1. Introduction
The Laurentian Great Lakes region holds one of the largest inland freshwater reserves. Their physical properties, including basin characteristics, thermal stratification, and lake hydrodynamics, can modulate the regional hydroclimate by altering atmospheric boundary layer processes such as wind patterns, heat fluxes, and lake-induced precipitation (Notaro et al. 2013). Equally, lake water resources, biological productivity, and ecosystems are highly sensitive to climatic and meteorological anomalies (Adrian et al. 2009; Fichot et al. 2019; Piccolroaz et al. 2015; Smits et al. 2020; Toffolon et al. 2020; Woolway et al. 2020; Zhong et al. 2016). Under future warming scenarios, lake properties and lake-effect processes can significantly deviate from their mean state, and the inland water bodies often show a stronger response to climate change; for example, past studies have found an increase of 2°–3°C in Lake Superior surface temperature over the 1970–2000 time period, which is roughly twice the regional atmospheric warming (Austin and Colman 2007, 2008; Van Cleave et al. 2014). Hanrahan et al. (2010) further linked the lake surface temperature rise to considerable increase in summertime evaporation rates, which induce variations in the lake water levels. The effects of these changes are not uniform in space and time, with notable differences between the shallow lakes (Lakes Erie and Ontario) and deep lakes (Lake Superior) due to seasonal variations in their response to warming conditions (Fichot et al. 2019; Kravtsov et al. 2018; Toffolon et al. 2020; Zhong et al. 2016).
The Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project (CMIP) model outputs can be used to assess climate warming impacts on atmospheric and hydrological processes, and to understand the seasonal and spatial variability in the model simulations of atmospheric moisture budget (Dagan et al. 2019; Hirota et al. 2016; Levang and Schmitt 2015). Arguably, precipitation is one of the most important moisture components; however, it is difficult to simulate in climate models and shows large spatiotemporal uncertainties in future simulations (Aloysius et al. 2016; Mehran et al. 2014). Over the Great Lakes region, X. Wang et al. (2016), using a regional climate ensemble, projected a 12.2% increase in the annual precipitation by the 2050s as compared to the 1961–90 mean, with spatial variability across the subbasins. Byun and Hamlet (2018) assessed CMIP5 model projections and found a region-wide increase in winter (December–February) precipitation which was more pronounced northwest of Lake Superior, while the summer (June–August) change had mixed patterns with areas of both increasing and decreasing magnitudes by the 2050s. Notaro et al. (2015), using two dynamically downscaled CMIP5 general circulation models (GCMs), found contrasting behavior in the two models by the mid-twenty-first century, where one model showed a higher increase in lake evaporation and moderate increase in precipitation leading to a decline in annual net basin supply (NBS), while the other model showed small rise in evaporation and a strong increase in precipitation leading to an overwhelming rise in NBS. Similarly, Mailhot et al. (2019), using regional climate models (RCMs), found an increase in overlake precipitation by 2100 in winter–spring and an increase in lake evaporation in summer, which produces seasonal differences in the NBS and consequent lake water levels.
For the Great Lakes region, precipitation is fed by both local and remote sources in the form of regional evapotranspiration and moisture influx respectively, with a notable role of the lakes in influencing and modulating these components (Minallah and Steiner 2021). However, many CMIP models do not adequately represent these inland water bodies, which introduces limitations in the use of these datasets for hydroclimatic assessments and regional planning. This necessitates the need to first understand the long-term monthly and seasonal climatology of the hydroclimatic variables, their spatiotemporal variations, and projected changes in the future. This study aims to assess the historical and future simulations of atmospheric moisture variables in multiple CMIP6 models (Eyring et al. 2016a,b), providing a broad comparison of their performance for the Great Lakes region. Specifically, we aim to evaluate 1) monthly biases in historical precipitation simulations and future changes in its seasonality, 2) the contribution of lakes to the seasonal and spatial patterns of evapotranspiration, and 3) the effects of changes in the remote moisture sources and regional evapotranspiration on precipitation in the future simulations. Furthermore, we also provide an overview of how lakes are represented in each model and the consequent effects on the spatial patterns of evapotranspiration and precipitation.
2. Data and methods
We assess three atmospheric moisture variables (precipitation, evapotranspiration, and moisture flux) in 15 CMIP6 GCMs (Table 1) for the Great Lakes region (40°–51°N, 94°–74°W). We use daily data for all variables over the historical period (defined as the 1981–2010 climatology) and future projections over the midcentury period (defined as the 2041–70 climatology) for assessment of the middle-of-the-road SSP2–4.5 scenario (O’Neill et al. 2017). In NCAR-CESM2 (Danabasoglu et al. 2020), the future averages are computed for 2045–70 because of missing data for select years. ERA-Interim (Dee et al. 2011), with a horizontal spatial resolution of 1° × 1°, is used as a reference dataset because it provides a relatively consistent higher-resolution output and has been used for past moisture budget assessments (Lavers and Villarini 2013, 2015; Seager and Henderson 2013). This dataset also shows a spatiotemporally adequate representation of the various moisture budget quantities within the Great Lakes region when compared to other reanalyses (Minallah and Steiner 2021). For historical precipitation, monthly wet/dry biases in CMIP6 data are computed from the observation-based gridded Climatic Research Unit time series (CRU TS4.03) (Harris et al. 2014; Harris et al. 2020).
CMIP6 models and their native spatial resolutions. The presence of lakes is identified using the two proxy variables (percentage of grid cell occupied by land and lake signature in evaporation). The lake representation in predecessor CMIP5 version of the models as identified by the Great Lakes Integrated Sciences and Assessments (GLISA) Program is also included.
The representation of the Great Lakes differs considerably from model to model, where some do not represent the lakes while others have partial or inaccurate representation. Lakes are generally simulated within the land component of the GCM in which different land surface types are represented by “tiles” (e.g., lake, vegetation, frozen, urban) and quantities (e.g., evaporation rates, surface temperature) are averaged over the grid cell occupied by the specific tile (Working Group on Coupled Modeling 2015). The representation of lakes in the GCMs is often not clearly communicated in the standard output, and therefore we use two variables as proxy to identify the lake presence or signature summarized in Table 1: 1) the percentage of the grid cell occupied by land and lakes (Fig. S3) and 2) land–lake evaporation contrast. The former identifies the regions that are not represented as either land or lake surface, indicating that the grid cells likely align with the ocean component of the model, while the latter identifies that the land model of the GCM is responsible for simulating the surface processes over the water bodies. Broadly, this provides three categories of lake representation: land only (no discernible water body presence), a lake signature (representing all or fraction of the grid cell as water), or the ocean component of the GCM is active over complete/partial lake grid cells. We further compare this categorization in Table 1 with the assessment of the Great Lakes in the predecessor CMIP5 GCMs conducted by the National Oceanic and Atmospheric Administration–Great Lakes Integrated Sciences and Assessments (NOAA-GLISA) program.
3. Results
a. Precipitation
1) Historical seasonality
In the CRU and ERA-Interim total precipitation seasonality, the summer months from June to September (JJAS) are the wettest at 3.09 ± 0.21 and 3.16 ± 0.22 mm day−1 respectively (mean ± 95% confidence interval), whereas the winter months from December to March (DJFM) are relatively drier (1.70 ± 0.16 and 1.82 ± 0.18 mm day−1, respectively). The region also exhibits a midsummer drying behavior in the precipitation climatology, where the magnitude drops from July to August by approximately 7% and recovers in September followed by a progressive decline into autumn (October–November) (Minallah and Steiner 2021). CMIP6 models exhibit a range of precipitation seasonal patterns, which generally do not conform to the seasonality shown in the observation-based datasets (Fig. 1a).
Seasonal cycle for (a),(b) total precipitation, (c),(d) evapotranspiration, and (e),(f) vertically integrated moisture flux convergence (MFC) for 15 CMIP6 models. Columns show (left) historical (1981–2010 mean) climatology and (right) midcentury (SSP2–4.5 2041–70 mean) climatology. ERA-Interim is shown in black dashed lines for the historical data. The corresponding standard deviation for each dataset and month is provided in Fig. S4 and in Figs. 7 and 8. All units are in mm day−1.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
Out of the 15 CMIP6 models, the seasonal precipitation cycle of EC-Earth3 resembles the observation-based datasets the most. However, EC-Earth simulates an overall higher magnitude throughout the year by up to 30%, with statistically significant differences from CRU in February–May and October–December (Fig. 2). MIROC6 seasonality is also comparable to CRU, with the months from May to September showing similar magnitudes (difference < 5%), whereas November–April are significantly wetter (by up to 36% in February). Various models, including ACCESS-ESM1-5, IPSL-CM6A-LR, MPI-ESM1-2-HR, and NUIST-NESM3, show a strong spring (April–May) and winter (DJFM) wet bias. For example, NUIST-NESM3 is overly wet in the winter to spring months with twice the precipitation magnitude in February as compared CRU and ERA-Interim (Fig. 2). Some models show a singular skewed peak (MRI-ESM2.0, GFDL-CM4, and CanESM5) with the precipitation maxima either in June or July and then decreasing slowly until December (Fig. 1a). Four models, specifically FGOALS-g3, NCAR-CESM2, NorESM2-MM, and BCC-CSM2-MR, exhibit a drier summer as compared to CRU with the JJAS magnitudes underestimated by 32%, 19%, 16%, and 14% respectively. These four models fail to capture the precipitation seasonal cycle entirely, with FGOALS-g3 being the driest of all and having the annual maxima in the month of November (Fig. 1a).
Percentage difference in monthly total precipitation of the CMIP6 models and ERA-Interim from CRU TS4.03, averaged over the 1981–2010 period. Positive (negative) values indicate a wet (dry) bias in the CMIP6 and ERA-Interim values. Months with statistically significant differences (p value < 0.05) are outlined in black.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
The convective precipitation magnitudes vary across the models; for example, the July maxima ranges from 0.62 ± 0.06 to 3.41 ± 0.20 mm day−1 (Fig. 3a). The convective contribution is the highest over the summer months and nominal in the colder months; for example, the January minima ranges from trace amounts for IPSL-CM6A-LR to 0.43 ± 0.08 mm day−1 for EC-Earth3, where the latter also has overall closer seasonality to ERA-Interim. For ERA-Interim and some models (ACCESS-ESM1-5, ACCESS-CM2, EC-Earth3, and MRI-ESM2.0), more than 70% of total precipitation comes from the convective systems in July, whereas this contribution is only 20% for MIROC6 and GFDL-CM4. The grid scale of the models, together with the parameterization schemes employed, influences how the total precipitation magnitude is partitioned into convective and large-scale stratiform quantities, therefore it is difficult to attribute intermodel differences in convection to specific processes without sensitivity tests. Past studies, using regional climate models, have shown that finer spatiotemporal resolution and convection-permitting scales (~4 km) can improve model precipitation biases (Fosser et al. 2015; Gao et al. 2017; X. B. Wang et al. 2016); however, most GCMs (even in the latest CMIP6 cycle) are run at coarser resolutions (>100 km). Nonetheless, we can compare the historical and future simulations of the same model to see how the SSP 2–4.5 scenario alters the magnitude and percentage contribution of convective to total precipitation.
(top) Seasonal cycle for convective precipitation (mm day−1) and (bottom) percentage contribution of convective to total precipitation for the (left) historical and (right) midcentury period on native model resolutions (Table 1). The corresponding standard deviation for each dataset and month is provided in Fig. S4.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
2) Future changes
In the difference between future and historical precipitation magnitudes, some common patterns emerge among the models despite their markedly different historical cycles. Specifically, the winter to spring months are nearly ubiquitously wetter in the midcentury simulations, whereas the midsummer months of July and August are drier in most models (Fig. 4a). In the future EC-Earth3 simulation, the winter–spring months are even wetter and the midsummer drying behavior is pronounced, as the percentage decline in August precipitation magnitude increases from 8% to 11% and the percentage increase in September magnitude rises from 3% to 17% in the future scenario. Another interesting feature is the emergence of the midsummer drying in some models that lacked this July–September pattern in the historical seasonality. This is visible in BCC-CSM2-MR, CanESM5, and IPSL-CM6A-LR (Fig. 1b), where the July–August drop (and subsequent August–September rise) in the future scenario changes by −17% (20%), −22% (13%), and −12% (6%) for each model, respectively. CanESM5 also shows the highest absolute magnitude change with a statistically significant decline of 0.56 mm day−1 in August and increase of 0.78 mm day−1 in May and December (Fig. 4a). For most models, there is a shift in the annual maxima to an earlier month: for example, it changes from June to May for CanESM5, ACCESS-CM2, EC-Earth3, and NCAR-CESM2 and from July to June for MPI-ESM1-2-HR, GFDL-CM4, and NUIST-NESM3.
Difference in absolute magnitude of (a) total precipitation, (b) convective precipitation, (c) evapotranspiration, and (d) vertically integrated moisture flux convergence between the midcentury (SSP2–4.5; 2041–70) and historical (1981–2010) periods. Blue (red) colors indicate an increase (a decrease) in midcentury magnitudes, where the statistically significant differences (p value < 0.05) are in bold and underlined. All units are in mm day−1.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
There are exceptions to the winter/spring-wetting and summer-drying pattern; for example, NUIST-NESM3 has a nonsignificant precipitation drop of −0.3 mm day−1 in February that is not present in any other model, and its spring–summer magnitude increases significantly (0.50 in April and 0.49 in June; Fig. 4a). MRI-ESM2.0 also simulates wetter conditions in all months except October, which shows a nonsignificant decline. In the historical climatology, this model is the wettest in summer (~22% wetter in JJAS than CRU) and this wet bias is amplified in the future scenario by approximately 0.35 mm day−1. MIROC6 overall has the smallest magnitude change in future precipitation, which is not significant in any month.
In convective precipitation, there is a universal increase in magnitudes for nearly all models and months (Fig. 4b), despite a total precipitation decrease in summer. The two exceptions are BCC-CSM2-MR and EC-Earth3, which have a significant and nonsignificant decline in August respectively that coincides with total precipitation magnitude decrease for these models (Fig. 4a). Overall, the percentage contribution of convective precipitation to total precipitation also shows a prevalent increase (Fig. 3d) and is especially high in the spring–summer months for IPSL-CM6A-LR, CanESM5, NUIST-NESM3, and MRI-ESM2.0, which all have a maximum rise in August contribution by 20%, 19%, 13%, and 13%, respectively.
Models diverge in the spatial patterns of future precipitation change, shown for the months of January and July (Fig. 5), which are usually the coldest and warmest months in the region. In January, there is an increase in future precipitation throughout the domain in nearly all models corresponding to the increase in Fig. 4a, while some models also show spurious regions of nonsignificant precipitation decrease (Fig. 5c). In the summer month of July, there is a less cohesive change among the models; for example, MRI-ESM2.0 shows a significant increase over Lake Superior, whereas MPI-ESM1-2-HR shows a significant decrease (Fig. 5d). Regions of statistically significant change (both increases and decreases) occur throughout the domain in various models (e.g., IPSL-CM6A-LR generally shows a decline in precipitation in various regions, while NUIST-NESM3 generally shows an increase in magnitude).
Spatial patterns of total precipitation for the months of (a) January and (b) July for the historical period (1981–2010 mean), and the difference between midcentury and historical magnitudes for (c) January and (d) July, where blue (red) colors indicate an increase (a decrease) in future magnitudes. Hatching in (a) and (b) show regions of statistically significant differences (p value < 0.05) from ERA-Interim in the historical climatology, and stippling in (c) and (d) shows statistically significant differences between future and historical climatology.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
b. Evapotranspiration
1) Historical patterns and lake contribution
The Great Lakes regional evapotranspiration seasonality has a Gaussian profile with the maxima in the months of June and July (3.28 ± 0.05 and 3.54 ± 0.06 mm day−1 for ERA-Interim, respectively; Fig. 1c). While most CMIP6 models follow the seasonal cycle simulated by ERA-Interim, the spread of evaporation rate is high, especially in the summer months. FGOALS-g3 has the lowest overall magnitude, peaking at 2.30 ± 0.04 mm day−1 in July, whereas ACCESS-ESM1-5 has the highest summertime magnitude of nearly double this rate in June and July at 4.52 ± 0.07 and 4.49 ± 0.06 mm day−1, respectively. Some models (including EC-Earth3, IPSL-CM6A-L, NCAR-CESM2, and NorESM2-MM) have weaker evaporative fluxes throughout the year as compared to ERA-Interim, while NUIST-NESM3 and MPI-ESM1-2-HR simulate high rates from March to August (spring to summer months).
Lake representation in models can have perceptible effects on spatial patterns of evaporation (Figs. 6a and 6b for January and July, respectively). Generally, lake surface temperatures exhibit a delayed response to seasonal cooling as compared to the surrounding land due to their different heat capacity (Scott and Huff 1996; Wetzel and Likens 2000). Therefore, the models that represent lakes will capture this contrast between the land and lake in their evapotranspiration patterns as well. In January, ERA-Interim has higher evaporation magnitudes over the Great Lakes as compared to the land surface, which is also simulated by MIROC6, IPSL-CM6A-LR, NorESM2-MM, NCAR-CESM2, and GFDL-CM4 with varying degrees. INM-CM5-0 evaporation patterns over Lakes Superior and Michigan correspond with its representation of these grid cells as oceans (Fig. S3 and Table 1). MPI-ESM1-2-HR also seems to represent lakes as sea/ocean; however, its winter evaporation rate does not capture the land–lake contrast (Fig. 6a). NUIST-NESM3 shows inconsistencies between the Great Lakes, where only Lake Superior and parts of Lakes Michigan and Huron show higher magnitudes, while EC-Earth3 has anomalous patterns with high intensity evaporation over Lakes Erie and Ontario and the southern parts of Lakes Michigan and Huron. In summer (Fig. 6b; July), the lakes tend to have lower surface temperatures and consequently evaporation rates as compared to the surrounding land, which is observed in ERA-Interim and some CMIP6 models (e.g., MIRCO6, EC-Earth3, IPSL-CM6A-LR, and GFDL-CM4). In MPI-ESM1-2-HR and INM-CM5-0, the lake regions that correspond with the ocean grid cells also show relatively lower magnitudes. BCC-CSM2-MR is peculiar in that its lake surface evaporation rates are much higher than the surrounding land in July, showing opposite behavior to other models with lake representation. FGOALs-g3, which has the lowest overall summertime evaporation rate (Fig. 1c), has lower evaporation throughout the domain with no discernible difference between land and lake fluxes. Similarly, ACCESS-ESM1-5, which has the highest magnitude in the seasonal cycle, simulates high evaporation rates throughout the domain without differentiation between land and lake.
Spatial patterns of evapotranspiration for the months of (a) January and (b) July for the historical period (1981–2010 mean), and the difference between midcentury and historical magnitudes for (c) January and (d) July, where blue (red) colors indicate an increase (a decrease) in future magnitudes. Hatching in (a) and (b) show regions of statistically significant differences (p value < 0.05) from ERA-Interim in the historical climatology, and stippling in (c) and (d) shows statistically significant differences between future and historical climatology.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
To assess the effect of lake representation on evaporation, Fig. 7b shows the percentage contribution of the lake grid cells, corresponding to real lake surface (Fig. 7a), to the total evaporation for each month. In models with some water body representation (either as ocean or lake), the lake grid cells tend to have higher percentage contribution to the winter monthly evaporation and lower contribution in the summer. For example, in ERA-Interim lake evaporation constitutes 56% of the January rate, whereas in EC-Earth3 this percentage is ~63% for the months of December and January, despite erroneous lake representation in the model (Fig. 6a). One exception is MPI-ESM1-2-HR, which appears to simulate lakes as oceans (Fig. S3) but does not show any defining signature in the spatial evaporation patterns (Fig. 6a) or contribution to total evaporation (Fig. 7b). The percentage contribution indicates that despite their smaller surface area as compared to total land in the study domain, lakes can have a nonnegligible effect on the seasonality of regional evaporation in model simulations, specifically by acting as a moisture source during the dry winter months and as a moisture sink during warmer months, when ERA-Interim even shows condensation over some parts of Lake Superior for select days (negative evaporation rates). However, we note that ERA-Interim and CMIP6 models do not simulate lake ice cover, which is prevalent in the late winter (February–March) and alters surface fluxes, such as by reducing lake evaporation magnitudes (Spence et al. 2013).
(a) Map of lake grid cells, identified using each 1° × 1° grid where at least 10% of the surface is covered by the water body. (b) Percentage contribution of lake grid cells to evapotranspiration for each month. Blue shades indicate higher contribution of the lake area to the total magnitude.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
2) Future changes
Evaporation increases throughout the year in all the models by midcentury (Fig. 4c) while retaining the same the seasonal cycle (Fig. 1d). This magnitude increase varies across the models by 15%–46% in winter, 7%–24% in spring, 2%–17% in summer, and 4%–20% in autumn. MRI-ESM2.0 has the highest overall rise in evapotranspiration, which is as high as 0.70 mm day−1 in the month of June. CanESM5 shows larger increase in the months of May and June (~0.5 mm day−1) that shifts its annual maxima from July to June (Fig. 1d). BCC-CSM2-MR is the only exception, showing a nonsignificant decrease (−0.07 mm day−1) in evaporation in August (Fig. 4c).
The spatial patterns of evaporation change reveal mostly consistent patterns across the models, barring a few distinguishable patterns over the lakes. In January (Fig. 6c), INM-CM5-0 shows a significant increase over the grid cells simulated as oceans, while NUIST-NESM3 shows a decrease in evaporation over the grid cells with lake representation. FGOALS-g3, which does not seem to have a lake signature in the historical data, surprisingly shows an increase in evaporation rates over parts of the Great Lakes that is distinct in magnitude from the surrounding land. Other models with some lake representation (e.g., GFDL-CM4, NCAR-CESM2, NorESM2-MM, EC-Earth3, IPSL-CM6A-LR) also simulate an increase in evaporation rates over the lake surface (especially Lake Superior) in January. In summer (July; Fig. 6d), the patterns of change are more diverse. MRI-ESM2.0 shows a very strong increase over Lake Superior that drives an overall increase in regional evaporation (Fig. 1d); however, this change is statistically not significant. In some models (e.g., BCC-CSM2-MR, CanESM5, NCAR-CESM2, and NorEMS2-MM), we also observe a decrease in evapotranspiration, especially over land south and west of the domain. In EC-Earth3, the grid cells over Lakes Erie and Ontario and south of Lakes Michigan and Huron, which had unusual spatial patterns in the historical simulations, behave differently in the future projections as well and show no significant change in evaporation.
c. Moisture fluxes
1) Historical seasonality
The moisture flux convergence climatology of ERA-Interim shows net divergence in July and August (−0.30 ± 0.32 and −0.55 ± 0.27 mm day−1, respectively) and weak convergence in June (0.12 ± 0.37 mm day−1), while the remaining months have stronger convergence (ranging from 0.52 ± 0.33 to 1.33 ± 0.26 mm day−1; Fig. 1e). This seasonality is produced by month-to-month variations in zonal and meridional fluxes, driven by wind speeds/specific humidity gradients across the boundaries and synoptic-scale (submonthly) weather systems (Minallah and Steiner 2021). Most CMIP6 models follow this seasonal pattern, albeit with a broad range in the magnitudes (Figs. 1e and 8). Similar to ERA-Interim, some models are divergent only in July and August (CanESM5, MIROC6, MPI-ESM1-2-HR, NorESM2-MM, and NUIST-NESM3), while in others the months from June to August are divergent (BCC-CSM2-MR, FGOALS-g3, ACCESS-ESM1–5, NCAR-CESM2, and INM-CM5-0). BCC-CSM2-MR has the strongest divergence magnitude with a July maximum of −0.98 ± 0.21 mm day−1 and simulates weaker spring convergence (April–May; 0.70 ± 0.30 mm day−1) than ERA-Interim (1.08 ± 0.24 mm day−1). NUIST-NESM3 has high convergence in winters with a DJFM mean of 1.62 ± 0.31 mm day−1 (as compared to 0.93 ± 0.20 mm day−1 for ERA-Interim) but shows otherwise similar magnitude to ERA-Interim in the remaining months. IPSL-CM6A-LR and MRI-ESM2.0 have weak divergence in August and July, respectively (−0.16 ± 0.22 and −0.19 ± 0.20 mm day−1), while EC-Earth3 and GFDL-CM4 are convergent throughout the year. GFDL-CM4 shows small seasonal amplitude, where the difference between the maxima and minima is only 0.76 mm day−1, whereas for NUIST-NESM3, BCC-CSM2-MR, and CanESM5 this amplitude exceeds 2 mm day−1 (Fig. 8).
Historical (1981–2010 mean) monthly moisture fluxes across the four boundaries (south = dark blue, north = red, west = green, east = light blue) for the 15 CMIP6 models and ERA-Interim (left y axis; positive (negative) values indicate moisture inflow (outflow) to (from) the model domain), and vertically integrated moisture flux convergence [right y axis; positive (negative) values indicate moisture flux convergence (divergence)]. The 95% confidence intervals for MFC are also included.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
The moisture fluxes across the four boundaries (Fig. 7a) can explain these monthly variations in the convergence magnitudes. In general, zonal fluxes are larger due to the dominant zonal winds for the Great Lakes region (Minallah and Steiner 2021), where the western boundary always provides moisture inflow and eastern boundary carries moisture out of the domain (Fig. 8). It is important to note that at finer spatiotemporal resolutions (e.g., hourly time series), this inflow–outflow behavior may be altered due to localized changes in wind direction associated with subsynoptic weather patterns; however, the westerly flow direction is consistent among all models at the monthly time scale. The magnitude of meridional fluxes is weaker by nearly an order of magnitude, but they nonetheless play an important role in producing the net divergence/convergence for the region.
The winter/spring net convergence is driven by moisture inflow at the western boundary and supplemented by moisture transport from the southern boundary, with the zonal inflow generally dominating in percentage contribution for most models. However, for BCC-CSM2-MR, CanESM5, NUIST-NESM3, and EC-Earth3, the total inflow is much larger than the outflow because of the substantial moisture influx through the southern boundary, which even exceeds the zonal inflow in some months for the latter two models. These four models also have the highest net convergence among all the datasets with a mean of 1.19 ± 0.20, 1.27 ± 0.20, 1.62 ± 0.31, and 1.10 ± 0.20 mm day−1 over the DJFM months, where the percentage contribution to the total inflow of the southern boundary is 37.3%, 43.3%, 44.5%, and 51.4%, respectively while of the western boundary is 59.8%, 56.7%, 54.3%, and 47.5%, respectively. The fluxes through the northern boundary are not sufficiently large during the winter/spring months to influence the net moisture flux. The patterns in summer and autumn seasons are more complex, where the south boundary can act both as an inflow or outflow boundary depending on the model and month. For some models the north boundary has relatively high contribution to the total inflow, specifically BCC-CSM2-MR, FGOALS-g3, ACCESS-ESM1-5, MIROC6, NCAR-CESM2, and INM-CM5-0, where the percentage contribution of north to the total JJAS inflow is 10.6%, 24.7%, 15.9%, 21.4%, 10.8%, and 14.1%, respectively.
2) Future changes
In the midcentury summer, there is an unambiguous decrease in the magnitude of the moisture flux convergence (or an increase in divergence) in nearly all the models (Figs. 1f and 4d). In winter, however, the model responses are variable and exhibit a nonsignificant change. We note that in the future, both inflow and outflow magnitudes increase through most of the year (Fig. S5); however, the difference between inflow and outflow drives the change observed in Fig. 4d and can be dominated by variations across a single boundary. In general, while these nuanced differences drive the net convergence/divergence magnitudes, the seasonal cycle of historical and midcentury moisture flux convergence does not vary in its structure; that is, the shape of the monthly climatology is similar, barring two exceptions (EC-Earth3 and NorESM2-MM). In NorESM2-MM, the month of June changes from convergent to divergent (0.41 ± 0.35 mm day−1 in historical to −0.29 ± 0.32 mm day−1 in midcentury), where the inflow decreases by 0.12 mm day−1 and outflow increases by 0.58 mm day−1 (Fig. S5). EC-Earth3, which is convergent throughout the year in the historical period, simulates divergence in July and August in midcentury when the outflow from the east boundary dominates (Fig. 9). GFDL-CM4, on the other hand, remains convergent throughout the year and its change in convergence magnitude does not show any distinct pattern in the different seasons (Fig. 4d). In NUIST-NESM3, February inflow decreases by −0.81 mm day−1 while the outflow decreases by −0.38 mm day−1 that ultimately increases the net divergence, which is primarily driven by an inflow decline from the south boundary (Figs. 8 and 9). For the month of September, both inflow and outflow increase in NUIST-NESM3 (by 1.75 and 2.24 mm day−1, respectively); however, the outflow from the east dictates the overall change.
As in Fig. 8, but for the midcentury (SSP2–4.5 2041–70) period.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
4. Discussion
Model biases in historical simulations can propagate to future projections (Knutti and Sedlacek 2013; Lin et al. 2017), adding another tier of uncertainty to the scenario assessments. Therefore, assessment of historical model simulations can be an indicator of the model performance (Flato et al. 2013; Srivastava et al. 2020). While each model has unique seasonal characteristics, stemming from their native resolution, dynamical core, and various parameterization schemes (Demory et al. 2014; Kusunoki et al. 2020; Vannière et al. 2018), we broadly observe the following for precipitation simulations over the Great Lakes region:
Some models are extremely dry in the summer (FGOALS-g3, NCAR-CESM2, NorESM2-MM, and BCC-CSM2-MR; Fig. 2) and remain dry in the future projections (Fig. 1b).
Certain models show strong winter and springtime wet bias in the historical climatology, which is especially pronounced in ACCESS-ESM1-5, IPSL-CM6A-LR, MPI-ESM1-2-HR, and NUIST-NESM3. By the midcentury, the former two get even wetter in these months; NUIST-NESM3 shows an anomalous decline in February magnitude (not observed in any other model) but overall remains wet in the future.
Only two models out of the 15 (EC-Earth3 and MIRCO6) capture the historical precipitation climatology when compared to CRU and ERA-Interim, but the future behavior of the two models is quite different. The seasonal amplitude in EC-Earth3 becomes more pronounced (i.e., stronger midsummer drying and wetter remaining months), while in MIROC6 the seasonal amplitude reduces from historical to midcentury (1.12 and 1.05 mm day−1, respectively).
The remaining models simulate varying behaviors; for example, MRI-ESM2.0, which is wet in summers in the historical data, continues to get wetter by midcentury by approximately 7.8%–9.8% from June to September. For select models, similar biases are shown in Dagan et al. (2019) for the annual means across the 1981–2005 period; for example, MRI-ESM2.0, IPSL-CM6A-LR, GFDL-CM4, and EC-Earth3 are all shown to be wet as compared to Climate Prediction Center Unified CONUS precipitation dataset (CPC), which matches our monthly bias assessment (Fig. 2).
Barring a few exceptions, there is a predominant case of summer drying and winter/spring wetting in most models (Fig. 4a), which is corroborated by previous CMIP5 assessment by Byun and Hamlet (2018), indicating that the precipitation seasonality will somewhat shift toward the colder months by the midcentury. This is indeed observed in the climatology where the annual maximum shifts a month earlier for most models (10 out of 15) or remains the same with the winter/spring months still getting wetter (3 out of 15).
The magnitude of convective precipitation varies across the models due to the strong differences in spatial resolutions of the model runs and parameterizations schemes. Nonetheless, we note that both the convective magnitude and the percentage contribution of convective to total precipitation persistently increase by the midcentury for nearly all models and months. A more involved assessment of convective systems is required to understand whether the storms are developing in situ (i.e., within the Great Lakes region) or propagating into the domain.
The evaporation seasonal cycle has a similar Gaussian profile for all models, although the magnitudes vary considerably; for example, the annual maxima vary between 2.3 and 4.5 mm day−1 among the models. The presence of lakes and whether/how they are simulated in the model have considerable impact on evaporation, which provides the potential to influence precipitation patterns. For example, the EC-Earth3 lake representation leads to abnormally high winter evaporation rates over Lakes Erie and Ontario and parts of Lakes Michigan and Huron (Fig. 6a), which also correspond to higher simulated precipitation in these grid cells (Fig. 5a). In the future scenario, the evaporative fluxes increase throughout the year for all months and models (Fig. 4c). Additionally, there is a higher increase over water surfaces than the surrounding land in winter months where lakes are represented (especially for Lake Superior; Fig. 6c), whereas in summer some models (BCC-CSM2-MR, CanESM5, NCAR-CESM2, and NorESM2-MM) simulate statistically significant decrease in land evaporation in southern and western parts of the domain. Past studies have indicated the sensitivity of deep lakes such as Lake Superior to climate warming, where the lake response is amplified as compared to the surroundings (Kravtsov et al. 2018; Zhong et al. 2016). We note this to be the case in some models, where Lake Superior has a statistically significant increase in the evaporation rates in winter (e.g., IPSL-CM6A-LR, NCAR-CESM2, and GFDL-CM4). For large deep lakes, the internal dynamics, lake stratification, and thermodynamics play a dominant role in the lake surface–atmosphere interactions and consequently on the response to climate change (Sugiyama et al. 2018; Zhong et al. 2019). Inclusion of a lake model in the GCM alters the radiative and evaporative fluxes, the impact of which can be prominent on regional scales (Le Moigne et al. 2016). In absence of simulations that alter ice cover and surface energy budgets, the response to warming may not be adequately captured by the CMIP6 models.
The regional evapotranspiration contributes toward the total precipitation in the form of precipitation recycling (Dominguez et al. 2006; Eltahir and Bras 1996; Zangvil et al. 2004). This is represented by the recycling ratio [Eq. (4)], with a small contribution (generally less than 10%) from evapotranspiration to total precipitation in the winter (Fig. 10a) when the land surface is generally frozen. One source of this winter contribution can arise from the relatively warmer lake surface, which produces higher evaporation rates (Fig. 6a for models with lake representation) and can thus induce lake-effect precipitation (Shi and Xue 2019). The recycling ratio is the highest in May and June, and its magnitude varies among the models from 10.1% ± 0.47% (for NorESM2-MM) to 22.6% ± 0.91% (for ACCESS-ESM1-5) in May, and 11.3% ± 0.47% (for FGOALS-g3) to 21.2% ± 1.1% (for ACCESS-ESM1-5) in June.
(a) Seasonal cycle of historical precipitation recycling ratio and (b) the percentage difference between midcentury and historical ratios for each month. Green (brown) colors indicate an increase (decrease) in the recycling ratio in the future SSP2–4.5 scenario. The statistically significant differences (p value < 0.05) are in bold and underlined.
Citation: Journal of Climate 34, 12; 10.1175/JCLI-D-20-0751.1
For the CMIP6 models, we highlight that 1) the recycled precipitation peak is typically in May/June, preceding the annual maxima in evapotranspiration (typically in July); 2) in some models, there is correspondence between higher evapotranspiration rates and contribution of recycled precipitation to total precipitation (e.g., in ACCESS-ESM1-5, ACCESS-CM2, and INM-CM5-0); and 3) there are models where the evapotranspiration and the recycling ratio are decoupled; for example, NUIST-NESM3 has one of the highest evapotranspiration rates in spring/summer months (Fig. 1c) but a low recycling ratio (maxima of 12.5% in June). In this model, the simulated moisture flux convergence is anomalously high, thereby reducing the recycling ratio. By midcentury, both the regional evapotranspiration (Fig. 4c) and moisture inflow (Fig. S5a) are ubiquitously increasing, which together create changes in the recycling contribution (Fig. 10b). Most models simulate an increase in winter recycled precipitation by the midcentury, and a decline in summer and autumn months. However, it is important to note that this recycling computation does not account for atmospheric moisture storage (Zangvil et al. 2004), which acts as a reservoir that is not accounted for by the other three moisture budget quantities assessed here, and could lead to an overestimation in the recycling ratio. An increase in future air temperature will also increase the capacity of the atmospheric column to hold more moisture and increase the role of total precipitable water in the atmospheric moisture budget (Trenberth 1998).
We observe that the midcentury moisture flux convergence cycle is amplified. Specifically, the magnitude of winter convergence and summer divergence is projected to increase (Fig. 1f), similar to the global findings of Levang and Schmitt (2015). This study identified an increase in global mean zonal and meridional moisture fluxes, resulting in amplified regional patterns of moisture convergence/divergence in a warmer future scenario, even without circulation changes (which are generally smaller than the thermal effects). Moisture fluxes and regional evapotranspiration play a concurrent role in defining the relative contribution of remote and local moisture sources to the total precipitation. Dagan et al. (2019) state that at regional scales, local precipitation changes are not well constrained by the changes in evaporation, and thus the moisture flux convergence can contribute appreciably to future changes in precipitation. We indeed find that in the Great Lakes region, the synoptic-scale moisture inflow remains the predominant contributor to the total precipitation and the contribution of recycled precipitation is secondary, despite its role in shaping the precipitation seasonality (Minallah and Steiner 2021). In the future simulations, the decrease in summer convergence appears to drive the projected decline in July and August precipitation, despite an increase in the evapotranspiration magnitude (Fig. 4). In midcentury summers, the two moisture sources (regional evapotranspiration and moisture influx from remote regions) increase, while the net moisture outflow from the domain also increases concurrently (Fig. S5b). However, the difference between the two (inflow minus outflow) decreases at a higher rate than the increase in evaporation, thus reducing the net moisture content available for precipitation.
A persistent limitation in the CMIP models (both phases 5 and 6) is coarse horizontal and vertical resolution. Past studies have shown that the model resolution influences the amount of simulated precipitation, and the coarse native resolution can introduce some limitations in simulation of precipitation processes. Vannière et al. (2018) discuss these resolution effects on the hydrological cycle and highlight the improvements in precipitation patterns and amplitude at regional scales due to better simulation of the seasonal mean circulation in finer-resolution models. However, finer-resolution dynamically downscaled studies that resolve the lakes struggle to capture the seasonal precipitation cycle as well (Basile et al. 2017; Bryan et al. 2015). Similarly, evaporation rates also increase at finer resolutions especially over the oceans, but the fate of moisture and its conversion to precipitation over land is dependent on the model formulation (Vannière et al. 2018). The temporal resolution of the models also affects computation of moisture fluxes, and previous assessments have found that wintertime moisture flux convergence can be up to 25% weaker in daily data as compared to 6-hourly time series (Minallah and Steiner 2021; Seager and Henderson 2013). These limitations must be taken into account when performing process-specific and detailed evaluation of regional changes.
5. Conclusions
Understanding the evolution of precipitation seasonality by midcentury is a critical component in assessing water resources for the Great Lakes region. Past studies have shown that seasonal changes in atmospheric moisture budget can alter net basin supply and consequently lake levels in the future (Mailhot et al. 2019; Notaro et al. 2015). Therefore, strong changes in atmospheric moisture, as projected by the CMIP6 models, will require adaptation of management and policy interventions. For the Great Lakes region, this study provides a broad assessment of the simulation of various atmospheric moisture components and their future changes that can help identify a regional subset of the CMIP6 GCMs for more comprehensive mechanistic assessments of the atmospheric and land water budgets.
We find a predominant case of summer drying and winter/spring wetting in nearly all models, indicating that the precipitation seasonality will shift toward the colder months by the midcentury. While the newer CMIP6 models are shown to have some improvements compared to earlier generation CMIP experiments (Grose et al. 2020; Kim et al. 2020), biases in model representation of historical precipitation persist, which can be carried on to future simulations. We also find that the models with historical dry and wet biases tend to retain these biases in the future simulations. Nonetheless, the magnitude of convective precipitation and its percentage contribution to the total precipitation is universally increasing throughout the year by the midcentury. Precipitation in the region is primarily driven by moisture influx into the domain that carries evaporated moisture from remote regions, with a secondary contribution of the local evapotranspiration in the form of recycled precipitation. We find that by midcentury, both moisture inflow and regional evapotranspiration are increasing; however, nuanced differences in the fluxes across the boundaries result in the summer-drying and winter-wetting behavior. In some models, the contribution of the recycled precipitation can be as high as 15%–20% for the spring/summer months.
Many CMIP6 models do not simulate large inland water bodies such as the Laurentian Great Lakes, while others have major inconsistencies in how the lakes are represented. From a regional perspective, these lakes have notable effects on the moisture generation and distribution processes at meteorological and climatic time scales. We found that lakes have substantial contribution to evapotranspiration magnitudes in the colder months (in models that do provide some lake representation), in addition to altering the spatial patterns of evapotranspiration and, in some instances, precipitation in the form of lake-induced winter snow on the downwind shores (Baijnath-Rodino et al. 2018; Notaro et al. 2015, 2013). Further, both lake dynamics and thermodynamics alter the surface energy budgets and lake–atmosphere interactions, and influence the seasonal variations in lake response to climate warming. This suggests that representation of detailed lake processes in GCMs is important for understanding the hydroclimate of regions with large inland water bodies and for accurate future climate assessments.
Acknowledgments
This study was supported by National Science Foundation Grant OCE-1600012. The authors acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP6, the climate modelling groups (listed in Table 1 of this paper) for producing and making available their model output, and the Earth System Grid Federation (ESGF) for archiving and providing access to the data. We thank the three anonymous reviewers for their constructive feedback.
REFERENCES
Adrian, R., and Coauthors, 2009: Lakes as sentinels of climate change. Limnol. Oceanogr., 54, 2283–2297, https://doi.org/10.4319/lo.2009.54.6_part_2.2283.
Aloysius, N. R., J. Sheffield, J. E. Saiers, H. B. Li, and E. F. Wood, 2016: Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models. J. Geophys. Res. Atmos., 121, 130–152, https://doi.org/10.1002/2015JD023656.
Austin, J., and S. Colman, 2007: Lake Superior summer water temperatures are increasing more rapidly than regional air temperatures: A positive ice-albedo feedback. Geophys. Res. Lett., 34, L06604, https://doi.org/10.1029/2006GL029021.
Austin, J., and S. Colman, 2008: A century of temperature variability in Lake Superior. Limnol. Oceanogr., 53, 2724–2730, https://doi.org/10.4319/lo.2008.53.6.2724.
Baijnath-Rodino, J. A., C. R. Duguay, and E. LeDrew, 2018: Climatological trends of snowfall over the Laurentian Great Lakes Basin. Int. J. Climatol., 38, 3942–3962, https://doi.org/10.1002/joc.5546.
Basile, S. J., S. A. Rauscher, and A. L. Steiner, 2017: Projected precipitation changes within the Great Lakes and western Lake Erie Basin: A multi-model analysis of intensity and seasonality. Int. J. Climatol., 37, 4864–4879, https://doi.org/10.1002/joc.5128.
Bi, D. H., and Coauthors, 2013: The ACCESS coupled model: Description, control climate and evaluation. Aust Meteor. Ocean, 63, 41–64, https://doi.org/10.22499/2.6301.004.
Bryan, A. M., A. L. Steiner, and D. J. Posselt, 2015: Regional modeling of surface–atmosphere interactions and their impact on Great Lakes hydroclimate. J. Geophys. Res. Atmos., 120, 1044–1064, https://doi.org/10.1002/2014JD022316.
Byun, K., and A. F. Hamlet, 2018: Projected changes in future climate over the Midwest and Great Lakes region using downscaled CMIP5 ensembles. Int. J. Climatol., 38, e531–e553, https://doi.org/10.1002/joc.5388.
Cao, J., and Coauthors, 2018: The NUIST Earth System Model (NESM) version 3: Description and preliminary evaluation. Geosci. Model Dev., 11, 2975–2993, https://doi.org/10.5194/gmd-11-2975-2018.
Dagan, G., P. Stier, and D. Watson-Parris, 2019: Analysis of the atmospheric water budget for elucidating the spatial scale of precipitation changes under climate change. Geophys. Res. Lett., 46, 10 504–10 511, https://doi.org/10.1029/2019GL084173.
Danabasoglu, G., and Coauthors, 2020: The Community Earth System Model version 2 (CESM2). J. Adv. Model. Earth Syst., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Demory, M. E., P. L. Vidale, M. J. Roberts, P. Berrisford, J. Strachan, R. Schiemann, and M. S. Mizielinski, 2014: The role of horizontal resolution in simulating drivers of the global hydrological cycle. Climate Dyn., 42, 2201–2225, https://doi.org/10.1007/s00382-013-1924-4.
Dominguez, F., P. Kumar, X. Z. Liang, and M. F. Ting, 2006: Impact of atmospheric moisture storage on precipitation recycling. J. Climate, 19, 1513–1530, https://doi.org/10.1175/JCLI3691.1.
Eltahir, E. A. B., and R. L. Bras, 1996: Precipitation recycling. Rev. Geophys., 34, 367–378, https://doi.org/10.1029/96RG01927.
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016a: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.
Eyring, V., and Coauthors, 2016b: Towards improved and more routine Earth system model evaluation in CMIP. Earth Syst. Dyn., 7, 813–830, https://doi.org/10.5194/esd-7-813-2016.
Fichot, C. G., K. Matsumoto, B. Holt, M. M. Gierach, and K. S. Tokos, 2019: Assessing change in the overturning behavior of the Laurentian Great Lakes using remotely sensed lake surface water temperatures. Remote Sens. Environ., 235, 111427, https://doi.org/10.1016/j.rse.2019.111427.
Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.
Fosser, G., S. Khodayar, and P. Berg, 2015: Benefit of convection permitting climate model simulations in the representation of convective precipitation. Climate Dyn., 44, 45–60, https://doi.org/10.1007/s00382-014-2242-1.
Gao, Y., L. R. Leung, C. Zhao, and S. Hagos, 2017: Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions. J. Geophys. Res. Atmos., 122, 2714–2733, https://doi.org/10.1002/2016JD025896.
GLISA, 2020: Evaluation of lakes in climate models. Great Lakes Integrated Sciences and Assessments, https://glisa.umich.edu/sustained-assessment/climate-models/.
Grose, M. R., and Coauthors, 2020: Insights from CMIP6 for Australia’s future climate. Earth’s Future, 8, e2019EF001469, https://doi.org/10.1029/2019EF001469.
Hanrahan, J. L., S. V. Kravtsov, and P. J. Roebber, 2010: Connecting past and present climate variability to the water levels of Lakes Michigan and Huron. Geophys. Res. Lett., 37, L01701 , https://doi.org/10.1029/2009GL041707.
Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 dataset. Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711.
Harris, I., T. J. Osborn, P. Jones, and D. Lister, 2020: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3.
Held, I. M., and Coauthors, 2019: Structure and performance of GFDL’s CM4.0 climate model. J. Adv. Model. Earth Syst., 11, 3691–3727, https://doi.org/10.1029/2019MS001829.
Hirota, N., Y. N. Takayabu, and A. Hamada, 2016: Reproducibility of summer precipitation over northern Eurasia in CMIP5 multiclimate models. J. Climate, 29, 3317–3337, https://doi.org/10.1175/JCLI-D-15-0480.1.
Kim, Y.-H., S.-K. Min, X. Zhang, J. Sillmann, and M. Sandstad, 2020: Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Wea. Climate Extrem., 29, 100269, https://doi.org/10.1016/j.wace.2020.100269.
Knutti, R., and J. Sedlacek, 2013: Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Climate Change, 3, 369–373, https://doi.org/10.1038/nclimate1716.
Kravtsov, S., N. Sugiyama, and P. Roebber, 2018: Role of nonlinear dynamics in accelerated warming of great lakes. Advances in Nonlinear Geosciences, A. Tsonis, Ed., Springer, 279–295.
Kusunoki, S., T. Ose, and M. Hosaka, 2020: Emergence of unprecedented climate change in projected future precipitation. Sci. Rep., 10, 4802, https://doi.org/10.1038/s41598-020-61792-8.
Lavers, D. A., and G. Villarini, 2013: Atmospheric rivers and flooding over the central United States. J. Climate, 26, 7829–7836, https://doi.org/10.1175/JCLI-D-13-00212.1.
Lavers, D. A., and G. Villarini, 2015: The contribution of atmospheric rivers to precipitation in Europe and the United States. J. Hydrol., 522, 382–390, https://doi.org/10.1016/j.jhydrol.2014.12.010.
Law, R. M., and Coauthors, 2017: The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1)—Part I: Model description and pre-industrial simulation. Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10-2567-2017.
Le Moigne, P., J. Colin, and B. Decharme, 2016: Impact of lake surface temperatures simulated by the FLake scheme in the CNRM-CM5 climate model. Tellus, 68A, 31274, https://doi.org/10.3402/tellusa.v68.31274.
Levang, S. J., and R. W. Schmitt, 2015: Centennial changes of the global water cycle in CMIP5 models. J. Climate, 28, 6489–6502, https://doi.org/10.1175/JCLI-D-15-0143.1.
Lin, Y., W. Dong, M. Zhang, Y. Xie, W. Xue, J. Huang, and Y. Luo, 2017: Causes of model dry and warm bias over central U.S. and impact on climate projections. Nat. Commun., 8, 881, https://doi.org/10.1038/s41467-017-01040-2.
Lurton, T., and Coauthors, 2020: Implementation of the CMIP6 forcing data in the IPSL-CM6A-LR model. J. Adv. Model. Earth Syst., 12, e2019MS001940, https://doi.org/10.1029/2019MS001940.
Mailhot, E., B. Music, D. F. Nadeau, A. Frigon, and R. Turcotte, 2019: Assessment of the Laurentian Great Lakes’ hydrological conditions in a changing climate. Climatic Change, 157, 243–259, https://doi.org/10.1007/s10584-019-02530-6.
Massonnet, F., M. Menegoz, M. Acosta, X. Yepes-Arbos, E. Exarchou, and F. J. Doblas-Reyes, 2020: Replicability of the EC-Earth3 Earth system model under a change in computing environment. Geosci. Model Dev., 13, 1165–1178, https://doi.org/10.5194/gmd-13-1165-2020.
Mehran, A., A. AghaKouchak, and T. J. Phillips, 2014: Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations. J. Geophys. Res. Atmos., 119, 1695–1707, https://doi.org/10.1002/2013JD021152.
Minallah, S., and A. L. Steiner, 2021: Role of the atmospheric moisture budget in defining the precipitation seasonality of the Great Lakes region. J. Climate, 34, 643–657, https://doi.org/10.1175/JCLI-D-19-0952.1.
Müller, W. A., and Coauthors, 2018: A higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR). J. Adv. Model. Earth Syst., 10, 1383–1413, https://doi.org/10.1029/2017MS001217.
Na, Y., Q. Fu, and C. Kodama, 2020: Precipitation probability and its future changes from a global cloud-resolving model and CMIP6 simulations. J. Geophys. Res. Atmos., 125, e2019JD031926, https://doi.org/10.1029/2019JD031926.
Notaro, M., K. Holman, A. Zarrin, E. Fluck, S. Vavrus, and V. Bennington, 2013: Influence of the Laurentian Great Lakes on regional climate. J. Climate, 26, 789–804, https://doi.org/10.1175/JCLI-D-12-00140.1.
Notaro, M., V. Bennington, and S. Vavrus, 2015: Dynamically downscaled projections of lake-effect snow in the Great Lakes basin. J. Climate, 28, 1661–1684, https://doi.org/10.1175/JCLI-D-14-00467.1.
O’Neill, B. C., and Coauthors, 2017: The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environ. Change, 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004.
Piccolroaz, S., M. Toffolon, and B. Majone, 2015: The role of stratification on lakes’ thermal response: The case of Lake Superior. Water Resour. Res., 51, 7878–7894, https://doi.org/10.1002/2014WR016555.
Scott, R. W., and F. A. Huff, 1996: Impacts of the Great Lakes on regional climate conditions. J. Great Lakes Res., 22, 845–863, https://doi.org/10.1016/S0380-1330(96)71006-7.
Seager, R., and N. Henderson, 2013: Diagnostic computation of moisture budgets in the ERA-Interim reanalysis with reference to analysis of CMIP-archived atmospheric model data. J. Climate, 26, 7876–7901, https://doi.org/10.1175/JCLI-D-13-00018.1.
Seland, Ø., and Coauthors, 2020: The Norwegian Earth System Model, NorESM2—Evaluation of theCMIP6 DECK and historical simulations. Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020.
Shi, Q., and P. Xue, 2019: Impact of lake surface temperature variations on lake effect snow over the Great Lakes region. J. Geophys. Res. Atmos., 124, 12 553–12 567, https://doi.org/10.1029/2019JD031261.
Smits, A. P., S. MacIntyre, and S. Sadro, 2020: Snowpack determines relative importance of climate factors driving summer lake warming. Limnol. Oceanogr. Lett., 5, 271–279, https://doi.org/10.1002/lol2.10147.
Spence, C., P. D. Blanken, J. D. Lenters, and N. Hedstrom, 2013: The importance of spring and autumn atmospheric conditions for the evaporation regime of Lake Superior. J. Hydrometeor., 14, 1647–1658, https://doi.org/10.1175/JHM-D-12-0170.1.
Srivastava, A., R. Grotjahn, and P. A. Ullrich, 2020: Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. Wea. Climate Extremes, 29, 100268, https://doi.org/10.1016/j.wace.2020.100268.
Sugiyama, N., S. Kravtsov, and P. Roebber, 2018: Multiple climate regimes in an idealized lake-ice-atmosphere model. Climate Dyn., 50, 655–676, https://doi.org/10.1007/s00382-017-3633-x.
Swart, N. C., and Coauthors, 2019: The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019.
Tatebe, H., and Coauthors, 2019: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019.
Toffolon, M., S. Piccolroaz, and E. Calamita, 2020: On the use of averaged indicators to assess lakes’ thermal response to changes in climatic conditions. Environ. Res. Lett., 15, 034060, https://doi.org/10.1088/1748-9326/ab763e.
Trenberth, K. E., 1998: Atmospheric moisture residence times and cycling: Implications for rainfall rates and climate change. Climatic Change, 39, 667–694, https://doi.org/10.1023/A:1005319109110.
Van Cleave, K., J. D. Lenters, J. Wang, and E. M. Verhamme, 2014: A regime shift in Lake Superior ice cover, evaporation, and water temperature following the warm El Niño winter of 1997–1998. Limnol. Oceanogr., 59, 1889–1898, https://doi.org/10.4319/lo.2014.59.6.1889.
Vannière, B., and Coauthors, 2018: Multi-model evaluation of the sensitivity of the global energy budget and hydrological cycle to resolution. Climate Dyn., 52, 6817–6846, https://doi.org/10.1007/s00382-018-4547-y.
Volodin, E. M., and Coauthors, 2017: Simulation of the present-day climate with the climate model INMCM5. Climate Dyn., 49, 3715–3734, https://doi.org/10.1007/s00382-017-3539-7.
Wang, X. B., P. Steinle, A. Seed, and Y. Xiao, 2016: The sensitivity of heavy precipitation to horizontal resolution, domain size, and rain rate assimilation: Case studies with a convection-permitting model. Adv. Meteor., 2016, 7943845, https://doi.org/10.1155/2016/7943845.
Wang, X. Q., G. Huang, B. W. Baetz, and S. Zhao, 2016: Probabilistic projections of regional climatic changes over the Great Lakes Basin. Climate Dyn., 49, 2237–2247, https://doi.org/10.1007/s00382-016-3450-7.
Wetzel, R. G., and G. E. Likens, 2000: The heat budget of lakes. Limnological Analyses, 3rd ed. Springer, 45–56.
Woolway, R. I., E. Jennings, and L. Carrea, 2020: Impact of the 2018 European heatwave on lake surface water temperature. Inland Waters, 10, 322–332, https://doi.org/10.1080/20442041.2020.1712180.
Working Group on Coupled Modelling, 2015: Overview of CMIP6-endorsed MIPs. WGCM, 280 pp., https://www.wcrp-climate.org/images/modelling/WGCM/CMIP/CMIP6-EndorsedMIPs_Summary_150819_Sent.pdf.
Wu, T., and Coauthors, 2019: The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019.
Yukimoto, S., and Coauthors, 2019: The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. J. Meteor. Soc. Japan, 97, 931–965, https://doi.org/10.2151/jmsj.2019-051.
Zangvil, A., D. H. Portis, and P. J. Lamb, 2004: Investigation of the large-scale atmospheric moisture field over the Midwestern United States in relation to summer precipitation. Part II: Recycling of local evapotranspiration and association with soil moisture and crop yields. J. Climate, 17, 3283–3301, https://doi.org/10.1175/1520-0442(2004)017<3283:IOTLAM>2.0.CO;2.
Zhong, Y. F., M. Notaro, S. J. Vavrus, and M. J. Foster, 2016: Recent accelerated warming of the Laurentian Great Lakes: Physical drivers. Limnol. Oceanogr., 61, 1762–1786, https://doi.org/10.1002/lno.10331.
Zhong, Y. F., M. Notaro, and S. J. Vavrus, 2019: Spatially variable warming of the Laurentian Great Lakes: An interaction of bathymetry and climate. Climate Dyn., 52, 5833–5848, https://doi.org/10.1007/s00382-018-4481-z.
Zhou, T., and Coauthors, 2018: The FGOALS climate system model as a modeling tool for supporting climate sciences: An overview. Earth Planet. Phys., 2, 276–291, https://doi.org/10.26464/epp2018026.