships are weaker during the 1980s, consistent with the change in sign of the correlation between the SOI and West Antarctic precipitation. The decadal variations are apparently related to changes in the quasi-stationary eddies that determine the local onshore and offshore flow over West Antarctica.

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David H. Bromwich, Zhichang Guo, Lesheng Bai, and Qiu-shi Chen

Abstract

Surface snow accumulation is the primary mass input to the Antarctic ice sheets. As the dominant term among various components of surface snow accumulation (precipitation, sublimation/deposition, and snow drift), precipitation is of particular importance in helping to assess the mass balance of the Antarctic ice sheets and their contribution to global sea level change.

The Polar MM5, a mesoscale atmospheric model based on the fifth-generation Pennsylvania State University–NCAR Mesoscale Model, has been run for the period of July 1996 through June 1999 to evaluate the spatial and temporal variability of Antarctic precipitation. Drift snow effects on the redistribution of surface snow over Antarctica are also assessed with surface wind fields from Polar MM5 in this study. It is found that areas with large drift snow transport convergence and divergence are located around escarpment areas where there is considerable katabatic wind acceleration. It is also found that the drift snow transport generally diverges over most areas of East and West Antarctica with relatively small values.

The use of the dynamic retrieval method (DRM) to calculate precipitation has been developed and verified for the Greenland ice sheet. The DRM is also applied to retrieve the precipitation over Antarctica from 1979 to 1999 in this study. Most major features in the spatial distribution of Antarctic accumulation are well captured by the DRM results. In comparison with predicted precipitation amounts from atmospheric analyses and reanalyses, DRM calculations capture more mesoscale features of the precipitation distribution over Antarctica. A significant upward trend of +1.3 to +1.7 mm yr−2 for 1979–99 is found from DRM and forecast precipitation amounts for Antarctica that is consistent with results reported by other investigators and indicates that an additional 0.05 mm yr−1 is being extracted from the global ocean and locked up in the Antarctic ice sheets. While there is good agreement in this trend among all of the datasets, the interannual variability about the trend on the continental scale is not well captured. However, on the subcontinental scale, the interannual variability about the trend is well resolved for sectors in West Antarctica and the South Atlantic. It is also noted that the precipitation trend is weakly downward over much of the continent.

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David H. Bromwich, Andrew J. Monaghan, and Zhichang Guo

Abstract

The Polar fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is employed to examine the El Niño–Southern Oscillation (ENSO) modulation of Antarctic climate for July 1996–June 1999, which is shown to be stronger than for the mean modulation from 1979 to 1999 and appears to be largely due to an eastward shift and enhancement of convection in the tropical Pacific Ocean. This study provides a more comprehensive assessment than can be achieved with observational datasets by using a regional atmospheric model adapted for high-latitude applications (Polar MM5). The most pronounced ENSO response is observed over the Ross Ice Shelf–Marie Byrd Land and over the Weddell Sea–Ronne/Filchner Ice Shelf. In addition to having the largest climate variability associated with ENSO, these two regions exhibit anomalies of opposite sign throughout the study period, which supports and extends similar findings by other investigators. The dipole structure is observed in surface temperature, meridional winds, cloud fraction, and precipitation. The ENSO-related variability is primarily controlled by the large-scale circulation anomalies surrounding the continent, which are consistent throughout the troposphere. When comparing the El Niño/La Niña phases of this late 1990s ENSO cycle, the circulation anomalies are nearly mirror images over the entire Antarctic, indicating their significant modulation by ENSO. Large temperature anomalies, especially in autumn, are prominent over the major ice shelves. This is most likely due to their relatively low elevation with respect to the continental interior making them more sensitive to shifts in synoptic forcing offshore of Antarctica, especially during months with considerable open water. The Polar MM5 simulations are in broad agreement with observational data, and the simulated precipitation closely follows the European Centre for Medium-Range Weather Forecasts Tropical Ocean–Global Atmosphere precipitation trends over the study period. The collective findings of this work suggest the Polar MM5 is capturing ENSO-related atmospheric variability with good skill and may be a useful tool for future climate studies.

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Zhichang Guo, Paul A. Dirmeyer, Timothy DelSole, and Randal D. Koster

Abstract

Total predictability within a chaotic system like the earth’s climate cannot increase over time. However, it can be transferred between subsystems. Predictability of air temperature and precipitation in numerical model forecasts over North America rebounds during late spring to summer because of information stored in the land surface. Specifically, soil moisture anomalies can persist over several months, but this memory cannot affect the atmosphere during early spring because of a lack of coupling between land and atmosphere. Coupling becomes established in late spring, enabling the effects of soil moisture anomalies to increase atmospheric predictability in 2-month forecasts begun as early as 1 May. This predictability is maintained through summer and then drops as coupling fades again in fall. This finding suggests summer forecasts of rainfall and air temperature over parts of North America could be significantly improved with soil moisture observations during spring.

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Randal D. Koster, Zhichang Guo, Rongqian Yang, Paul A. Dirmeyer, Kenneth Mitchell, and Michael J. Puma

Abstract

The soil moisture state simulated by a land surface model is a highly model-dependent quantity, meaning that the direct transfer of one model’s soil moisture into another can lead to a fundamental, and potentially detrimental, inconsistency. This is first illustrated with two recent examples, one from the National Centers for Environmental Prediction (NCEP) involving seasonal precipitation forecasting and another from the realm of ecological modeling. The issue is then further addressed through a quantitative analysis of soil moisture contents produced as part of a global offline simulation experiment in which a number of land surface models were driven with the same atmospheric forcing fields. These latter comparisons clearly demonstrate, on a global scale, the degree to which model-simulated soil moisture variables differ from each other and that these differences extend beyond those associated with model-specific layer thicknesses or soil texture. The offline comparisons also show, however, that once the climatological statistics of each model’s soil moisture variable are accounted for (here, through a simple scaling using the first two moments), the different land models tend to produce very similar information on temporal soil moisture variability in most parts of the world. This common information can perhaps be used as the basis for successful mappings between the soil moisture variables in different land models.

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Jiangfeng Wei, Paul A. Dirmeyer, Zhichang Guo, Li Zhang, and Vasubandhu Misra

Abstract

An atmospheric general circulation model (AGCM) is coupled to three different land surface schemes (LSSs), both individually and in combination (i.e., the LSSs receive the same AGCM forcing each time step and the averaged upward surface fluxes are passed back to the AGCM), to study the uncertainty of simulated climatologies and variabilities caused by different LSSs. This tiling of the LSSs is done to study the uncertainty of simulated mean climate and climate variability caused by variations between LSSs. The three LSSs produce significantly different surface fluxes over most of the land, no matter whether they are coupled individually or in combination. Although the three LSSs receive the same atmospheric forcing in the combined experiment, the inter-LSS spread of latent heat flux can be larger or smaller than the individually coupled experiment, depending mostly on the evaporation regime of the schemes in different regions. Differences in precipitation are the main reason for the different latent heat fluxes over semiarid regions, but for sensible heat flux, the atmospheric differences and LSS differences have comparable contributions. The influence of LSS uncertainties on the simulation of surface temperature is strongest in dry seasons, and its influence on daily maximum temperature is stronger than on minimum temperature. Land–atmosphere interaction can dampen the impact of LSS uncertainties on surface temperature in the tropics, but can strengthen their impact in middle to high latitudes. Variations in the persistence of surface heat fluxes exist among the LSSs, which, however, have little impact on the global pattern of precipitation persistence. The results provide guidance to future diagnosis of model uncertainties related to LSSs.

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Paul A. Dirmeyer, Sanjiv Kumar, Michael J. Fennessy, Eric L. Altshuler, Timothy DelSole, Zhichang Guo, Benjamin A. Cash, and David Straus

Abstract

The climate system model of the National Center for Atmospheric Research is used to examine the predictability arising from the land surface initialization of seasonal climate ensemble forecasts in current, preindustrial, and projected future settings. Predictability is defined in terms of the model's ability to predict its own interannual variability. Predictability from the land surface in this model is relatively weak compared to estimates from other climate models but has much of the same spatial and temporal structure found in previous studies. Several factors appear to contribute to the weakness, including a low correlation between surface fluxes and subsurface soil moisture, less soil moisture memory (lagged autocorrelation) than other models or observations, and relative insensitivity of the atmospheric boundary layer to surface flux variations. Furthermore, subseasonal cyclical behavior in plant phenology for tropical grasses introduces spurious unrealistic predictability at low latitudes during dry seasons. Despite these shortcomings, intriguing changes in predictability are found. Areas of historical land use change appear to have experienced changes in predictability, particularly where agriculture expanded dramatically into the Great Plains of North America, increasing land-driven predictability there. In a warming future climate, land–atmosphere coupling strength generally increases, but added predictability does not always follow; many other factors modulate land-driven predictability.

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