Abstract

The summer Mei-yu event over eastern China, which is strongly influenced by large-scale circulation, is an important aspect of East Asian climate; for example, the Mei-yu frequently brings heavy precipitation to the Yangtze–Huai River valley (YHRV). Both observations and a regional model were used to study the Mei-yu front and its relation to large-scale circulation during the summer of 1991 when severe floods occurred over YHRV. This study has two parts: the first part, presented here, analyzes the association between heavy Mei-yu precipitation and relevant large-scale circulation, while the second part, documented by W. Gong and W.-C. Wang, examines the model biases associated with the treatment of lateral boundary conditions (the objective analyses and coupling schemes) used as the driving fields for the regional model.

Observations indicate that the Mei-yu season in 1991 spans 18 May–14 July, making it the longest Mei-yu period during the last 40 yr. The heavy precipitation over YHRV is found to be intimately related to the western Pacific subtropical high, upper-tropospheric westerly jet at midlatitudes, and lower-tropospheric southwest wind and moisture flux. The regional model simulates reasonably well the regional mean surface air temperature and precipitation, in particular the precipitation evolution and its association with the large-scale circulation throughout the Mei-yu season. However, the model simulates smaller precipitation intensity, which is due partly to the colder and drier model atmosphere resulting from excessive low-level clouds and the simplified land surface process scheme used in the present study.

Abstract

Hydrological processes are strongly coupled with atmospheric processes related, for example, to precipitation and temperature, and a coupled atmosphere–land surface system is required for a meaningful hydrological forecast. Since the atmosphere is a chaotic system with limited predictability, ensemble forecasts offer a practical tool to predict the future state of the coupled system in a probabilistic fashion, potentially leading to a more complete and informative hydrologic prediction. As ensemble forecasts with coupled meteorological–hydrological models are operationally running at major numerical weather prediction centers, it is currently possible to produce a gridded streamflow prognosis in the form of a probabilistic forecast based on ensembles. Evaluation and improvement of such products require a comprehensive assessment of both components of the coupled system.

In this article, the atmospheric component of a coupled ensemble forecasting system is evaluated in terms of its ability to provide reasonable forcing to the hydrological component and the effect of the uncertainty represented in the atmospheric ensemble system on the predictability of streamflow as a hydrological variable. The Global Ensemble Forecast System (GEFS) of NCEP is evaluated following a “perfect hydrology” approach, in which its hydrological component, including the Noah land surface model and attached river routing model, is considered free of errors and the initial conditions in the hydrological variables are assumed accurate. The evaluation is performed over the continental United States (CONUS) domain for various sizes of river basins. The results from the experiment suggest that the coupled system is capable of generating useful gridded streamflow forecast when the land surface model and the river routing model can successfully simulate the hydrological processes, and the ensemble strategy significantly improves the forecast. The expected forecast skill increases with increasing size of the river basin. With the current GEFS system, positive skill in short-range (one to three days) predictions can be expected for all significant river basins; for the major rivers with mean streamflow more than 500 m³ s⁻¹, significant skill can be expected from extended-range (the second week) predictions. Possible causes for the loss of skills, including the existence of systematic error and insufficient ensemble spread, are discussed and possible approaches for the improvement of the atmospheric ensemble forecast system are also proposed.

Abstract

This study examines the performance of the NCEP Global Forecast System (GFS) surface layer parameterization scheme for strongly stable conditions over land in which turbulence is weak or even disappears because of high near-surface atmospheric stability. Cases of both deep snowpack and snow-free conditions are investigated. The results show that decoupling and excessive near-surface cooling may appear in the late afternoon and nighttime, manifesting as a severe cold bias of the 2-m surface air temperature that persists for several hours or more. Concurrently, because of negligible downward heat transport from the atmosphere to the land, a warm temperature bias develops at the first model level. The authors test changes to the stable surface layer scheme that include introduction of a stability parameter constraint that prevents the land–atmosphere system from fully decoupling and modification to the roughness-length formulation. GFS sensitivity runs with these two changes demonstrate the ability of the proposed surface layer changes to reduce the excessive near-surface cooling in forecasts of 2-m surface air temperature. The proposed changes prevent both the collapse of turbulence in the stable surface layer over land and the possibility of numerical instability resulting from thermal decoupling between the atmosphere and the surface. The authors also execute and evaluate daily GFS 7-day test forecasts with the proposed changes spanning a one-month period in winter. The assessment reveals that the systematic deficiencies and substantial errors in GFS near-surface 2-m air temperature forecasts are considerably reduced, along with a notable reduction of temperature errors throughout the lower atmosphere and improvement of forecast skill scores for light and medium precipitation amounts.

Abstract

The NCEP Climate Forecast System Reanalysis (CFSR) uses the NASA Land Information System (LIS) to create its land surface analysis: the NCEP Global Land Data Assimilation System (GLDAS). Comparing to the previous two generations of NCEP global reanalyses, this is the first time a coupled land–atmosphere data assimilation system is included in a global reanalysis. Global observed precipitation is used as direct forcing to drive the land surface analysis, rather than the typical reanalysis approach of using precipitation assimilating from a background atmospheric model simulation. Global observed snow cover and snow depth fields are used to constrain the simulated snow variables. This paper describes 1) the design and implementation of GLDAS/LIS in CFSR, 2) the forcing of the observed global precipitation and snow fields, and 3) preliminary results of global and regional soil moisture content and land surface energy and water budgets closure. With special attention made during the design of CFSR GLDAS/LIS, all the source and sink terms in the CFSR land surface energy and water budgets can be assessed and the total budgets are balanced. This is one of many aspects indicating improvements in CFSR from the previous NCEP reanalyses.

Abstract

A number of polar datasets have recently been released involving in situ measurements, satellite retrievals, and reanalysis output that provide new opportunities to evaluate regional climate in the Arctic. These data have been used to assess a 1-yr pan-Arctic simulation (October 1985–September 1986) performed by a version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) that incorporated the NCAR land surface model (LSM) and a simple thermodynamic sea ice model to investigate interactions between the land surface and atmosphere. The model's standard cloud scheme using relative humidity was replaced by one using simulated cloud liquid water and ice water after a set of short test simulations revealed excessive cloud cover.

Model validation concentrates on factors relevant to the water cycle: atmospheric circulation, temperature, surface radiation fluxes, precipitation, and runoff. The model captures general patterns of atmospheric circulation over land. The rms differences from the Historical Arctic Rawinsonde Archive (HARA) rawinsonde winds at 850 hPa are smaller for the simulation (9.8 m s⁻¹) than for the NCEP–NCAR reanalysis (10.5 m s⁻¹) that supplies the model's boundary conditions. For continental watersheds, the model simulates well annual average surface air temperature (bias <2°C) and precipitation (bias <0.5 mm day⁻¹). However, the model has a summer dry bias with monthly precipitation error occasionally exceeding 1 mm day⁻¹. The model simulates the approximate magnitude of spring runoff surge, but annual runoff is less than observed (18%–48% less among the continental watersheds). Analysis of precipitation and surface air temperature errors indicates that further improvements in both evapotranspiration and precipitation are needed to simulate well the full annual water cycle.

Abstract

The Arctic’s land surface has large areas of wetlands that exchange moisture, energy, and momentum with the atmosphere. The authors use a mesoscale, pan-Arctic model simulating the summer of 1986 to examine links between the wetlands and arctic atmospheric dynamics and water cycling. Simulations with and without wetlands are compared to simulations using perturbed initial and lateral boundary conditions to delineate when and where the wetlands influence rises above nonlinear internal variability. The perturbation runs expose the temporal variability of the circulation’s sensitivity to changes in lower boundary conditions. For the wetlands cases examined here, the period of the most significant influence is approximately two weeks, and the wetlands do not introduce new circulation changes but rather appear to reinforce and modify existing circulation responses to perturbations. The largest circulation sensitivity, and thus the largest wetlands influence, occurs in central Siberia. The circulation changes induced by adding the wetlands appear as a propagating, equivalent barotropic wave. The wetlands anomaly circulation spreads alterations of surface fluxes to other locations, which undermines the potential for the wetlands to present a distinctive, spatially fixed forcing to atmospheric circulation. Using the climatology of artic synoptic-storm occurrence to indicate when the arctic circulation is most sensitive to altered forcing, the results suggest that the circulation is susceptible to the direct influence of wetlands for a limited time period extending from spring thaw of wetlands until synoptic-storm occurrence diminishes in midsummer. Sensitivities in arctic circulation uncovered through this work occur during a period of substantial transition from a fundamentally frozen to thawed state, a period of major concern for impacts of greenhouse warming on pan-Arctic climate. Changing arctic climate could alter the behavior revealed here.

Abstract

To support the forecasting needs of the United States Antarctic Program at McMurdo, Antarctica, a special numerical weather prediction program, the Antarctic Mesoscale Prediction System (AMPS), was established for the 2000–01 field season. AMPS employs the Polar MM5, a version of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) that has physics modifications for polar environments. This study assesses the performance of AMPS in forecasting an event of mesoscale cyclogenesis in the western Ross Sea during 13–17 January 2001. Observations indicate the presence of a complex trough having two primary mesoscale lows that merge to the east of Ross Island shortly after 0700 UTC 15 January. In contrast, AMPS predicts one primary mesoscale low throughout the event, incorrectly placing it until the 1800 UTC 15 January forecast, when the observed system carries a prominent signature in the initialization. The model reproduces the evolution of upper-level conditions in agreement with the observations and shows skill in resolving many small-scale surface features common to the region (i.e., katabatic winds; lows and highs induced by wind/topography). The AMPS forecasts can rely heavily on the representation of surface lows and upper-level forcing in the first-guess fields derived from NCEP's Aviation Model (AVN). Furthermore, even with relatively high spatial resolution, mesoscale models face observation-related limitations on performance that can be particularly acute in Antarctica.

Abstract

Since the second phase of the North American Land Data Assimilation System (NLDAS-2) was operationally implemented at NOAA/NCEP as part of the production suite in August 2014, developing the next phase of NLDAS has been a key focus of the NCEP and NASA NLDAS teams. The Variable Infiltration Capacity (VIC) model is one of the four land surface models of the NLDAS system. The current operational NLDAS-2 uses version 4.0.3 (VIC403), the research NLDAS-2 used version 4.0.5 (VIC405), and the NASA Land Information System (LIS)-based NLDAS uses version 4.1.2.l (VIC412). The purpose of this study is to evaluate VIC403 and VIC412 and check if the latter version has better performance for the next phase of NLDAS. Toward this, a comprehensive evaluation was conducted, targeting multiple variables and using multiple metrics to assess the performance of different model versions. The evaluation results show large and significant improvements in VIC412 over the southeastern United States when compared with VIC403 and VIC405. In other regions, there are very limited improvements or even deterioration to some degree. This is partially due to 1) the sparseness of USGS streamflow observations for model parameter calibration and 2) a deterioration of VIC model performance in the Great Plains (GP) region after a model upgrade to a newer version. Overall, the model upgrade enhances model performance and skill scores for most parts of the continental United States; exceptions include the GP and western mountainous regions, as well as the daily soil moisture simulation skill, suggesting that VIC model development is on the right path. Further efforts are needed for scientific understanding of land surface physical processes in the GP, and a recalibration of VIC412 using reasonable reference datasets is recommended.

Abstract

Soil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North American Land Data Assimilation phase 2 (NLDAS-2) has generated 31 years (1979–2009) of simulated hourly soil temperature data with a spatial resolution of ⅛°. This dataset has not been comprehensively evaluated to date. Thus, the purpose of this paper is to assess Noah-simulated soil temperature for different soil depths and time scales. The authors used long-term (1979–2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0–10, 10–40, and 40–100 cm) for annual and monthly time scales. Short-term (1997–99) observed soil temperatures from 72 Oklahoma Mesonet stations were used to validate simulated soil temperatures for three soil layers and for daily and hourly time scales. The results showed that the Noah land surface model generally matches observed soil temperature well for different soil layers and time scales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season that are due to small downward longwave radiation and issues related to model parameters.

Abstract

Optimized regional climate simulations are conducted using the Polar MM5, a version of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5), with a 60-km horizontal resolution domain over North America during the Last Glacial Maximum (LGM, 21 000 calendar years ago), when much of the continent was covered by the Laurentide Ice Sheet (LIS). The objective is to describe the LGM annual cycle at high spatial resolution with an emphasis on the winter atmospheric circulation. Output from a tailored NCAR Community Climate Model version 3 (CCM3) simulation of the LGM climate is used to provide the initial and lateral boundary conditions for Polar MM5. LGM boundary conditions include continental ice sheets, appropriate orbital forcing, reduced CO₂ concentration, paleovegetation, modified sea surface temperatures, and lowered sea level.

Polar MM5 produces a substantially different atmospheric response to the LGM boundary conditions than CCM3 and other recent GCM simulations. In particular, from November to April the upper-level flow is split around a blocking anticyclone over the LIS, with a northern branch over the Canadian Arctic and a southern branch impacting southern North America. The split flow pattern is most pronounced in January and transitions into a single, consolidated jet stream that migrates northward over the LIS during summer. Sensitivity experiments indicate that the winter split flow in Polar MM5 is primarily due to mechanical forcing by LIS, although model physics and resolution also contribute to the simulated flow configuration.

Polar MM5 LGM results are generally consistent with proxy climate estimates in the western United States, Alaska, and the Canadian Arctic and may help resolve some long-standing discrepancies between proxy data and previous simulations of the LGM climate.