CAQIMS - Components, Illinois State Water Survey

Climate and Atmospheric Science

Climate, Air Quality and Impact Modeling System (CAQIMS)

A Basis for Achieving Economic, Societal and Environmental Goals in Illinois

Xin-Zhong Liang
Department of Atmospheric Sciences and Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois

The CMM5 is a climate extension of the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model. It was found that the buffer zone treatment, including the physically-based domain choice and revised assimilation technique, critically determines the downscaling performance (Liang et al. 2001). It has been demonstrated that CMM5, with a horizontal grid spacing of 30 km, has considerable downscaling skill over the U.S., producing more realistic regional details and overall smaller biases than the driving reanalyses or GCM simulations (Liang et al., 2004a,b, 2006; Zhu and Liang 2005, 2007). The actual CMM5 performance, however, is region-dependent and sensitive to cumulus parameterization. A CMM5 ensemble based on the Grell (1993) and Kain and Fritsch (1993) parameterizations provides superior performance in downscaling U.S.-Mexico precipitation seasonal-interannual variations because distinct regions exist where each scheme complementarily captures certain observed signals (Liang et al. 2007). Recently, the CMM5 downscaling has been shown to reduce significantly driving GCMs’ present-climate biases and narrows inter-model differences in representing climate sensitivity and hence in simulating the present and future climates and also demonstrated how major model present-climate biases are systematically propagated into future-climate projections at regional scales (Liang et al. 2008). 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. Furthermore, the CMM5 downscaling reduces the GCM biases in heat wave threshold temperature by a factor of 2, suggesting a higher credibility in the future projections; all CMM5 simulations suggest that there is a high probability of heat waves of unprecedented severity by the end of the 21st Century if a high emissions path is followed (Kunkel et al. 2010).

1. Kunkel, K.E., X.-Z. Liang, and J. Zhu, 2010: Regional climate model projections and uncertainties of U.S. summer heat waves. J. Climate (accepted).

Regional climate model (RCM) simulations, driven by low- and high-climate sensitivity coupled general circulation models (CGCMs) under various future emissions scenarios, were compared to project changes in heat wave characteristics. The RCM downscaling reduces the CGCM biases in heat wave threshold temperature by a factor of 2, suggesting a higher credibility in the future projections. All of the RCM simulations suggest that there is a high probability of heat waves of unprecedented severity by the end of the 21st Century if a high emissions path is followed. In particular, the annual 3-day heat wave temperature increases generally by 3-8°C; the number of heat wave days increases by 30-60 per year over much of the western and southern U.S. with slightly smaller increases elsewhere; the variance spectra for intermediate, 3-7 days ( prolonged, 7-14 days) temperature extremes increase (decrease) in the central (western) U.S. If a lower emissions path is followed, the outcomes range from quite small changes to substantial increases. In all cases, the mean temperature climatological shift is the dominant change in heat wave characteristics, suggesting that adaptation and acclimatization could reduce impacts.

Figure
Figure. Projections of change in the average annual 3-day heat wave temperature (°C) (a,c) and the average annual number of heat wave days (b,d). Simulations include the RCM-P (a,b) for the high (A1Fi) and the RCM-H (c,d) for the moderately high (A2) emissions scenarios.

2. Liang, X.-Z., K.E. Kunkel, and A.N. Samel, 2001: Development of a regional climate model for U.S. Midwest applications. Part 1: Sensitivity to buffer zone treatment. J. Clim., 14, 4363-4378.

A regional climate model (RCM) is being developed for U.S. Midwest applications on the basis of the newly released Pennsylvania State University–NCAR Fifth-Generation Mesoscale Model (MM5), version 3.3. This study determines the optimal RCM domain and effective data assimilation technique to accurately integrate lateral boundary conditions (LBCs) across the buffer zones. The LBCs are constructed from both the NCEP– NCAR and ECMWF reanalyses to depict forcing uncertainties. The RCM domain was chosen to correctly represent the governing physical processes while minimizing LBC errors. Sensitivity experiments are conducted for the Midwest 1993 summer flood to investigate buffer zone treatment impacts on RCM performance. The results demonstrate the superiority of the buffer zone treatment that consists of the physically based domain choice and revised assimilation technique. Given this treatment, the RCM realistically simulates both temporal variations and spatial distributions in the major flood area (MFA). This success is identified with the accurate representation of both the midlatitude upper-level jet stream and Great Plains low-level jet (LLJ). The RCM reproduces different climate regimes, where observed rainfall was identified with the periodic (5 day) passage of midlatitude cyclones in June and persistent synoptic circulations in July. The model also correctly simulates the MFA rainfall diurnal cycle (with the peak amount at 0900 UTC), which follows the LLJ cycle by approximately 3 h. On the other hand, RCM performance is substantially reduced when the southern buffer zone extends to the Tropics, where large forcing errors exist. In particular, the RCM generates a weaker LLJ and, as a consequence, a decreased amount and delayed diurnal cycle of the MFA rainfall. In addition, the MM5 default LBC data assimilation technique produces considerable model biases, whereas the revised technique improves overall RCM performance and reduces sensitivity to domain size.

Figure
Figure. The RCM domain design. Interactions between domains (D1-3) can be one-way, two-way or completely deactivated. The GCM provides the RCM with LBCs in the buffer zones (shaded narrow outer edges). The horizontal resolution increases from a mother to a nested domain by an integer factor, and may be specified differently depending on a particular application.

3. Liang, X.-Z., L. Li, A. Dai, and K.E. Kunkel, 2004a: Regional climate model simulation of summer precipitation diurnal cycle over the United States. Geophys. Res. Lett., 31, L24208, doi:10.1029/2004GL021054.

MM5-based regional climate model (CMM5) simulations of the diurnal cycle of U.S. summer precipitation are found to be sensitive to the choice of cumulus parameterization schemes, whose skills are highly regime selective. The Grell scheme realistically simulates the nocturnal precipitation maxima and their associated eastward propagation of convective systems over the Great Plains where the diurnal timing of convection is controlled by the large-scale tropospheric forcing; whereas the Kain- Fritsch scheme is more accurate for the late afternoon peaks in the southeast U.S. where moist convection is governed by the near-surface forcing. In radar rainfall data and the simulation with the Grell scheme, another weaker eastward propagating diurnal signal is evident from the Appalachians to the east coast. The result demonstrates the importance of cumulus schemes and provides a realistic simulation of the central U.S. nocturnal precipitation maxima.

Figure
Figure 1. Spatial distributions of summer (June–August) rainfall diurnal cycles observed by (a) the rain gauge measurement (GAU) and (b) multi-sensor analysis (MUL), and simulated by the CMM5 using (c) Kain-Fritsch (MKF) and (d) Grell (MGR) cumulus schemes. Colors represent the normalized amplitude (i.e., in units of daily mean) while unit vectors denote the local solar time (LST) of the peak (phase clock).
Figure
Figure 2. Mean diurnal evolution (relative to LST) of the normalized rainfall averaged over 6 key regions: (a) the central Rockies, (b) central high Plains, (c) central Plains, (d) North American monsoon, (e) low level jet, and (f) southeast U.S., corresponding to the boxes in Figure 1a from left to right and top to bottom.

4. Liang, X.-Z., L. Li, K.E. Kunkel, M. Ting, and J.X.L. Wang, 2004b: Regional climate model simulation of U.S. precipitation during 1982-2002, Part 1: Annual cycle. J. Clim., 17, 3510-3528.

The fifth-generation PSU–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the U.S. precipitation annual cycle is evaluated with a 1982–2002 continuous baseline integration driven by the NCEP–DOE second Atmospheric Model Intercomparison Project (AMIP II) reanalysis. The causes for major model biases (differences from observations) are studied through supplementary seasonal sensitivity experiments with various driving lateral boundary conditions (LBCs) and physics representations. It is demonstrated that the CMM5 has a pronounced rainfall downscaling skill, producing more realistic regional details and overall smaller biases than the driving global reanalysis. The precipitation simulation is most skillful in the Northwest, where orographic forcing dominates throughout the year; in the Midwest, where mesoscale convective complexes prevail in summer; and in the central Great Plains, where nocturnal low-level jet and rainfall peaks occur in summer. The actual model skill, however, is masked by existing large LBC uncertainties over datapoor areas, especially over oceans. For example, winter dry biases in the Gulf States likely result from LBC errors in the south and east buffer zones. On the other hand, several important regional biases are identified with model physics deficiencies. In particular, summer dry biases in the North American monsoon region and along the east coast of the United States can be largely rectified by replacing the Grell with the Kain–Fritsch cumulus scheme. The latter scheme, however, yields excessive rainfall in the Atlantic Ocean but large deficits over the Midwest. The fall dry biases over the lower Mississippi River basin, common to all existing global and regional models, remain unexplained and the search for their responsible physical mechanisms will be challenging. In addition, the representation of cloud–radiation interaction is essential in determining the precipitation distribution and regional water recycling, for which the new scheme implemented in the CMM5 yields significant improvement.

Figure
Figure. Monthly 1982–2002 mean precipitation (mm day-1) variations averaged over the eight key regions for observations (OBS; thick solid), the CMM5 baseline integration (thick dashed), and the R-2 model output (thin dashed).

5. Liang, X.-Z., J. Pan, J. Zhu, K.E. Kunkel, J.X.L. Wang, and A. Dai, 2006: Regional climate model downscaling of the US. summer climate and future change. J. Geophys. Res., 111, D10108, doi:101029/2005JD006685.

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.

Figure
Figure. The future changes of summer mean (left) precipitation (mm d-1), (middle) surface air temperature (ºC), and (right) 850-hPa wind (m s-1) projected by the PCM, and downscaled by the CMM5 using the Grell (PGR) and Kain-Fritsch (PKF) cumulus scheme. For wind, colors represent the speed while unit vectors denote the direction.

6. Liang, X.-Z., M. Xu, K.E. Kunkel, G.A. Grell, and J. Kain, 2007: Regional climate model simulation of U.S.-Mexico summer precipitation using the optimal ensemble of two cumulus parameterizations. J. Clim., 20, 5201-5207.

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.

Figure
Figure. Frequency distributions of (a),(b) interannual anomaly correlation coefficients and (c),(d) rms errors with observations as simulated by CMM5 with the KF or GR cumulus scheme and their ensemble using an equal (EQ) or optimal (EC) weight. The optimization is solved with the unbiased (u), biased (b), or normalized (n) precipitation estimator by minimizing RMS; one exception is ECx, which is identical to ECu but minimizing (1-COR). Geographic distributions of interannual anomaly correlation coefficients (color scale on left bottom) of (e) KF and (f) ECu with observations; climate mean precipitation (mm day-1, color scale at right bottom) (g) observed and simulated by (h) KF and (i) ECb. All calculations are based on 1982–2002 summer (June, July, and August) monthly means at 30-km grid spacing; for all ECs, they are the predicted values using the leave-one-out cross-validation approach.

7. Liang, X.-Z., K.E. Kunkel, G.A. Meehl, R.G. Jones, and J.X.L. Wang, 2008: RCM downscaling analysis of GCM present climate biases propagation into future change projections. Geophys. Res. Lett., 35, L08709, doi:10.1029/2007GL032849.

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.

Figure
Figure. The precipitation (PR, mm day-1) and surface 2-m air temperature (TA, ºC) biases (from observations, left panels, a–d) of the driving GCMs (PCM, HAD) and differences (from the respective GCM) due to the RCM downscaling (PGR, HGR) in the present (1990s, middle panels, e–h) and future (2090s, right panels, i–l). Shown are summer averages of 10 years: 1991–2000 and A1Fi 2090–2099 for PCM, PGR, and 1980–1989 and A2 2090–2099 for HAD, HGR.

8. Zhu, J., and X.-Z. Liang, 2007: Regional climate model simulation of U.S. precipitation and surface air temperature during 1982-2002: Interannual variation. J. Climate, 20, 218-232.

The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the interannual variations of U.S. precipitation and surface air temperature during 1982–2002 is evaluated with a continuous baseline integration driven by the NCEP– Department of Energy (DOE) Second Atmospheric Model Intercomparison Project Reanalysis (R-2). It is demonstrated that the CMM5 has a pronounced downscaling skill for precipitation and temperature interannual variations. The EOF and correlation analyses illustrate that, for both quantities, the CMM5 captures the spatial pattern, temporal evolution, and circulation teleconnections much better than the R-2. In particular, the CMM5 more realistically simulates the precipitation pattern centered in the Northwest, where the representation of the orographic enhancement by the forced uplifting during winter (rainy season) is greatly improved over the R-2.

The downscaling skill, however, is sensitive to the cumulus parameterization. This sensitivity is studied by comparing the baseline with a branch summer integration replacing the Grell with the Kain–Fritsch cumulus scheme in the CMM5. The dominant EOF mode of the U.S. summer precipitation interannual variation, identified with the out-of-phase relationship between the Midwest and Southeast in observations, is reproduced more accurately by the Grell than the Kain–Fritsch scheme, which largely underestimates the variation in the Midwest. This pattern is associated with east–west movement of the Great Plains low-level jet (LLJ): a more western position corresponds to a stronger southerly flow bringing more moisture and heavier rainfall in the Midwest and less in the Southeast. The second EOF pattern, which describes the consistent variation over the southern part of the Midwest and the South in observations, is captured better by the Kain–Fritsch scheme than the Grell, whose pattern systematically shifts southward.

Figure
Figure. (a)–(c) The PC during 1982–2002 of the first three dominant EOF modes of summer precipitation. The respective geographic distributions (eigenvectors) of these EOF patterns (d)–(f) observed (OBS), downscaled by the (g)–(i) RGR and (j)–(l) RKF, and (m)–(o) simulated by the R-2.

9. Zhu, J., and X.-Z. Liang, 2005: Regional climate model simulation of U.S. soil temperature and moisture during 1982-2002. J. Geophys. Res., 110, D24110, doi:10.1029/2005JD006472.

The fifth-generation PSU-NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the U.S. soil temperature and soil moisture annual cycle and interannual variability is evaluated by comparing the 1982–2002 continuous integration driven by the NCEP-DOE AMIP II reanalysis (R-2) with observations, the R-2 derivatives and North American Land Data Assimilation System (NLDAS) products. For the annual cycle, the CMM5 produces more realistic regional details and overall smaller biases than the driving R-2 and NLDAS outputs. The CMM5 also faithfully simulates interannual variations of soil temperature over the central United States and soil moisture in Illinois and Iowa, where observational data are available. The existing CMM5 differences from observations in soil temperature (moisture) cannot be fully explained by model biases in surface air temperature (precipitation). Inconsistencies between measurements taken under short grass versus model representations beneath other land cover types may play an important role. In particular, such measurements overestimate soil temperature in summer and fall while generating a 1-month phase lead in the soil moisture annual cycle with respect to croplands in the model. The result emphasizes the need for more comprehensive study on model evaluation and bias understanding of soil temperature and soil moisture.

Figure
Figure. Monthly variations of precipitation (mm day-1) (a, d), soil moisture (mm) for top 1-m (b, e) and 2-m (c, f) layer for observations (OBS, thick solid) and simulation of the CMM5 (thick dashed), R-2 (thin dashed) and NLDAS/Mosaic (thin dot-dashed) averaged over Illinois (left) during 1984–2002 and Iowa (right) averaged during 1982–1994 except for the Mosaic during 1997–2002. Annual (April–November) means listed in Table 1 are removed from the respective cycles of soil moisture for both layers in Illinois (Iowa).

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