Xin-Zhong Liang

A coupled simulation-optimization with reanalysis-based short-term weather forecasts from a regional climate model (RCM) is proposed to optimize an irrigation scheduling problem. Using different physical configurations of the climate extension of a weather research and forecasting model (CWRF) that is driven by national atmospheric model project reanalysis data, five ensemble outlooks of 15 consecutive daily forecasts have been generated during five different crop-growing seasons. Six daily climatic variables are forecasted, namely, rainfall, minimum temperature, maximum temperature, humidity, wind speed, and solar radiation. To correct the forecasts for any inherent bias, the quantile mapping method is applied to all six daily climatic variables. After bias correction, a skill assessment of the reanalysis-based RCM forecasts indicate that only the first three climatic variables are predicted with reliable accuracy; thus, average climatic means are used to replace the remaining three variables (humidity, wind speed, and solar radiation). The framework is applied to the Havana Lowlands region, Illinois, as a case study, and the value of forecasts is assessed against two baseline scenarios: no-rain forecast (a pessimistic case) and average climatology (a normal case). Using reanalysis-based RCM forecasts to guide farmers’ irrigation decisions could yield about 1–3% in expected profit gain and 4–6% in water reduction when compared to the no-rain forecast scenario, and 1–6% in expected profit gain when compared to the average climatology scenario. This study is a first preliminary attempt to use an ensemble of weather simulations in the optimization of irrigation scheduling, and the developed framework can be used to incorporate operational forecasting once the reanalysis boundary is replaced by global weather forecasts.

This study uses the most recent simulations from all available fully coupled atmosphere–ocean general circulation models (CGCMs) to investigate whether the North American monsoon (NAM) precipitation seasonal–interannual variations are simulated and, if so, whether the key underlying physical mechanisms are correctly represented. This is facilitated by first identifying key centers where observed large-scale circulation fields and sea surface temperatures (SSTs) are significantly correlated with the NAM precipitation averages over the core region (central–northwest Mexico) and then examining if the modeled and observed patterns agree. Two new findings result from the analysis of observed NAM interannual variations. First, precipitation exhibits significantly high positive (negative) correlations with 200-hPa meridional wind centered to the northwest (southeast) of the core region in June and September (July and August). As such, wet conditions are associated with strong anomalous southerly upper-level flow on the northwest flank during the monsoon onset and retreat, but with anomalous northerly flow on the southeast flank, during the peak of the monsoon. They are often identified with a cyclonic circulation anomaly pattern over the central Great Plains for the July–August peak monsoon and, reversely, an anticyclonic anomaly pattern centered over the northern (southern) Great Plains for the June (September) transition. Second, wet NAM conditions in June and July are also connected with a SST pattern of positive anomalies in the eastern Pacific and negative anomalies in the Gulf of Mexico, acting to reduce the climatological mean gradient between the two oceans. This pattern suggests a possible surface gradient forcing that favors a westward extension of the North Atlantic subtropical ridge. These two observed features connected to the NAM serve as the metric for quantitative evaluation of the model performance in simulating the important NAM precipitation mechanisms. Out of 17 CGCMs, only the Meteorological Research Institute (MRI) model with a medium resolution consistently captures the observed NAM precipitation annual cycle (having a realistic amplitude and no phase shift) as well as interannual covariations with the planetary circulation patterns (having the correct sign and comparably high magnitude of correlation) throughout the summer. For the metric of correlations with 200-hPa meridional wind, there is general agreement among all CGCMs with observations for June and September. This may indicate that large-scale forcings dominate interannual variability for the monsoon onset and retreat, while variability of the peak of the monsoon in July and August may be largely influenced by local processes that are more challenging for CGCMs to resolve. For the metric of correlations with SSTs, good agreement is found only in June. These results suggest that the NAM precipitation interannual variability may likely be determined by large-scale circulation anomalies, while its predictability based on remote signals such as SSTs may not be sufficiently robust to be well captured by current CGCMs, with the exception of the June monsoon onset which is potentially more predictable.

A suite of eighteen simulations over the U.S. and Mexico, representing combinations of two mesoscale regional climate models (RCMs), two driving global general circulation models (GCMs), and the historical and four future anthropogenic forcings were intercompared. The RCMs' downscaling reduces significantly driving GCMs' present‐climate biases and narrows inter‐model differences in representing climate sensitivity and hence in simulating the present and future climates. Very high spatial pattern correlations of the RCM minus GCM differences in precipitation and surface temperature between the present and future climates indicate that major model present‐climate biases are systematically propagated into future‐climate projections at regional scales. The total impacts of the biases on trend projections also depend strongly on regions and cannot be linearly removed. The result suggests that the nested RCM‐GCM approach that offers skill enhancement in representing the present climate also likely provides higher credibility in downscaling the future climate projection.

Based on recent reviews of 11 physically based watershed models, the long-term continuous model soil and water assessment tool (SWAT) and the storm event dynamic watershed simulation model (DWSM) were selected to examine their hydrologic formulations, calibrate, and validate them on the 620 km2 watershed of the upper Little Wabash River at Effingham, Ill., and examine their compatibility and benefits of combining them into a more comprehensive and efficient model. Calibration and validation of the SWAT by comparing monthly simulated and observed flows and adjusting the model-assigned resulted in coefficients of determination and Nash–Sutcliffe coefficients for individual years and cumulatively for the calibration period (1995–1999) and for the entire simulation period (1995–2002) mostly above or near 0.50 with an exception of 0.05 and −0.27 , respectively, in 2001, relatively a dry year. Visual comparisons of the hydrographs showed SWAT’s weakness in predicting monthly peak flows (mostly underpredictions....

The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) simulations of U.S.–Mexico summer precipitation are quite sensitive to the choice of Grell or Kain–Fritsch convective parameterization. An ensemble based on these two parameterizations provides superior performance because distinct regions exist where each scheme complementarily captures certain observed signals. For the interannual anomaly, the ensemble provides the most significant improvement over the Rockies, Great Plains, and North American monsoon region. For the climate mean, the ensemble has the greatest impact on skill over the southeast United States and North American monsoon region, where CMM5 biases associated with the individual schemes are of opposite sign. Results are very sensitive to the specific methods used to generate the ensemble. While equal weighting of individual solutions provides a more skillful result overall, considerable further improvement is achieved when the weighting of individual solutions is optimized as a function of location.

Ultraviolet (UV) radiation is the source energy for tropospheric photolysis processes, while harmful for living organism of the earth. It is thus necessary to incorporate UV radiation for an integrated earth modeling system to predict interactions between climate, chemistry and ecosystem processed. The widely-used NCAR TUV (Tropospheric Ultraviolet and Visible) radiation model has been coupled with the state-of-the-art mesoscale CWRF (Climate extension of the Weather Research and Forecasting model) to predict the UV dependence of local climate conditions and its impacts on air quality and crop growth. The original TUV v4.2 has been significantly improved by (1) replacing the core radiation transfer solver, DISORT v1.1 with the latest v2.0beta; (2) adding a new aerosol scheme based on the Shettle (1989); (3) recoding the entire model to follow the CWRF F90 standard with dynamic memory allocation and modular design; and (4) developing a flexible interface for coupling with CWRF. Given the lack of detailed cloud information in observations, this study focuses on validation of the TUV module in a standalone mode against the USDA UV-B data under clear-sky conditions. To facilitate this, a cloud detection scheme based on Long and Ackerman (2000) is incorporated to distinguish clear versus cloudy sky conditions from the UV-B observations. The model input includes in situ measurements of the column ozone and total aerosol optical depth; TOMS retrievals of the column ozone (in case missing in situ) and climatologically surface reflectivity; and the NARR (North American Regional Analysis) meteorological conditions. The TUV results agree well with the UV-B measurements at 7 narrow spectral bands (300, 305, 311, 317, 325, 332, 368 nm).

A mesoscale model (MM5)–based regional climate model (CMM5) integration driven by the Parallel Climate Model (PCM), a fully coupled atmosphere‐ocean‐land‐ice general circulation model (GCM), for the present (1986–1995) summer season climate is first compared with observations to study the CMM5's downscaling skill and uncertainty over the United States. The results indicate that the CMM5, with its finer resolution (30 km) and more comprehensive physics, simulates the present U.S. climate more accurately than the driving PCM, especially for precipitation, including summer mean patterns, diurnal cycles, and daily frequency distributions. Hence the CMM5 downscaling provides a credible means to improve GCM climate simulations. A parallel CMM5 integration driven by the PCM future (2041–2050) projection is then analyzed to determine the downscaling impact on regional climate changes. It is shown that the CMM5 generates climate change patterns very different from those predicted by the driving PCM. A key difference is a summer “warming hole” over the central United States in the CMM5 relative to the PCM. This study shows that the CMM5 downscaling can significantly reduce GCM biases in simulating the present climate and that this improvement has important consequences for future projections of regional climate changes. For both the present and future climate simulations, the CMM5 results are sensitive to the cumulus parameterization, with strong regional dependence. The deficiency in representing convection is likely the major reason for the PCM's unrealistic simulation of U.S. precipitation patterns and perhaps also for its large warming in the central United States.

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (land-cover category, and static fractional vegetation cover and varying leaf-plus-stem-area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe.