North American Climate in CMIP5 Experiments. Part II: Evaluation of Historical Simulations of Intraseasonal to Decadal Variability
By Zhao, Ming | |
Proquest LLC |
ABSTRACT
This is the second part of a three-part paper on North American climate in phase 5 of the
1. Introduction
This is the second part of a three-part paper on phase 5 of the
The CMIP5 provides an unprecedented collection of climate model output data for the assessment of future climate projections as well as evaluations of climate models for contemporary climate, the attribution of observed climate change, and improved understanding of climate processes and feedbacks. As such, these data contribute to the
The goal of this study is to provide a broad evaluation of CMIP5 models in their depiction of North American climate variability. It draws from individual work by investigators within the
We begin in section 2 by describing the CMIP5, providing an overview of the models analyzed, the historical simulations, and the generalmethodology for evaluating the models. Details of the main observational datasets to which the climate models are compared are also given in this section. The next five sections focus on different aspects of North American climate variability, organized by the time scale of the climate feature. Section 3 covers intraseasonal variability with focus on variability in the eastern
2. CMIP5 models and simulations
a. CMIP5 models
We use data from multiple model simulations of the ''historical'' scenario from the CMIP5 database. The CMIP5 experiments were carried out by 20 modeling groups representing more than 50 climate models with the aim of further understanding past and future climate change in key areas of uncertainty (Taylor et al. 2012). In particular, experiments have been focused on understanding model differences in clouds and carbon feedbacks, quantifying decadal climate predictability and why models give different answers when driven by the same forcings. The CMIP5 builds on the previous phase [phase 3 of CMIP (CMIP3)] experiments in several ways. First, a greater number of modeling centers and models have participated. Second, the models are more comprehensive in terms of the processes that they represent and are run at higher spatial resolution, therefore hopefully resulting in better skill in representing current climate conditions and reducing uncertainty in future projections. Table 1 provides an overview of the models used. The specific models used vary for each individual analysis because of data availability at the time of this study, and so the model names are provided within the results section where appropriate.
b. Overview of methods
Data from the historical CMIP5 scenario are evaluated, which is a coupled atmosphere-ocean mode simulation that is forced by historical estimates of changes in atmospheric composition from natural and anthropogenic sources, volcanoes, greenhouse gases, and aerosols, as well as changes in solar output and land cover. Historical scenario simulations were carried out for the period from the start of the industrial revolution to near present: 1850-2005.Our evaluations are generally carried out for the last 30 yr of the simulations, depending on the type of analysis and the availability of observations. For some analyses the only, or best available, data are from satellite remote sensing which restricts the analysis to the satellite period, which is generally from 1979 onward. In other cases the observational data are very uncertain for particular regions and time periods (e. g., precipitation in high latitudes in the first half of the twentieth century) and this is noted in the relevant subsection. For other analyses, multiple observational datasets are available and are used to capture the uncertainty in the observations. The observational datasets are summarized in Table 2 and further details of the datasets and data processing are given in the relevant subsections and figure captions. Where the comparisons go beyond 2005 (e.g., 1979-2008), data from the model representative concentration pathway 8.5 (RCP8.5) future projection scenario simulation (as this is regarded as closest to the business as usual trajectory) are appended to the model historical time series. About half the models have multiple ensemble members, but we select the first ensemble member for simplicity and discuss the variability in the results across the ensemble where appropriate.
3. Tropical intraseasonal variability
a. MJO-related variability over the eastern
It has been well documented that convection over the eastern
Here, model fidelity in representing ISV over the EP and intra-American sea (IAS) region is assessed by analyzing daily output of rainfall and 850-hPa winds from 18 CMIP5 models. Figure 1 displays a Taylor diagram for summer-mean (May-September) precipitation from the CMIP5 models over the EP domain (58S-308N, 1508-808W) compared to the TMPA precipitation (see Table 2 for expanded dataset names). While the two HadGEM models (HadGEM2-CC and HadGEM2-ES; see Table 1 for expanded model names) display the highest pattern correlations (;0.93), the MRI-CGCM3 show the smallest RMS because of its better skill in simulating the spatial standard deviations of summermean rainfall over the EP. In addition, four models (MPI-ESM-LR, CSIROMk3.6.0, CanESM2, and CNRMCM5) also exhibit relatively better pattern correlations than other models.
The leading ISV modes over the EP based on observed and simulated rainfall fields are identified using a complex empirical orthogonal function (CEOF) approach (Maloney et al. 2008). CEOF analyses were applied to 30-90-day bandpass filtered daily rainfall anomalies and the spatial amplitude and phase for the first CEOF mode (CEOF1) based on TMPA are illustrated in Figs. 2a,b.A single ensemble member was used for each model for 1981-2005. The TMPA data are available for a shorter time period (13 yr), but the sensitivity of the results to different sample sizes (based on data from a selected model) was found to be small. Similar to Maloney et al. (2008), the maximum amplitude of the observed rainfall CEOF1 occurs over the far eastern part of the EP. Figure 2b illustrates the pattern of spatial phase of observed rainfall CEOF1. In agreement with previous studies, the observed leading ISV mode associated with the CEOF1 largely exhibits an eastward propagation, while a northward component is also evident (e.g., Jiang and Waliser 2008; Maloney et al. 2008; Jiang et al. 2011).
Next, the fidelity of the CMIP5 models in simulating the leading EP ISV mode is assessed by calculating pattern correlations of the simulated rainfall CEOF1 against observations. To increase sampling, spatial patterns of rainfall anomalies associated with the CEOF1 based on both observations and model simulations are derived at two quadratic phases by multiplying the CEOF1 amplitude by the cosine and sine of spatial phase at each grid point, respectively. The pattern correlations are then calculated at both of these two quadratic phases. A final pattern correlation for a particular model is derived by averaging these two pattern correlation coefficients. Figure 2c illustrates pattern correlations in depicting the CEOF1 rainfall pattern for eachmodel simulation versus domain-averaged CEOF1 amplitude relative to observations, which provide measures of model performance of variability in space and time, respectively. A majority of the CMIP5 models tend to underestimate the amplitude of the leading EP ISV mode associated with the rainfall CEOF1, except CNRM-CM5, MIROC5, MPIESM-LR, HadGEM2-CC, and HadGEM2-ES. Among the 18 models examined, 8 models exhibit relative higher pattern correlations (.0.75).
The models with relative better skill in representing the leading EP ISV mode also largely exhibit better skill for summer-mean rainfall (cf. Figs. 1, 2c) and 850-hPa wind patterns (not shown). A common feature among the more skillful models is the presence of westerly or very weak easterly mean low-level winds over the EP warm pool region, as in the observations. Most of the models with relatively lower skill exhibit a stronger easterly summermean flow (.4ms21). This suggests that realistic representation of the mean state could be crucial for improved simulations of the EP ISV, which is in agreement with a recent study by Rydbeck et al. (2013), and has also been discussed for Madden-Julian oscillation (MJO) simulations over the western
b. Midsummer drought over
The rainy season in
Figure 3 shows the observational and CMIP5 estimates of the MSD and highlights the large uncertainties in its spatial distribution among observational datasets. The CMIP5 multimodel ensemble (MME) does reasonably well at representing the essence of the MSD over much of the inter-
4. East
a. Tropical storm track
The density of traveling synoptic-scale disturbances across the tropics, referred to in the literature as the tropical storm track (e.g., Thorncroftand Hodges 2001; Serra et al. 2008, 2010), is examined in this section. These systems serve as precursors to a majority of tropical storms and hurricanes in the Atlantic and eastern North Pacific and their frequency at 850 hPa overAfrica and the eastern Atlantic has been shown to be positively correlated with Atlantic hurricane activity (Thorncroftand Hodges 2001). As global models better resolve these systems than tropical cyclones, they provide an advantage over direct tracking of tropical cyclones to assess model tropical storm activity (see section 4b). As in Serra et al. (2010), the tropical storm track density is calculated based on the method of Hodges (1995, 1999) using smoothed, 6-hourly, 850-hPa relative vorticity. Only positive vorticity centers with a minimum threshold of 0.5 × 10-6 s-1 that persist for at least 2 days and have tracks of at least 1000 km in length are included in the analysis. This method primarily identifies westward moving disturbances such as easterly waves (e.g., Serra et al. 2010), although more intense storms that could potentially reach hurricane intensity are not excluded. We analyze a single ensemble member from nine CMIP5 models and compared the track statistics to the ERA-Interim (Fig. 4, left). These models were selected based on whether the 6-hourly pressure level data were available at the time of the analysis. Mean track strength, the mean of the smoothed 850-hPa vorticity along the track, is also examined (Fig. 4, right).
The multimodel mean track density is in good agreement with ERA-Interim; however, significant differences are seen with the individual models. The most apparent discrepancies are with the BCC-CSM1.1, CanESM2, and CCSM4 models, which strongly overestimate activity across the eastern
To better understand the biases in mean track density and strength, we examine the spatial correlations of 850- and 500-hPa winds and heights, as well as track density and strength with ERA-Interim. While all nine models have relatively good spatial correlations in the wind components and heights at 500 hPa (not shown), there is a wide spread in performance at the 850-hPa level that corresponds reasonably well with the rankings for the combined track density and strength correlations (Table 4). In particular, the top two models for the combined 850-hPa wind and height correlations (CNRM-CM5 and HadGEM2-ES) are also among the highest ranked for the combined track density and strength correlations. On the other hand, CanESM2 has a high ranking in the combined 850-hPa index but is one of the poorer models with respect to track density and spatial correlations, suggesting that there are other important factors contributing to the track statistics than just the large-scale low-level heights and winds across the region.
b. Tropical cyclones in the North Atlantic and eastern North Pacific
It is well known since the 1970s that climate models are able to simulate tropical cyclone-like storms (e.g.,
Tropical storms and cyclones in this study are identified using the tracking method of Camargo and Zebiak (2002), which uses low-level vorticity, surface winds, surface pressure, and atmospheric temperature and considers only warm core storms. The method uses model-dependent (and resolution) thresholds and storms have to last at least 2 days.Only a subset of the tropical disturbances examined in the previous section will intensify enough to be identified by this tracking method and the percentage that this occurs will vary among different models.As will be shown, the CMIP5 standard models have trouble simulating the number of tropical cyclones, which can be attributed in part to their coarse resolution. Therefore, we also show results from the GFDL high-resolution model.
TC-type structures were tracked in five models for 1950-2005. We compare with observations from besttrack datasets of the
Figure 6 shows the mean number of TCs per month for the North Atlantic and eastern North Pacific. In some cases, the models produce too many storms in the offseason, while all models produce too few storms in the peak season. The bottom panels show the spread of the number of storms per year, emphasizing the low number of storms per year in all models. The highest-resolution model MRI-CGCM3 (1.18 3 1.18) has the least bias relative to the observations and the highest bias is for the coarsest-resolution model (GFDL-ESM2M; 2.58 3 2.08). However, resolution cannot explain the rankings for all models, with the HadGEM2-ES and MPI-ESM-LR models having relatively large and small biases, respectively, despite both having intermediate resolutions. The model dynamical core, convection scheme and their interactions are other factors that have been shown to be important (Camargo 2013). Examination of variability across ensemble members in producing tropical cyclones was carried out for five member runs of the MRICGCM3 model (not shown) but was much less than among different models.
Figure 7 shows results for the GFDL-C180-
5. Interannual to decadal variability
a.
The El Niño-Southern Oscillation (ENSO) is the most important driver of global climate variability on interannual time scales. It impacts many regions worldwide through climate teleconnections (Ropelewski and Halpert 1987), which link the tropical
1) EVALUATION OF ENSO TELECONNECTIONS
We examine how well the historical simulations of CMIP5 models reproduce the composite near-surface air temperature (SAT) and precipitation patterns over
In observations, approximately 90% of El Niño and 89% of La Niña episodes feature peak amplitudes in fall or winter. In the CMIP5 ensemble of the historical simulations, however, only 68% of El Niño and 65% of La Niña episodes have peak amplitudes in fall or winter, although several of the models (CanESM2, CNRMCM5, HadCM3, and NorESM1-M) do have fall-winter peak frequencies exceeding 80% for both El Niño and La Niña episodes. This finding suggests that CMIP5 models do not fully reproduce the phase locking of
Because the dynamics of extratropical
Figure 9 shows the composites of 300-hPa geopotential height, SAT, precipitation, and tropical SST for El Niño. The corresponding composites for La Niña (not shown) are quite similar but of opposite sign. The higher performance ensemble performs rather well in capturing the basic El Niño geopotential height, SAT, and precipitation teleconnections over the North Pacific and
To gain insight into possible reasons for the discrepancies between the higher and lower performance ensemble, Fig. 10a shows composite differences in tropical precipitation. The higher performance ensemble exhibits much higher precipitation anomalies in the central and eastern equatorial
2) EAST PACIFIC-CENTRAL PACIFIC ENSO AND TELECONNECTIONS WITH U.S. WINTER SURFACE AIR TEMPERATURE
It has been increasingly recognized that different types of
We repeated the EOF and regression analyses to evaluate how well the CMIP5 models reproduce the different U.S. impacts to the two types of
3) ENSO WARM-COLD EVENTS ASYMMETRY
Lack of
b. Persistent droughts and wet spells over Great Plains and the southern-tier states
Persistent dry and wet summers are features of the U.S. Great Plains and southern
Comparison of the two figures indicates some similarities but also very different processes causing the persistent wet or dry summers. The integrated moisture fluxes in both datasets indicate high moisture in an averaged cyclonic rotation in the troposphere in persistent wet summers (Figs. 15a, 16a) but anticyclonic rotation in dry summers (Figs. 15b, 16b) in the Great Plains. However, the sources of the moisture and the low-level dynamic structure are quite different. For the reanalysis, the convergence of moisture in the central Great Plains during wet summers results from southerly flow anomalies in the enhanced subtropical high pressure system in the North Atlantic and northerly flow anomalies in low pressure anomalies centered in the Midwest (Fig. 15d). These anomalies suggest a frontal system along the depression from the Midwest to the Southwest. A nearly reversed pattern of flow anomalies is shown during the dry summers (Figs. 15e,f). The model simulations show a different pattern of flow anomalies (Figs. 16d,e). In wet summers, the moisture is primarily from the east along the easterly and southeasterly quadrants of a high pressure anomaly center in the
Although the integrated moisture fluxes in the models resemble those in the reanalysis estimates in wet and dry summers, the sources of moisture differ considerably, suggesting that the models are not correctly representing the mechanisms that force variability in the Great Plains. Controls on summertime Great Plains precipitation have been found to depend strongly on moisture transport from the
6. Decadal variability
a. PDO and its influence on North American climate
On interdecadal time scales, variability in the tropical and extratropical North Pacific, particularly that of the
For each set of seasonal temperature and precipitation regressions, we calculate the centered pattern correlations and RMS differences between the observed and CMIP5 model regressions (Table 5). Despite fairly low pattern correlations in many cases, for most models and most seasons the differences in the regression patterns are not statistically significant. This may be due to a combination of small effective sample size, large uncertainty in the regression coefficients, a relatively modest impact of the PDO on seasonal SAT and precipitation, and the ability of the models to capture the general PDO behavior during the winter and spring when the PDO impacts are strongest. In particular, the full ensemble performs well in capturing the winter and spring PDO SAT patterns, but substantial differences in the precipitation regressions are evident, particularly in spring.
Figure 18 shows the DJF SAT and precipitation regressions in observations and the CMIP5 ensemble. The CMIP5 models do rather well in capturing the PDO influence on North American SAT, with positive (negative) SAT anomalies in northwestern (southeastern)
b. AMO
The Atlantic multidecadal oscillation (AMO) is an important mode of multidecadal climate variability manifesting in North Atlantic SSTs (e.g., Kerr 2000; Enfield et al. 2001). The AMO has significant regional and global climate associations, such as northeast Brazilian and Sahel rainfall (e.g., Folland et al. 1986; Rowell et al. 1995;Wang et al. 2012), hurricane activity in the North Atlantic and the eastern North Pacific (Goldenberg et al. 2001; Wang and Lee 2009), and North American and European summer climate (Enfield et al. 2001; McCabe et al. 2004;
The AMO index is defined as the detrended North Atlantic SST during the Atlantic hurricane season of June-November (JJASON) from the equator to 608N and from 758 to 58W with the 11-yr running mean (e.g., Enfield et al. 2001; Knight et al. 2005). As shown in Fig. 19a, the individual models show highly varying amplitudes and phases, with a large spread across models. This is to be expected given that the AMO is likely of internal origin. All models show the warming in the last two decades when anthropogenic warming becomes influential. The MME mean tends to follow the main variations in the earlier part of the record, albeit subdued because of averaging across models, but fails to show the warm period during 1926-65. Compared to the CMIP3 results (Medhaug and Furevik 2011), the CMIP5 simulation of the AMO has generally improved, particularly after 1960. This may be due to higher resolution, improved parameterizations, and the addition of time-evolving land cover. Results for individual models (Table 6) indicate that the standard deviations are comparable to or slightly weaker than the observations with typical amplitudes ranging from 0.098 to 0.19°C as compared to about 0.18°C in the observations, which is an improvement from CMIP3 models (Ting et al. 2009).
The lagged autocorrelation of theAMOindex for lags zero to 35 yr (Fig. 19b) shows that the models generally represent the quasi-periodic nature of the observed AMO, with the peak oscillation at 30-35 yr in the observation but generally shorter for the models. The persistence in the AMO index as defined as the maximum time lag when the autocorrelation first crosses the significance line at the 10% level, and varies from 5 to 25 yr in the models, implying the potential for predicting future SSTs (Corti et al. 2012; H.-
The ability of the models to represent the AMO and its impact on precipitation over
In general, the models do not capture the SST seasonality of theAMOwell. The simulated SST anomalies are generally larger in summer than in autumn in the majority of the models (not shown). While all models tend to place the maximum SST anomalies over the mid-
The precipitation impact of the AMO is a bigger challenge for the models (see Table 7 for individual model spatial correlations for precipitation), and they generally fail to represent the drier conditions over the central
Numerous studies have shown the importance of the AMO in generating precipitation variability over the region (e.g., Enfield et al. 2001;
7. Multidecadal trends
a. Trends in temperature and the ''warming hole'' over the southeastern
A unique of feature of U.S. temperature change during the twentieth century is the so-called warming hole (WH) observed in the southeastern
We evaluate whether the CMIP5 models show the warming hole as a forced response in Fig. 21, which shows the annual and seasonal trends, in near-surface air temperature from the observation and the CMIP5 multimodel mean from 17 models (see Fig. 21 caption). Model and observation data are regridded to a common resolution 2.58 3 2.58 using area averaging. Trends are calculated for the 1930-2004 period using the Theil-Sen approach (Theil 1950; Sen 1968). The choice of 1930-2004 gives a prominent WH signal in the observations starting from the warmest decade following the Dust Bowl drought. Only one ensemble member from eachmodel is included in the analysis as ensemble members from the same model show similar spatial patterns of long-term trends (Kumar et al. 2013b). The MME mean shows neither a cooling trend in the eastern
Figure 22 shows the temporal evolution of 30-yr moving window annual temperature trends over the eastern
b. Trends in DTR
Observed warming during the day and night has been asymmetric, with nocturnal minimum surface air temperature (Tmin) rising about twice as fast than daytime maximum temperature (Tmax) during the second half of twentieth century, mostly during 1950-80 (Vose et al. 2005). Changes in cloud cover, atmospheric water vapor, soil moisture, and other factors account for 25%-50% of the diurnal temperature range (DTR) reduction (Dai et al. 1999). Cloud cover, soil moisture, precipitation, and atmospheric/oceanic teleconnections account for up to 80% of regional variance over 1901-2002. Over
Figure 23 shows a comparison of DTR magnitude and the linear trend in DTR from 17 models against the CRU TS3.1 observational dataset. The observed mean DTR (Tmax - Tmin) is characterized by high values over the western high mountainous regions in summer and low values in high latitudes (Fig. 23a). The MME-mean simulates this general pattern with underestimation in the mountains. The observedDTR trend is predominantly negative in
c. Trends in precipitation
Precipitation has generally increased over
8. Discussion and conclusions
This study has evaluated the simulated variability from the CMIP5 multimodel ensemble at intraseasonal to multidecadal time scales for
For the climate features and models analyzed here, there does not appear to be a great deal of improvement since CMIP3. For example, CMIP5 models still cannot capture the seasonal timing of
The results have implications for the interpretation and robustness of the model projected future changes. Part III evaluates the model projections for a subset of the features analyzed here and in Part I. As noted in Part I, the accurate simulation of historic climate features is not sufficient for credible projections, although the depiction of large-scale climate features is necessary. Several studies of future projections show only small differences between models that do better at replicating observations and those that do worse (e.g., Brekke et al. 2008; Knutti et al. 2010), while others have found relationships between model performance and future projections that can be related to physical processes (e.g., Hall and Qu 2006; Boe et al. 2009). However, these types of studies are generally specific to certain climate features that do not necessarily provide confidence or pessimism in model skill in a broader sense.
The adequate depiction of the variability is nevertheless necessary because this is generally associated with the more extreme aspects of climate that impose the largest impacts. Furthermore, the depiction of the teleconnections associated with large-scale variability is especially important because the impacts of potential changes in the variability of, say,ENSO(vanOldenborgh et al. 2005; Muller and Roeckner 2008) are subject to uncertainties in the representation of teleconnections (Part III). Model variability can also have a large impact on future changes because the signal to noise ratio can be highly dependent on the model's natural variability resulting inmisleading assessments of future changes and uncertainties across models (Tebaldi et al. 2011). The ability of the models to reproduce the observed trends may be a better indicator of model reliability than depiction of the mean climate or even its variability, because this indicates the model's sensitivity to an external forcing that may continue into the future, such as greenhouse gas concentrations. The problem here is that the trend analyzed is subject to uncertainties in the observations, the complications of natural variability in the real world and models, and uncertainties in feedbacks and how they may change in the future (Reuroaiseuroanen 2007; Knutti 2010). The generally poor ability of the models to reproduce the trends in precipitation, DTR, and some features of regional temperature shown here are indicative of this.
Acknowledgments. We acknowledge the
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JUSTIN SHEFFIELD,a SUZANA J. CAMARGO,b RONG FU,c QI HU,d XIANAN JIANG,e NATHANIEL JOHNSON,f KRISTOPHER B. KARNAUSKAS,g SEON TAE KIM,h JIM KINTER,i SANJIV KUMAR,i
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pCooperative
q Physical Oceanography Division,
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tNOAA/Geophysical Fluid Dynamics Laboratory,
(Manuscript received
Corresponding author address:
E-mail: [email protected]
DOI: 10.1175/JCLI-D-12-00593.1
Copyright: | (c) 2013 American Meteorological Society |
Wordcount: | 15570 |
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