Current Climate Simulations Research Articles

Comparison of HadCM3, CSIRO Mk3 and GFDL CM2.1 in Prediction the Climate Change in Taleghan River Basin

Climate change is a complex and long-term global atmospheric-oceanic phenomenon which can be influenced by natural factors such as volcanoes, solar, oceans and atmosphere activities which they have interactions between or may be as a result of human activities. Atmospheric general circulation models are developed for simulation of current climate of the earth and are able to predict the earth's future climate change. In this paper, the performance of GFDL CM2.1, CSIRO Mk3 and HadCM3 AOGCMs were assessed and evaluated in the study of the climate change effects on temperature and precipitation in Taleghan basin. The results show that HadCM3 model in comparison with CSIRO Mk3 and GFDL CM2.1 models has indicated the better performance in this region.

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

AbstractConventional bias correction is usually applied on a grid‐by‐grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two‐step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back‐transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA‐based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

Significant Contributions of Volcanic Aerosols to Decadal Changes in the Stratospheric Circulation

AbstractThe stratospheric circulation is an important element of climate as it determines the concentration of radiatively active species like water vapor and aerosol above the tropopause. Climate models predict that increasing greenhouse gas levels speed up the stratospheric circulation. However, these results have been challenged by observational estimates of the circulation strength, constituting an uncertainty in current climate simulations. Here, we quantify the effect of volcanic aerosol on the stratospheric circulation focusing on the Mount Pinatubo eruption and discussing further the minor extratropical volcanic eruptions after 2008. We show that the observed pattern of decadal circulation change over the past decades is substantially driven by volcanic aerosol injections. Thus, climate model simulations need to realistically take into account the effect of volcanic eruptions, including the minor eruptions after 2008, for a reliable reproduction of observed stratospheric circulation changes.

Projected Response of Low‐Level Convergence and Associated Precipitation to Greenhouse Warming

AbstractThe parameterization of convection in climate models is a large source of uncertainty in projecting future precipitation changes. Here an objective method to identify organized low‐level convergence lines has been used to better understand how atmospheric convection is organized and projected to change, as low‐level convergence plays an important role in the processes leading to precipitation. The frequency and strength of convergence lines over both ocean and land in current climate simulations is too low compared to reanalysis data. Projections show a further reduction in the frequency and strength of convergence lines over the midlatitudes. In the tropics, the largest changes in frequency are generally associated with shifts in major low‐latitude convergence zones, consistent with changes in the precipitation. Further, examining convergence lines when in the presence or absence of precipitation results in large spatial contrasts, providing a better understanding of regional changes in terms of thermodynamic and dynamic effects.

An Analysis of Regional Climate Simulations for Western Australia’s Wine Regions—Model Evaluation and Future Climate Projections

AbstractThe Weather Research and Forecasting (WRF) Model is evaluated as a regional climate model for the simulation of climate indices that are relevant to viticulture in Western Australia’s wine regions at a 5-km resolution under current and future climate. WRF is driven with ERA-Interim reanalysis for the current climate and three global climate models (GCMs) for both current and future climate. The focus of the analysis is on a selection of climate indices that are commonly used in climate–viticulture research. Simulations of current climate are evaluated against an observational dataset to quantify model errors over the 1981–2010 period. Changes to the indices under future climate based on the SRES A2 emissions scenario are then assessed through an analysis of future (2030–59) minus present (1970–99) climate. Results show that when WRF is driven with ERA-Interim there is generally good agreement with observations for all of the indices although there is a noticeable negative bias for the simulation of precipitation. The results for the GCM-forced simulations were less consistent. Namely, while the GCM-forced simulations performed reasonably well for the temperature indices, all simulations performed inconsistently for the precipitation index. Climate projections showed significant warming for both of the temperature indices and indicated potential risks to Western Australia’s wine growing regions under future climate, particularly in the north. There was disagreement between simulations with regard to the projections of the precipitation indices and hence greater uncertainty as to how these will be characterized under future climate.

Quantifying uncertainties on regional sea level change induced by multidecadal intrinsic oceanic variability

AbstractA global eddy‐permitting (1/4° resolution) ocean general circulation model is shown to spontaneously generate intrinsic oceanic variability (IOV) up to multidecadal timescales (T > 20 years) under a repeated seasonal atmospheric forcing. In eddy‐active regions, the signature of this multidecadal eddy‐driven IOV on sea level is substantial, weakly autocorrelated, and is comparable to (and may clearly exceed) the corresponding signature of internal climate variability (ICV) produced by current coupled climate models—whose laminar ocean components may strongly underestimate IOV. Deriving sea level trends from finite‐length time series in eddy‐active regions yields uncertainties induced by this multidecadal IOV, which are of the same order of magnitude as those due to ICV. A white noise model is proposed to approximate the low‐frequency tail of the IOV spectra and could be used to update ICV estimates from current climate simulations and projections.

Projections of future deterioration in UK river quality are hampered by climatic uncertainty under extreme conditions

ABSTRACTA modelling study was undertaken to quantify effects that the climate likely to prevail in the 2050s might have on water quality in two contrasting UK rivers. In so doing, it pinpointed the extent to which time series of climate model output, for some variables derived following bias correction, are fit for purpose when used as a basis for projecting future water quality. Working at daily time step, the method involved linking regional climate model (HadRM3-PPE) projections, Future Flows Hydrology (rainfall–runoff modelling) and the QUESTOR river network water quality model. In the River Thames, the number of days when temperature, dissolved oxygen, biochemical oxygen demand and phytoplankton exceeded undesirable values (>25°C, <6 mg L−1, >4 mg L−1 and >0.03 mg L−1, respectively) was estimated to increase by 4.1–26.7 days per year. The changes do not reflect impacts of any possible change in land use or land management. In the River Ure, smaller increases in occurrence of undesirable water quality are likely to occur in the future (by 1.0–11.5 days per year) and some scenarios suggested no change. Results from 11 scenarios of the hydroclimatic inputs revealed considerable uncertainty around the levels of change, which prompted analysis of the sensitivity of the QUESTOR model to simulations of current climate and hydrology. Hydrological model errors were deemed of less significance than those associated with the derivation and downscaling of driving climatic variables (rainfall, air temperature and solar radiation). Errors associated with incomplete understanding of river water quality interactions with the aquatic ecosystem were found likely to be more substantial than those associated with hydrology, but less than those related to climate model inputs. These errors are largely a manifestation of uncertainty concerning the extent to which phytoplankton biomass is controlled by invertebrate grazers, particularly in mid-summer; and the degree to which this varies from year to year. The quality of data from climate models for generating flows and defining driving variables at the extremes of their distributions has been highlighted as the major source of uncertainty in water quality model outputs.EDITOR A. Castellarin; ASSOCIATE EDITOR X. Fang

Atmosphere-Ocean Coupling Effect on Intense Tropical Cyclone Distribution and its Future Change with 60 km-AOGCM

Atmosphere-ocean coupling effect on the frequency distribution of tropical cyclones (TCs) and its future change is studied using an atmosphere and ocean coupled general circulation model (AOGCM). In the present climate simulation, the atmosphere-ocean coupling in the AOGCM improves biases in the AGCM such as the poleward shift of the maximum of intense TC distribution in the Northern Hemisphere and too many intense TCs in the Southern Hemisphere. Particularly, subsurface cold water plays a key role to reduce these AGCM biases of intense TC distribution. Besides, the future change of intense TC distribution is significantly different between AOGCM and AGCM despite the same monthly SST. In the north Atlantic, subsurface warming causes larger increase in frequency of intense TCs in AOGCM than that in AGCM. Such subsurface warming in AOGCM also acts to alter large decrease of intense TC in AGCM to no significant change in AOGCM over the southwestern Indian Ocean. These results suggest that atmosphere-ocean coupling characterized by subsurface oceanic structure is responsible for more realistic intense TC distribution in the current climate simulation and gives significant impacts on its future projection.

The roles of bias-correction and resolution in regional climate simulations of summer extremes

The suitability of dynamical downscaling in producing high-resolution climate scenarios for impact assessments is limited by the quality of the driving data and regional climate model (RCM) error. Multiple RCMs driven by a single global climate model simulation of current climate show a reduction in bias compared to the driving data, and the remaining bias motivates exploration of bias correction and higher RCM resolution. The merits of bias correcting the mean climate of the driving data (boundary bias correction) versus bias correcting the mean of the RCM output data are explored and compared to model resolution sensitivity. This analysis focuses on the simulation of summer temperature and precipitation extremes using a single RCM, the Nested Regional Climate Model (NRCM). The NRCM has a general cool bias for hot and cold extremes, a wet bias for wet extremes and a dry bias for dry extremes. Both bias corrections generally reduced the bias and overall error with some indication that boundary bias correction provided greater benefits than bias correcting the mean of the RCM output data, particularly for precipitation. High resolution tended not to lead to further improvements, though further work is needed using multiple resolution evaluation datasets and convection permitting resolution simulations to comprehensively assess the value of high resolution.

Thermodynamic and dynamic contributions to future changes in regional precipitation variance: focus on the Southeastern United States

The frequency and severity of extreme events are tightly associated with the variance of precipitation. As climate warms, the acceleration in hydrological cycle is likely to enhance the variance of precipitation across the globe. However, due to the lack of an effective analysis method, the mechanisms responsible for the changes of precipitation variance are poorly understood, especially on regional scales. Our study fills this gap by formulating a variance partition algorithm, which explicitly quantifies the contributions of atmospheric thermodynamics (specific humidity) and dynamics (wind) to the changes in regional-scale precipitation variance. Taking Southeastern (SE) United States (US) summer precipitation as an example, the algorithm is applied to the simulations of current and future climate by phase 5 of Coupled Model Intercomparison Project (CMIP5) models. The analysis suggests that compared to observations, most CMIP5 models (~60 %) tend to underestimate the summer precipitation variance over the SE US during the 1950–1999, primarily due to the errors in the modeled dynamic processes (i.e. large-scale circulation). Among the 18 CMIP5 models analyzed in this study, six of them reasonably simulate SE US summer precipitation variance in the twentieth century and the underlying physical processes; these models are thus applied for mechanistic study of future changes in SE US summer precipitation variance. In the future, the six models collectively project an intensification of SE US summer precipitation variance, resulting from the combined effects of atmospheric thermodynamics and dynamics. Between them, the latter plays a more important role. Specifically, thermodynamics results in more frequent and intensified wet summers, but does not contribute to the projected increase in the frequency and intensity of dry summers. In contrast, atmospheric dynamics explains the projected enhancement in both wet and dry summers, indicating its importance in understanding future climate change over the SE US. The results suggest that the intensified SE US summer precipitation variance is not a purely thermodynamic response to greenhouse gases forcing, and cannot be explained without the contribution of atmospheric dynamics. Our analysis provides important insights to understand the mechanisms of SE US summer precipitation variance change. The algorithm formulated in this study can be easily applied to other regions and seasons to systematically explore the mechanisms responsible for the changes in precipitation extremes in a warming climate.

Future Changes in Surface Runoff over Korea Projected by a Regional Climate Model under A1B Scenario

This study assesses future change of surface runoff due to climate change over Korea using a regional climate model (RCM), namely, the Global/Regional Integrated Model System (GRIMs), Regional Model Program (RMP). The RMP is forced by future climate scenario, namely, A1B of Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The RMP satisfactorily reproduces the observed seasonal mean and variation of surface runoff for the current climate simulation. The distribution of monsoonal precipitation-related runoff is adequately captured by the RMP. In the future (2040–2070) simulation, it is shown that the increasing trend of temperature has significant impacts on the intra-annual runoff variation. The variability of runoff is increased in summer; moreover, the strengthened possibility of extreme occurrence is detected in the future climate. This study indicates that future climate projection, including surface runoff and its variability over Korea, can be adequately addressed on the RMP testbed. Furthermore, this study reflects that global warming affects local hydrological cycle by changing major water budget components. This study adduces that the importance of runoff should not be overlooked in regional climate studies, and more elaborate presentation of fresh-water cycle is needed to close hydrological circulation in RCMs.

Will Future Climate Favor More Erratic Wildfires in the Western United States?

AbstractWildfires that occurred over the western United States during August 2012 were fewer in number but larger in size when compared with all other Augusts in the twenty-first century. This unique characteristic, along with the tremendous property damage and potential loss of life that occur with large wildfires with erratic behavior, raised the question of whether future climate will favor rapid wildfire growth so that similar wildfire activity may become more frequent as climate changes. This study addresses this question by examining differences in the climatological distribution of the Haines index (HI) between the current and projected future climate over the western United States. The HI, ranging from 2 to 6, was designed to characterize dry, unstable air in the lower atmosphere that may contribute to erratic or extreme fire behavior. A shift in HI distribution from low values (2 and 3) to higher values (5 and 6) would indicate an increased risk for rapid wildfire growth and spread. Distributions of Haines index are calculated from simulations of current (1971–2000) and future (2041–70) climate using multiple regional climate models in the North American Regional Climate Change Assessment Program. Despite some differences among the projections, the simulations indicate that there may be not only more days but also more consecutive days with HI ≥ 5 during August in the future. This result suggests that future atmospheric environments will be more conducive to erratic wildfires in the mountainous regions of the western United States.

Potential for added value to downscaled climate extremes over Korea by increased resolution of a regional climate model

This study estimates the potential for added value in dynamical downscaling by increasing the spatial resolution of the regional climate model (RCM) over Korea. The Global/Regional Integrated Model System—Regional Model Program with two different resolutions is employed as the RCM. Large-scale forcing is given by a historical simulation of a global climate model, namely the Hadley Center Global Environmental Model version 2. As a standard procedure, the reproducibility of the RCM results for the present climate is evaluated against the reanalysis and observation datasets. It is confirmed that the RCM adequately reproduces the major characteristics of the observed atmospheric conditions and the increased resolution of the RCM contributes to the improvement of simulated surface variables including precipitation and temperature. For the added-value assessment, the interannual and daily variabilities of precipitation, temperature are compared between the different resolution RCM experiments. It is distinctly shown that variabilities are additionally described as the spatial resolution becomes higher. The increased resolution also contributes to capture the extreme weather conditions, such as heavy rainfall events and sweltering days. The enhanced added value is more evident for the precipitation than for the temperature, which stands for a usefulness of the high-resolution RCM especially for diagnosing potential hazard related to heavy rainfall. The results of this study assure the effectiveness of increasing spatial resolution of the RCM for detecting climate extremes and also provide credibility to the current climate simulation for future projection studies.

Towards Assessing NARCCAP Regional Climate Model Credibility for the North American Monsoon: Current Climate Simulations

AbstractThe authors examine 17 dynamically downscaled simulations produced as part of the North American Regional Climate Change Assessment Program (NARCCAP) for their skill in reproducing the North American monsoon system. The focus is on precipitation and the drivers behind the precipitation biases seen in the simulations of the current climate. Thus, a process-based approach to the question of model fidelity is taken in order to help assess confidence in this suite of simulations.The results show that the regional climate models (RCMs) forced with a reanalysis product and atmosphere-only global climate model (AGCM) time-slice simulations perform reasonably well over the core Mexican and southwest United States regions. Some of the dynamically downscaled simulations do, however, have strong dry biases in Arizona that are related to their inability to develop credible monsoon flow structure over the Gulf of California. When forced with different atmosphere–ocean coupled global climate models (AOGCMs) for the current period, the skill of the RCMs subdivides largely by the skill of the forcing or “parent” AOGCM. How the inherited biases affect the RCM simulations is investigated. While it is clear that the AOGCMs have a large influence on the RCMs, the authors also demonstrate where the regional models add value to the simulations and discuss the differential credibility of the six RCMs (17 total simulations), two AGCM time slices, and four AOGCMs examined herein. It is found that in-depth analysis of parent GCM and RCM scenarios can identify a meaningful subset of models that can produce credible simulations of the North American monsoon precipitation.

High dimensional decision dilemmas in climate models

Abstract. An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Building upon on the smoothness of the response of an atmospheric circulation model (AGCM) to changes of four adjustable parameters, Neelin et al. (2010) used a quadratic metamodel to objectively calibrate the AGCM. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.

Regional Interdependency of Precipitation Indices across Denmark in Two Ensembles of High-Resolution RCMs

Abstract Outputs from climate models are the primary data source in climate change impact studies. However, their interpretation is not straightforward. In recent years, several methods have been developed in order to quantify the uncertainty in climate projections. One of the common assumptions in almost all these methods is that the climate models are independent. This study addresses the validity of this assumption for two ensembles of regional climate models (RCMs) from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project based on the land cells covering Denmark. Daily precipitation indices from an ensemble of RCMs driven by the 40-yr ECMWF Re-Analysis (ERA-40) and an ensemble of the same RCMs driven by different general circulation models (GCMs) are analyzed. Two different methods are used to estimate the amount of independent information in the ensembles. These are based on different statistical properties of a measure of climate model error. Additionally, a hierarchical cluster analysis is carried out. Regardless of the method used, the effective number of RCMs is smaller than the total number of RCMs. The estimated effective number of RCMs varies depending on the method and precipitation index considered. The results also show that the main cause of interdependency in the ensemble is the use of the same RCM driven by different GCMs. This study shows that the precipitation outputs from the RCMs in the ENSEMBLES project cannot be considered independent. If the interdependency between RCMs is not taken into account, the uncertainty in the RCM simulations of current regional climate may be underestimated. This will in turn lead to an underestimation of the uncertainty in future precipitation projections.

Modeling and understanding persistence of climate variability

In this study, two parsimonious statistical representations of climate variability on interannual to multidecadal timescales are compared: the short‐memory first order autoregressive representation (AR1) and the long‐memory “power law” representation. Parameters for each statistical representation are fitted to observed surface air temperature at each spatial point. The parameter estimates from observations are found in general to be captured credibly in the Coupled Model Intercomparison Project 3 (CMIP3) simulations. The power law representation provides an upper bound and the AR1 representation provides a lower bound on persistence as measured by the lag‐one autocorrelation. Both representations fit the data equally well according to goodness‐of‐fit‐tests. Comparing simulations with and without external radiative forcings shows that anthropogenic forcing has little effect on the measures of persistence considered (for detrended data). Given that local interannual to multi decadal climate variability appears to be more persistent than an AR1 process and less persistent than a power law process, it is concluded that both representations are potentially useful for statistical applications. It is also concluded that current climate simulations can well represent interannual to multidecadal internal climate persistence in the absence of natural and anthropogenic radiative forcing, at least to within observational uncertainty.

Assessment of climate change in Europe from an ensemble of regional climate models by the use of Köppen–Trewartha classification

ABSTRACTThrough the use of the climatic classification of Köppen–Trewartha (K‐T), the ability to reproduce the current climate of Europe has been shown for an ensemble of 15 regional climate model simulations nested in six global climate models. Depending on the simulation, between 55.4 and 81.3% of the grid points are in agreement with observations regarding the location of climate types in current climate simulations (1971–2000). In this respect, the result of the ensemble of 15 simulations is better than that of any individual model, with 83.5% of the grid points in agreement with observations. K‐T classification has also been used to analyse the projected climate change over the 21st century under the SRES‐A1B emissions scenario. It was found that 22.3% of the grid points in the domain change their climate by the period 2021–2050 compared to current climate and 48.1% change by 2061–2090. The climate shifts affecting the biggest extensions are projected in Central Europe and Fennoscandia, but other smaller areas suffer more intense changes which potentially are more dangerous to vegetation and ecosystems. Generally, these changes occur at a sustained rate throughout the century, reaching speeds of up to 90 × 103 km2 decade−1 in the retreat or expansion of some climates.

Projections of mid-century summer air-quality for North America: effects of changes in climate and precursor emissions

Abstract. Ten year simulations of North American current and future air-quality were carried out using a regional air-quality model driven by a regional climate model, in turn driven by a general circulation model. Three separate summer scenarios were performed: a scenario representing the years 1997 to 2006, and two SRES A2 climate scenarios for the years 2041 to 2050. The first future climate scenario makes use of 2002 anthropogenic precursor emissions, and the second applied emissions scaling factors derived from the IPCC Representative Concentration Pathway 6 (RCP 6) scenario to estimate emissions for 2050 from existing 2020 projections. Ten-year averages of ozone and PM2.5 at North American monitoring network stations were used to evaluate the model's current chemical climatology. The model was found to have a similar performance for ozone as when driven by an operational weather forecast model. The PM2.5 predictions had larger negative biases, likely resulting from the absence of rainwater evaporation, and from sub-regional negative biases in the surface temperature fields, in the version of the climate model used here. The differences between the two future climate simulations and the current climate simulation were used to predict the changes to air-quality that might be expected in a future warmer climate, if anthropogenic precursor emissions remain constant at their current levels, versus if the RCP 6 emissions controls were adopted. Metrics of concentration, human health, and ecosystem damage were compared for the simulations. The scenario with future climate and current anthropogenic emissions resulted in worse air-quality than for current conditions – that is, the effect of climate-change alone, all other factors being similar, would be a worsening of air-quality. These effects are spatially inhomogeneous, with the magnitude and sign of the changes varying with region. The scenario with future climate and RCP 6 emissions for 2050 resulted in an improved air-quality, with decreases in key pollutant concentrations, in acute human mortality associated with air-pollution, and in sulphur and ozone deposition to the ecosystem. The positive outcomes of the RCP 6 emissions reductions were found to be of greater magnitude than the negative outcomes of climate change alone. The RCP 6 scenario however resulted in an increase in the deposition of nitrogen, as a result of increased ammonia emissions expected in that scenario, compared to current ammonia emissions levels. The results of the study raise the possibility that simultaneous reductions of greenhouse gases and air pollution precursors may further reduce air pollution levels, with the added benefits of an immediate reduction in the impacts of air pollution on human and ecosystem health. Further scenarios to investigate this possibility are therefore recommended.

Evaluation of regional climate simulations over the Great Lakes region driven by three global data sets

The performance of regional climate simulations is evaluated for the Great Lakes region. Three 10-year (1990–1999) current-climate simulations are performed using the MM5 regional climate model (RCM) with 36-km horizontal resolution. The simulations employed identical configuration and physical parameterizations, but different lateral boundary conditions and sea-surface temperatures derived from the NCEP Global Reanalysis and output from the CCSM3 and GISS general circulation models (GCMs). The simulation results are compared to the North American Regional Reanalysis (NARR). The three RCM simulations appeared to be more accurate in winter and least accurate in summer, and more accurate aloft than near the surface. The reanalysis-constrained simulation adequately captured the spatial distribution and seasonal cycle of the observed surface-air temperature and precipitation, but it produced consistently across all seasons a cold bias that is generally larger over the lakes than over land and a wet bias due to an overestimation of non-convective precipitation. The simulated seasonal cycle of moisture–flux convergence over the region was in very good agreement with NARR. The two GCM-driven runs adequately simulated the spatial and seasonal variation of temperature, but overestimated cold-season precipitation and underestimated summer precipitation, reversing the observed annual precipitation cycle. The GISS-driven run failed to simulate the prevailing low-level flow and moisture convergence patterns. All three RCM simulations successfully captured the impact of the Great Lakes on the region's climate, especially on winter precipitation, a significant improvement over coarse-resolution GCM simulations over the region.