Abstract

Simple equations are developed to express regional climate changes for the twenty-first century and associated uncertainty in terms of the global temperature change (GTC) without a dependence on the underlying emission pathways. The equations are applied to regional temperature and precipitation changes over different regions of the world, and relevant parameters are calculated using the latest multimodel ensemble of global climate change simulations. Examples are also shown of how to use the equations to develop probability density functions (PDFs) of regional climate change based on PDFs of GTC. The main advantage of these equations is that they can be used to estimate regional changes from GTC obtained either from simple and intermediate complexity models or from target CO₂ stabilization concentrations.

Abstract

A Limited Area Model (LAM) is nested in a General Circulation Model (GCM) to simulate the January climate over the western United States. In the nesting procedure, the GCM output is used to provide the initial and lateral atmospheric boundary conditions necessary to drive the LAM. In this approach, the GCM is used to simulate realistic large-scale atmospheric behavior over an area of interest and the LAM to describe the effect of local, sub-GCM grid scale forcings (such as those induced by the complex western United States topography) on regional patterns of climatic variables. Two versions of the National Center for Atmospheric Research (NCAR) Community Climate Model [the seasonal CCM1 at 4.5° × 7.5° (R15) and 2.89° × 2.89° (T42) latitude-longitude resolution] are used to drive a version of the Pennsylvania State University/NCAR mesoscale model (MM4 at 60 km resolution), which includes sophisticated soil hydrology calculations. The CCM1 large-scale January climatology over the region is analyzed first. Comparison with large-scale observations shows that geopotential height, zonal wind, temperature, relative humidity, cloudiness, precipitation and storm frequencies over the western United States and adjacent oceans are realistically simulated by both the T42 and R15 models. The T42 model, however, reproduces storm frequencies and strength and position of the jet stream better than the R15 model. A number of month-long January simulations were performed using both the R15 and T42 model outputs to drive the MM4. The large-scale average circulations over the western United States simulated by the nested MM4 are not substantially different from those of the driving CCM1, both when outputs from the R15 and T42 versions are used to drive the MM4. Owing to the more realistic topography in the MM4, the nested model system produces better regional detail of precipitation and temperature distribution than the CCM1 alone. Temperature and precipitation means, as well as frequencies of daily precipitation intensifies simulated by the nested MM4, compare well with high resolution observations, particularly in their spatial distribution. Also discussed am results of regional snow cover, cloudiness, and soil hydrology calculations included in the MM4.

Abstract

This paper investigates the effect of different physics parameterizations on summertime precipitation as simulated by the Pennsylvania State University-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM4). The period of simulation is July 1979. The control simulation is carried out with a standard version of the MM4 including a simplified Kuo scheme, a bulk boundary-layer representation, and a force-restore scheme for ground temperature calculation. The parameterizations tested are a modification of the standard MM4 Kuo scheme, which imposes storage and slow release of condensation heat, an explicit moisture scheme, a version of the Arakawa-Schubert scheme, and a relatively sophisticated hydrology package. The standard MM4 strongly overestimates precipitation over mountainous terrain and, in particular, it produces an excessively large number of gridpoint precipitation events in excess of several centimeters (“numerical point storms” or NPS's). These are due to intense vertical motions maintained by large and localized condensation heat release. Both the explicit moisture scheme and the modified Kuo scheme reduce precipitation and number of NPS occurrences, leading to an improvement of the overall precipitation simulation skill of the model. Compared to the Kuo scheme, the Arakawa-Schubert scheme produces less convective precipitation, but still overestimates total precipitation and number of NPS occurrences due to interactions with the resolvable-scale precipitation processes. The inclusion of the enhanced surface physics formulations significantly affects the simulation of surface fluxes, temperatures, and precipitation. Compared to previous wintertime precipitation simulations, the present summertime precipitation results show generally higher biases, lower threat scores, and greater sensitivity to physics parameterizations and local moisture sources.

Abstract

In this paper, a theoretical framework is described for the representation of surface heterogeneity within complex biophysical surface schemes for use in climate models. The methodology adopts aspects of the mosaic approach and the statistical–dynamical approach. A grid cell is subdivided into fractional areas covered by basic surface types, that is, vegetated, bare soil, snow-covered, and impermeable surfaces, which separately exchange momentum, energy, and water vapor with the overlying atmosphere. Fractional precipitation areas within a grid box, and fractional rainfall and snowfall areas within the precipitation area, can also be specified. Within each surface type, heterogeneity is described by assuming that surface temperatures and soil water content follow continuous analytical probability density functions (PDFs) and by integrating relevant nonlinear terms over the appropriate PDF. Linear and symmetric PDFs are chosen since they allow ready analytical partial and full integration. This heterogeneity representation is implemented within the framework of a surface package, including a multilayer soil model, a one-layer vegetation model, a multilayer snow model, a two-layer impermeable surface model, and a surface hydrology model. The companion paper by Giorgi describes an extensive set of experiments carried out to validate the model and assess its sensitivity to relevant parameters.

Abstract

This paper discusses a series of sensitivity experiments aimed at testing the surface heterogeneity representation proposed in the companion paper by Giorgi. When driven by observed climatic forcings at three locations and run in point mode, the model shows good performance in reproducing observed surface fluxes. The temperature heterogeneity representation mostly affects the process of snow formation and, therefore, the winter and spring energy and water budgets. The soil water heterogeneity primarily influences the processes of soil water movement and runoff generation, thereby modifying the surface hydrologic budget. In addition, the heterogeneous model results compare reasonably well with aggregated results from point-mode experiments. Model sensitivity to the presence of impermeable surface fractional cover, fractional precipitation area, and a crude delayed runoff formulation is also discussed. As a next phase of model evaluation, it is planned to include this heterogeneity representation within a regional climate model and assess its effect on atmospheric circulations.

Abstract

Recent results show disagreement between global and limited-area models as to the role of soil moisture feedback during the summer of 1993 in the central United States. July precipitation totals increase by 50% in the European Centre for Medium-Range Weather Forecasts global model when soil moisture is initialized “wet,” but two separate regional modeling groups [University of Utah Limited Area Model group and National Center for Atmospheric Research Regional Climate Model (RegCM) group] have found very different responses to soil moisture, indicating that drier soil moisture conditions might actually lead to increased precipitation via an increase in convective instability and an enhancement of the low-level jet from the Gulf of Mexico.

To further evaluate the sensitivity results of RegCM in this context, a new suite of simulations, driven by analyses of observations for May–July of 1988 and 1993 is performed. The model domain is larger than in the previous experiments and the sensitivity of predicted seasonal rainfall to “wet” and “dry” initial soil moisture is analyzed. In comparing the new simulations with the earlier results, it is found that the simulation of seasonal precipitation as well as its sensitivity to initial soil moisture are affected by domain size and location of the lateral boundaries in both the 1988 and 1993 experiments. The smaller domain captures observed precipitation better in the upper Mississippi basin; however, the sensitivity of precipitation to initial soil moisture appears to be more realistic in the larger domain. While the lateral boundary forcing in the small domain experiments constrains the model to a better overall simulation, it also yields an unrealistic response to internal forcings, which are not consistent with the applied large-scale forcing. These results demonstrate that the domain of a regional climate model must be carefully selected for its specific application. In particular, domains much larger than the area of interest appear to be needed for studies of sensitivity to internal forcings.

Abstract

The “reliability ensemble averaging” (REA) method for calculating average, uncertainty range, and a measure of reliability of simulated climate changes at the subcontinental scale from ensembles of different atmosphere–ocean general circulation model (AOGCM) simulations is introduced. The method takes into account two “reliability criteria”: the performance of the model in reproducing present-day climate (“model performance” criterion) and the convergence of the simulated changes across models (“model convergence” criterion). The REA method is applied to mean seasonal temperature and precipitation changes for the late decades of the twenty-first century, over 22 land regions of the world, as simulated by a recent set of nine AOGCM experiments for two anthropogenic emission scenarios (the A2 and B2 scenarios of the Intergovernmental Panel for Climate Change). In the A2 scenario the REA average regional temperature changes vary between about 2 and 7 K across regions and they are all outside the estimated natural variability. The uncertainty range around the REA average change as measured by ± the REA root-mean-square difference (rmsd) varies between 1 and 4 K across regions and the reliability is mostly between 0.2 and 0.8 (on a scale from 0 to 1). For precipitation, about half of the regional REA average changes, both positive and negative, are outside the estimated natural variability and they vary between about −25% and +30% (in units of percent of present-day precipitation). The uncertainty range around these changes (± rmsd) varies mostly between about 10% and 30% and the corresponding reliability varies widely across regions. The simulated changes for the B2 scenario show a high level of coherency with those for the A2 scenario. Compared to simpler approaches, the REA method allows a reduction of the uncertainty range in the simulated changes by minimizing the influence of “outlier” or poorly performing models. The method also produces a quantitative measure of reliability that shows that both criteria need to be met by the simulations in order to increase the overall reliability of the simulated changes.

Abstract

A mosaic-type parameterization of subgrid-scale topography and land use is implemented within the framework of a regional climate model, and its effects on a multiseasonal simulation over the European region are tested, with focus on the Alpine subregion. The parameterization adopts a regular finescale surface subgrid for each coarse model grid cell. Meteorological variables are disaggregated from the coarse grid to the fine grid, land surface calculations are then performed separately for each subgrid cell, and surface fluxes are reaggregated onto the coarse grid cell for input to the atmospheric model. The primary effects of the subgrid surface scheme are 1) an improvement of the finescale structure and overall simulation of surface air temperature over complex terrain, and 2) a more realistic simulation of snow as driven by the complex terrain features. The subgrid scheme also affects the warm season simulation through feedbacks between precipitation and the surface hydrology. The primary aspect of the scheme that has an impact on the model is the subgrid disaggregation of temperature and water vapor, which is based on the difference between the topographical elevation of the subgrid and corresponding coarse grid cells. The mosaic-type approach presented here with suitable meteorological disaggregation techniques and with the possible addition of a parameterization of subgrid-scale effects on precipitation can provide an effective tool to bridge the scaling gap between climate models and surface hydrological processes.

Abstract

As part of an ongoing study of the regional climate and hydrology of the southwestern United States, in this paper we investigate the systematic biases of two versions of the PSU/NCAR mesoscale model (MM4). These are a standard version and one that includes a more detailed treatment of radiative transfer, surface physics, and soil hydrology. We simulated the period 1–30 January 1979, in which nine Pacific storms moved across the western United States. Results from both model versions are compared to the large scale analysis used to provide initial and lateral boundary conditions. Both models show a lower tropospheric cold bias of 1–3 K near the surface over land and an upper tropospheric warm bias of less than 1 K, which suggest high model stability and reduced vertical mixing. The model atmospheres are wetter than that of the analysis, particularly in the lower troposphere and over the ocean. The wind magnitude bias is positive near the surface (∼1.5–3 m s⁻¹), negative in the upper troposphere (∼−1.5 m s⁻¹) and positive above the jet-level (∼3 m s⁻¹). The wind direction bias is small throughout the model atmospheres except at the top model layer near 10 mb. These results indicate that the model evaporation and nighttime land surface sensible heat fluxes are larger compared to the analysis, while the daytime sensible heat fluxes and surface wind drag are smaller. The biases are generally smaller in the midtroposphere than in the lower troposphere and in the stratosphere. In general, both models capture most regional features of the orographic forcing of precipitation by the western United States topography quite well. Compared to station data, precipitation amounts tend to be overpredicted. Daily precipitation threat scores for various precipitation thresholds vary between 0.315 and 0.385. The threat scores for the 30-day precipitation, more indicative of the model's ability to simulate climatological precipitation averages, are higher, ⩾0.8 for light precipitation to ∼0.5 for moderate to heavy precipitation. Snow depths predicted by the augmented model also show realistic regional features. In general, the inclusion of the new physics package did not strongly affect precipitation prediction or the temperature, moisture, and wind midtropospheric biases. In the boundary layer over land, however, the augmented model was significantly colder and drier than the standard model due to larger nighttime surface sensible heat fluxes and lower evaporation rates. The regional hydrologic budgets simulated by the soil hydrology package of the augmented MM4 appear realistic in many respects, although verification is difficult at the present model resolution.

Abstract

This paper examines the sensitivity of a regional atmospheric model to horizontal resolution and topographic forcing. The model is run for January and July month-long simulations over the European region at gridpoint spacings ranging from 200 to 50 km and with various topography configurations. Different precipitation parameterizations of complexity and structure similar to those used in present-day climate models are tested. When averaged over the whole continent, the precipitation amounts are more sensitive to gridpoint spacing than to topographic forcing. Topography mostly contributes to spatially redistributing precipitation, and its effect is dominant only over subregions characterized by complex topographical features (e.g., the Alps). Other variables, such as cloudiness, surface energy fluxes, and precipitation intensity distributions are also sensitive to resolution. Finally, simulated precipitation amounts vary with the parameterization scheme used at all resolutions. These results have important implications for climate modeling. They suggest that, when running a model on a wide range of horizontal resolutions, such as in a variable gridpoint spacing configuration, in “time slice” mode, or within a nested modeling system, the effects of physical forcings (e.g., topography) can be strongly modulated by the direct sensitivity of the model physics formulations to resolution.