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Zhichang Guo and Paul A. Dirmeyer

Abstract

Recent studies in the Global Land–Atmosphere Coupling Experiment (GLACE) established a framework to estimate the extent to which anomalies in the land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. Within this framework, a multiyear GLACE-type experiment is carried out with a coupled land–atmosphere general circulation model to examine the interannual variability of land–atmosphere coupling strength. Soil wetness with intermediate values are in the range at which rainfall generation, near-surface air temperature, and surface turbulent fluxes are most sensitive to soil moisture anomalies, and thus, land–atmosphere coupling strength peaks in this range. As a result, the “hot spots” with strong land–atmosphere coupling strength appear in regions with intermediate climatological soil wetness (e.g., transition zones between dry and wet climates), consistent with previous studies. Land–atmosphere coupling strength experiences significant year-to-year variation because of interannual variability of soil moisture and the local spatiotemporal evolution of hydrologic regime. Coupling strength over areas with dry (wet) climate is enhanced during wet (dry) years since the resultant soil wetness enters into the sensitive range from a relatively insensitive range, and soil moisture can have stronger potential impact on surface turbulent fluxes and convection. On the other hand, land–atmosphere coupling strength over areas with wet (dry) climate is weakened during wet (dry) years since the soil wetness moves further away from the sensitive range. This results in a positive correlation between the land–atmosphere coupling strength and soil moisture anomalies over areas with dry climate and a negative correlation over areas with wet climate.

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Paul A. Dirmeyer, Zhichang Guo, and Xiang Gao

Abstract

The characteristics of eight global soil wetness products, three produced by land surface model calculations, three from coupled land–atmosphere model reanalyses, and two from microwave remote sensing estimates, have been examined. The goal of this study is to determine whether there exists an optimal dataset for the initialization of the land surface component of global weather and climate forecast models. Their abilities to simulate the phasing of the annual cycle and to accurately represent interannual variability in soil wetness by comparing to available in situ measurements are validated. Because soil wetness climatologies vary greatly among land surface models, and models have different operating ranges for soil wetness (i.e., very different mean values, variances, and hydrologically critical thresholds such as the point where evaporation occurs at the potential rate or where surface runoff begins), one cannot simply take the soil wetness field from one product and apply it to an arbitrary land surface scheme (LSS) as an initial condition without experiencing some sort of initialization shock. A means of renormalizing soil wetness is proposed based on the local statistical properties of this field in the source and target models, to allow a large number of climate models to apply the same initialization in multimodel studies or intercomparisons. As a test of feasibility, renormalization among the model-derived products is applied to see how it alters the character of the soil wetness climatologies.

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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, 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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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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Zhichang Guo, David H. Bromwich, and John J. Cassano

Abstract

Evaluation of a complete annual cycle of nonhydrostatic mesoscale model simulations of the Antarctic atmospheric circulation is presented. The year-long time series are compiled from a series of overlapping short-duration (72 h) simulations of the atmospheric state with the first 24 h being discarded for spinup reasons, and the 24–72-h periods used for model evaluation. The simulations are generated with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), which is modified for polar applications, and is referred to as the Polar MM5. With a horizontal resolution of 60 km, the Polar MM5 has been run for the period of January 1993–December 1993, creating short-term simulations from initial and boundary conditions provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) Tropical Ocean Global Atmosphere (TOGA) operational analyses. The model output is compared with observations from automatic weather stations, upper-air data, and global atmospheric analyses as well as climatological maps over timescales from diurnal to annual. In comparison with the observations, the evaluation shows that simulations with the Polar MM5 capture both the large- and regional-scale circulation features with generally small bias in the modeled variables. For example, the differences between the observations and simulations at the 500-hPa level are usually less than 2°C for temperature and dewpoint temperature, and 20 m for geopotential height. On the annual timescale the largest errors in the model simulations are the deficient total cloud cover and precipitation, and the colder near-surface temperature over the interior of the Antarctic plateau. The deficiencies in the cloud prediction and precipitation simulation follow from low-level dry biases found in the Polar MM5 simulations, and the cold bias is related to the low predicted downward longwave radiation under clear skies in the radiation parameterization scheme. The deficient predicted precipitation also reflects the limited ability of Polar MM5 to represent clear sky precipitation. On the seasonal timescale a persistent positive pressure bias is found in the model simulations, caused by the interaction between the gravity waves and the model upper boundary condition. The observed synoptic variability of the pressure, temperature, wind speed, wind direction, and water vapor mixing ratio, as well as the diurnal cycles of temperature, wind speed, and mixing ratio, are reproduced by the Polar MM5 with reasonable accuracy.

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Xiang Gao, Paul A. Dirmeyer, Zhichang Guo, and Mei Zhao

Abstract

A coupled land–atmosphere climate model is used to investigate the impact of vegetation parameters (leaf area index, absorbed radiation, and greenness fraction) on the simulation of surface fluxes and their potential role in improving climate forecasts. Ensemble simulations for 1986–95 have been conducted with specified observed sea surface temperatures. The vegetation impact is analyzed by comparing integrations with two different ways of specifying vegetation boundary conditions: observed interannually varying vegetation versus the climatological annual cycle. Parallel integrations are also implemented and analyzed for the land surface model in an uncoupled mode within the framework of the Second Global Soil Wetness Project (GSWP-2) for the same period. The sensitivity to vegetation anomalies in the coupled simulations appears to be relatively small. There appears to be only episodic and localized favorable impacts of vegetation variations on the skill of precipitation and temperature simulations. Impacts are sometimes manifested strictly through changes in land surface fluxes, and in other cases involve clear interactions with atmospheric processes. In general, interannual variations of vegetation tend to increase the temporal variability of radiation fluxes, soil evaporation, and canopy interception loss in terms of both spatial frequency and global mean. Over cohesive regions of significant and persistent vegetation anomalies, cumulative statistics do show a net response of surface fluxes, temperature, and precipitation with vegetation anomalies of ±20% corresponding to a precipitation response of about ±6%. However, in about half of these cases no significant response was found. The results presented here suggest that vegetation may be a useful element of the land surface for enhancing seasonal predictability, but its role in this model appears to be relatively minor. Improvement does not occur in all circumstances, and strong anomalies have the best chance of a positive impact on the simulation.

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

Abstract

The Global Energy and Water Cycle Experiment/Climate Variability and Predictability (GEWEX/CLIVAR) Global Land–Atmosphere Coupling Experiment (GLACE) has provided an estimate of the global distribution of land–atmosphere coupling strength during boreal summer based on the results from a dozen weather and climate models. However, there is a great deal of variation among models, attributable to a range of sensitivities in the simulation of both the terrestrial and atmospheric branches of the hydrologic cycle. It remains an open question whether any of the models, or the multimodel estimate, reflects the actual pattern and strength of land–atmosphere coupling in the earth’s hydrologic cycle. The authors attempt to diagnose this by examining the local covariability of key atmospheric and land surface variables both in models and in those few locations where comparable, relatively complete, long-term measurements exist. Most models do not encompass well the observed relationships between surface and atmospheric state variables and fluxes, suggesting that these models do not represent land–atmosphere coupling correctly. Specifically, there is evidence that systematic biases in near-surface temperature and humidity among all models may contribute to incorrect surface flux sensitivities. However, the multimodel mean generally validates better than most or all of the individual models. Regional precipitation behavior (lagged autocorrelation and predisposition toward maintenance of extremes) between models and observations is also compared. Again a great deal of variation is found among the participating models, but remarkably accurate behavior of the multimodel mean.

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