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    Moisture budget for the observations. The colors show (a) precipitation (mm day−1; GPCP), (b) evapotranspiration (mm day−1; ERAI), and (c) moisture convergence estimated as PE (mm day−1; ERAI). The vectors in (c) show the moisture flux at 850 hPa (kg kg−1 m s−1; ERAI). The regional average precipitation over western and eastern Eurasia in section 3 is calculated in the left and right black boxes, respectively. The precipitation events in section 4 are defined as lag regressions with respect to the daily precipitation time series over the red boxes in western Eurasia and eastern Eurasia.

  • View in gallery

    Skill scores (Taylor 2001) for the spatial climatological JJA precipitation pattern over northern Eurasia (45°–90°N, Eastern Hemisphere) for the observations, reanalyses, and CMIP5 models. The reference is GPCP2. Characters O, R, M, H, L, and 5 below the figure indicate observations, reanalyses, MME, HSM, LSM, and the other CMIP5 models, respectively.

  • View in gallery

    JJA precipitation from the observation data, reanalyses, and CMIP5 models (mm day−1).

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    As in Fig. 3, but with anomalies from GPCP2.

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    As in Fig. 1, but using the MME mean.

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    As in Fig. 1, but for the MME bias from GPCP2–ERAI data. The hatched areas have a significance level >90%.

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    Differences in precipitation from the (a) convective and (b) large-scale condensation schemes between MME and ERAI (mm day−1). The hatched areas have a significance level >90%.

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    Scatterplot of the precipitation skill scores (abscissa) against the difference in precipitation between western Eurasia (45°–70°N, 15°–60°E) and eastern Eurasia (45°–70°N, 105°–150°E) (mm day−1; ordinate). The correlation coefficient for the CMIP5 models is 0.78. MME, HSM, and LSM are indicated by M, H, and L.

  • View in gallery

    MME bias for (a) surface air temperature (K), (b) sea level pressure (hPa), and (c) temperature (K, shading) and zonal wind (m s−1, contours) averaged at 40°–100°E from the CRU–ERAI data. The hatched areas have a significance level >90%.

  • View in gallery

    Scatterplot of the precipitation skill scores (abscissa) against (a) the average surface air temperatures for central Eurasia (40°–60°N, 20°–110°E) (°C; ordinate) and (b) the meridional wind difference between western Eurasia (45°–70°N, 15°–60°E) and eastern Eurasia (45°–70°N, 105°–150°E) at 850 hPa (m s−1; ordinate). MME, HSM, and LSM are indicated by M, H, and L.

  • View in gallery

    MME bias of the surface heat budget (W m−2) from CERES–ERAI data: (a) latent heat, (b) sensible heat, (c) shortwave radiation, (d) longwave radiation, and (e) shortwave cloud radiative forcing. The hatched areas have a significance level >90%.

  • View in gallery

    Scatterplot of the precipitation skill scores (abscissa) against the average shortwave cloud radiative forcing (W m−2) over central Eurasia (40°–60°N, 20°–110°E) (%; ordinate). MME, HSM, and LSM are indicated by M, H, and L.

  • View in gallery

    MME bias for the evaporation–precipitation index from the ERAI data. The hatched areas have a significance level >90%.

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    Scatterplot of the precipitation skill scores (abscissa) against the evaporation–precipitation indices over northern Eurasia (45°–90°N, Eastern Hemisphere) (ordinate). MME, HSM, and LSM are indicated by M, H, and L.

  • View in gallery

    Lag regressions for precipitation (colored, mm day−1; GPCP1DD) and Z500 (contoured, m; ERAI) with respect to the precipitation time series (GPCP1DD) using a 5° × 5° box for (left) western Eurasia (55°–60°N, 35°–40°E) and (right) eastern Eurasia (55°–60°N, 125°–130°E), from (top)–(bottom) day −2 to day +1.

  • View in gallery

    Lag regressions for (a),(b) precipitation (mm day−1), (c),(d) evaporation (mm day−1), (e),(f) PE (mm day−1), (g),(h) Z500 (m), and (i),(j) atmospheric instability (°C), against the precipitation time series using a 5° × 5° box, for (left) western Eurasia (55°–60°N, 35°–40°E) and (right) eastern Eurasia (55°–60°N, 125°–130°E). The abscissa shows the lag time (day). Red solid, red dashed, black solid, black dashed, and black dashed-dotted lines show the GPCP1DD, APHRODITE, ERAI, MME, HSM, and LSM, respectively. The shaded areas are the intermodel spreads (±1σ).

  • View in gallery

    The maximum precipitation intensity (colored; mm day−1) and the duration time (hatching) of the events with precipitation >10% of the maximum intensity on each grid (see Fig. 16a for an example of the maximum intensity and the duration time) for (a) APHRODITE and (b) MME. Blue and red hatchings show the duration time shorter and longer than 3.7 days, respectively.

  • View in gallery

    (a) Tendency of vertically integrated water and (b) convergence of vertically integrated moisture flux from ERAI (mm day−1). The vectors in (b) show the vertically integrated moisture flux (kg kg−1 m s−1). Values are very small everywhere in (a).

  • View in gallery

    (a),(c),(e),(g) HSM and (b),(d),(f),(h) LSM biases for (a),(b) precipitation (mm day−1), (c),(d) surface air temperature (°C), (e),(f) shortwave cloud radiative forcing (W m−2), and (g),(h) the evaporation–precipitation index. The references are (a),(b) GPCP2, (c),(d) CRU, (e),(f) CERES, and (g),(h) ERAI. The hatched areas have a significance level >90%.

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Reproducibility of Summer Precipitation over Northern Eurasia in CMIP5 Multiclimate Models

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  • 1 National Institute of Polar Research, and Atmosphere and Ocean Research Institute, The University of Tokyo, Tokyo, Japan
  • | 2 Atmosphere and Ocean Research Institute, The University of Tokyo, Tokyo, Japan
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Abstract

Reproducibility of summer precipitation over northern Eurasia in climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is evaluated in comparison with several observational and reanalysis datasets. All CMIP5 models under- and overestimate precipitation over western and eastern Eurasia, respectively, and the reproducibility measured using the Taylor skill score is largely determined by the severity of these west–east precipitation biases. The following are the two possible causes for the precipitation biases: very little cloud cover and very strong local evaporation–precipitation coupling. The models underestimate cloud cover over Eurasia, allowing too much sunshine and leading to a warm bias at the surface. The associated cyclonic circulation biases in the lower troposphere weaken the modeled moisture transport from the Atlantic to western Eurasia and enhance the northward moisture flux along the eastern coast. Once the dry west and wet east biases appear in the models, they become amplified because of stronger evaporation–precipitation coupling. The CMIP5 models reproduce precipitation events well over a time scale of several days, including the associated low pressure systems and local convection. However, the modeled precipitation events are relatively weaker over western Eurasia and stronger over eastern Eurasia compared to the observations, and these are consistent with the biases found in the seasonal average fields.

Denotes Open Access content.

Corresponding author address: Nagio Hirota, National Institute of Polar Research, 10-3, Midoricho, Tachikawa, Tokyo 190-8518, Japan. E-mail: nagio@aori.u-tokyo.ac.jp

Abstract

Reproducibility of summer precipitation over northern Eurasia in climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is evaluated in comparison with several observational and reanalysis datasets. All CMIP5 models under- and overestimate precipitation over western and eastern Eurasia, respectively, and the reproducibility measured using the Taylor skill score is largely determined by the severity of these west–east precipitation biases. The following are the two possible causes for the precipitation biases: very little cloud cover and very strong local evaporation–precipitation coupling. The models underestimate cloud cover over Eurasia, allowing too much sunshine and leading to a warm bias at the surface. The associated cyclonic circulation biases in the lower troposphere weaken the modeled moisture transport from the Atlantic to western Eurasia and enhance the northward moisture flux along the eastern coast. Once the dry west and wet east biases appear in the models, they become amplified because of stronger evaporation–precipitation coupling. The CMIP5 models reproduce precipitation events well over a time scale of several days, including the associated low pressure systems and local convection. However, the modeled precipitation events are relatively weaker over western Eurasia and stronger over eastern Eurasia compared to the observations, and these are consistent with the biases found in the seasonal average fields.

Denotes Open Access content.

Corresponding author address: Nagio Hirota, National Institute of Polar Research, 10-3, Midoricho, Tachikawa, Tokyo 190-8518, Japan. E-mail: nagio@aori.u-tokyo.ac.jp

1. Introduction

Terrestrial precipitation at high latitudes plays an important role in hydrological cycles and has been attracting increasing interest because its associated processes affect global warming. It can be stored as snow in the winter and melts and drains into the oceans during the spring and summer. River discharges are the primary freshwater sources of the Arctic Ocean (Aagaard and Carmack 1989), and this freshwater influences stratification and, therefore, sea ice formation. Terrestrial snow cover and sea ice have high albedos, keeping high-latitude surfaces cool, and a decrease in this cooling effect is considered to be a major feedback process in the polar amplification of global warming (Curry et al. 1995; Yoshimori et al. 2014). Freshwater from the Arctic Ocean, which flows into the Atlantic through the Fram Strait, also has important effects on the global thermohaline circulation (Walsh and Chapman 1990).

Many previous studies have analyzed observational and reanalysis data to understand precipitation processes over northern Eurasia (Serreze and Etringer 2003; Tachibana et al. 2008; Iwao and Takahashi 2008; Fukutomi et al. 2012). The annual terrestrial precipitation cycles show winter minima and summer maxima, reflecting that the saturated vapor pressure is controlled by temperature. In long-term-average fields, the moisture that becomes summer precipitation mostly corresponds to surface evapotranspiration, whereas the atmospheric moisture convergence generally reaches a minimum in the summer (and negative values in the Ob River basin in western Eurasia). The large contribution of evaporation suggests active water recycling, in which atmospheric moisture is removed as precipitation and resupplied by evaporation. The main origin of moisture is the North Atlantic Ocean and the moisture travels across Eurasia from west to east (Numaguti 1999). In addition, the moisture in eastern Eurasia is transported northward by circulations of the Asian summer monsoon (Tachibana et al. 2008). It has been shown that summer precipitation over northern Eurasia has large daily variations and is related to local convection and synoptic low pressure systems (Serreze et al. 2001; Serreze and Etringer 2003; Fukutomi et al. 2004, 2007; Sorteberg and Walsh 2008; Jakobson and Vihma 2010). Low pressure systems, along with southerly moisture transport to the east of the systems, are frequently mentioned as being important factors in heavy precipitation events, which often cause extreme floods, with negative impacts on society (Jacobeit et al. 2006; Nuissier et al. 2011; Stucki et al. 2012). The importance of circulations for summer precipitation variations in interannual and intraseasonal time scales is also documented in Iwao and Takahashi (2008) and Fukutomi et al. (2012), respectively.

There have only been limited evaluations of climate models with respect to high-latitude precipitation and related atmospheric processes. Kattsov et al. (2007) analyzed the multimodel dataset from the World Climate Research Programme’s (WCRP) phase 3 of the Coupled Model Intercomparison Project (CMIP3) and examined its precipitation distributions in the Arctic Ocean terrestrial watersheds (the Lena, MacKenzie, Ob, and Yenisey Rivers). The CMIP3 models generally reproduce the seasonal precipitation cycles, with summer maxima and winter minima, qualitatively, but the predicted amount of summer precipitation is relatively low in the Ob River basin and high in the other three basins compared to the observations. Although Kattsov et al. (2007) discussed the possible causes for the precipitation biases, suggesting that they may be related to errors in the large-scale circulation, convective, and land hydrology schemes, the mechanisms that cause these biases remain unclear. They ended their discussion by suggesting that further study of precipitation mechanisms is required.

Here, we examine the reproducibility of summer precipitation over northern Eurasia in the CMIP5 climate models and discuss the physical processes that determine the reproducibility. CMIP5 is the most recent CMIP phase and is expected to provide new insights into the climate for the fifth assessment report of the Intergovernmental Panel on Climate Change. This study is based on the CMIP3 evaluation by Kattsov et al. (2007), but we focus on understanding the physical processes. We investigate a range of variables over large time scales (seasonal averages) and precipitation events with shorter time scales (of several days). This study focuses on the summer data only. The other seasons will be analyzed in future work and may be more difficult to analyze because there are large uncertainties in solid precipitation (snowfall) observations. For example, the snow-catching efficiency of a gauge orifice is reduced in windy conditions, and ground snow can be blown into the gauge. Measurement errors during the snow seasons can be in the order of 100% (Yang et al. 2005).

We describe the data used in our analyses in section 2, and we assess the horizontal distribution of the seasonal average precipitation along with the atmospheric and land surface conditions in section 3. The time evolutions of typical precipitation events are presented in section 4, and section 5 contains a summary and discussion.

2. Data

We analyzed output data from historical experiments of 22 CMIP5 models (Table 1) with daily data available from the Program for Climate Model Diagnosis and Intercomparison (Taylor et al. 2012; http://www-pcmdi.llnl.gov). The variables used included evaporation, geopotential height, specific humidity, meridional wind, precipitation, sea level atmospheric pressure, surface radiative heat flux, surface air temperature at 2 m above ground level, air temperature at the upper levels, total cloud cover, and zonal wind.

Table 1.

A list of model names and modeling centers.

Table 1.

We compared the model outputs with observational precipitation datasets, which were derived from the Asian Precipitation Highly Resolved Observation Data Integration Toward Evaluation (APHRODITE; Yatagai et al. 2012) project, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997), and the Global Precipitation Climatology Project (GPCP; Huffman et al. 1997; Adler et al. 2003). Note that the GPCP we used is the monthly product of version 2.2 (GPCP2) and the 1° daily product of GPCP1DD. The GPCP dataset is a gridded precipitation dataset based on satellite retrievals, calibrated against gauge observations that are scaled using monthly correction factors published by Legates and Willmott (1990), for the wind-induced undercatch of snow measurements. CMAP is also based on satellite and gauge observations, whereas APHRODITE uses only gauge measurements. CMAP and APHRODITE do not include snow undercatch corrections. Reference values for the other variables were taken from monthly instrumental land–surface–air temperature records compiled by the University of East Anglia’s Climatic Research Unit (CRU; Jones et al. 2012) and monthly surface radiative heat flux records from satellite observations of the Clouds and the Earth’s Radiant Energy System (CERES; Kato et al. 2013).

In addition, datasets from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005), ECMWF interim reanalysis (ERAI; Simmons et al. 2007), the Japanese 25-year Reanalysis Project (JRA-25; Onogi et al. 2007), the Japanese 55-yr Reanalysis Project (JRA-55; Kobayashi et al. 2015), the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis (NCEP; Kalnay et al. 1996), the NCEP–U.S. Department of Energy (DOE) Atmospheric Model Intercomparison Project phase II (AMIP-II) reanalysis (NCEP-2; Kanamitsu et al. 2002), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA; Bosilovich et al. 2006) are used. These reanalysis datasets are produced from short-term forecasts with the assimilation of observational data, and they provide analyzed and forecast fields useful for evaluating various atmospheric and land surface conditions. Comparing many datasets allows the uncertainties in the observations and reanalyses, which may be large over northern Eurasia (Serreze et al. 2005; Troy et al. 2011), to be assessed. Note that precipitation results in reanalyses are predicted values using the forecasting model from the analyzed initial fields. They are not based on the observed precipitation but are consistent with the analyzed large-scale atmospheric fields.

The analyses were performed over northern Eurasia (45°–90°N, Eastern Hemisphere) for June–August (JJA) 1981–2000. The climatological average was defined as the average between 1981 and 2000. Because GPCP1DD and CERES observations are available only for 1997–2012 and 2000–09, respectively, the averages over those periods were used for these datasets. Because coupled models of CMIP5 have different timing on the decadal variabilities, the relatively short period of averaging may affect the climatology. However, comparing the 1981–90 average and the 1991–2000 average in the CMIP5 models, we have confirmed their differences are very small compared to differences among the models (not shown). Therefore, we consider that the effects of the decadal variabilities do not influence our main conclusions about the evaluations of climatology in the CMIP5 models. All data used were linearly interpolated into 2.5° × 2.5° horizontal grids to allow comparisons to be made.

3. Precipitation reproducibility in the seasonal average fields

In this section, we first describe the observed JJA average precipitation over northern Eurasia, and then we assess the reproducibility of the precipitation and its associated atmospheric and land surface conditions in the CMIP5 models.

Summer precipitation, from the GPCP2 observations, over northern Eurasia is shown in Fig. 1, along with the evaporation and precipitation minus evaporation (P − E), from the ERAI data. A moisture budget of an atmospheric column in reanalysis data can be written as
eq1
where W is vertically integrated water (vapor + liquid + ice), C is the vertically integrated moisture convergence, and A is an analysis increment. The analysis increment is an artificial modification to the short-term forecasts that reduces their differences from the observational data in the assimilation system. The tendency is negligible in the seasonal average fields, and we found that the P − E and vertical integrated moisture convergence results are very similar, suggesting that the analysis increments are very small in ERAI (appendix A). This was also true for the ERA-40 data, and the small increment size was mentioned as being an improvement on the previous 15-yr ECMWF Re-Analysis by Serreze et al. (2005). This enabled us to use P − E instead of the vertically integrated moisture convergence (appendix A), which is not available in the CMIP5 archive. Note that evaporation in reanalyses is usually considered to have large uncertainties. Albergel et al. (2012) have shown that soil moisture from ERAI is generally overestimated compared to in situ observations, while its variability is relatively well reproduced. Therefore, evaporation from ERAI may be biased as well. However, we consider that the biases in ERAI do not influence our conclusions because they are small compared with the model biases (see also appendix A, which shows similar distributions of P − E and moisture convergence from ERAI).
Fig. 1.
Fig. 1.

Moisture budget for the observations. The colors show (a) precipitation (mm day−1; GPCP), (b) evapotranspiration (mm day−1; ERAI), and (c) moisture convergence estimated as PE (mm day−1; ERAI). The vectors in (c) show the moisture flux at 850 hPa (kg kg−1 m s−1; ERAI). The regional average precipitation over western and eastern Eurasia in section 3 is calculated in the left and right black boxes, respectively. The precipitation events in section 4 are defined as lag regressions with respect to the daily precipitation time series over the red boxes in western Eurasia and eastern Eurasia.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

The observations show zonally extending precipitation around 45°–70°N along the Arctic coast, with small maxima over the western and eastern parts of Eurasia (Fig. 1a). The large moisture supply from evaporation over the continent (Fig. 1b) suggests active water recycling (Numaguti 1999; Serreze and Etringer 2003).

The moisture flux calculated as a product of daily wind and specific humidity at 850 hPa is shown using vectors in Fig. 1c. Since this flux neglects the effects of subdaily variabilities and is not vertically integrated over an air column, it may not fully describe the moisture convergence (or P − E ). However, we consider that this flux shows the moisture flow at least in a qualitative manner because the influence of subdaily variabilities is small (appendix A) and moisture is concentrated in the lower troposphere (Jakobson and Vihma 2010).

The moisture over western Eurasia mainly comes from the Atlantic Ocean (Numaguti 1999), slowly traveling across Eurasia from west to east. Southwesterlies from the East Asian monsoon transport moisture to higher latitudes along the eastern coast (Tachibana et al. 2008). The moisture convergence, estimated as P − E (color in Fig. 1c), is weakly negative over western Eurasia. This negative P − E consists of a stronger divergence of meridional moisture flux with a weaker convergence of zonal flux. The largest negative P − E is found from spring to early summer, which may be related to snowmelt moistening the land surface. It should be noted that the seasonal average precipitation shown in Fig. 1a comes from events with time scales of several days, with strong moisture convergence (positive P − E), despite the seasonal average convergence being negative, as will be discussed in section 4.

Next, we calculated the skill score, as defined by Taylor (2001), to evaluate the reproducibility of the climatological precipitation distributions in the JJA seasons over northern Eurasia (45°–90°N, Eastern Hemisphere). The score is defined as
eq2
where R is the pattern correlation between the models and the reference data, and SDR is the term used for the spatial standard deviation ratio. Therefore, this score is a measure of the similarity between the distribution and amplitude of the two spatial patterns. The skill scores for the observations, reanalyses, and CMIP5 models, calculated using GPCP2 as the precipitation reference dataset, are shown in Fig. 2, and their precipitation distributions and anomalies from GPCP2 are shown in Figs. 3 and 4, respectively. Naturally, the observations have higher skill scores than the reanalyses and CMIP5 models. Using different observations as the reference data did not influence our main results, because differences between observations are generally rather small compared to the biases in the CMIP5 models. Note, however, that this is not true for the winter, when snow measurements need to be adjusted, as described in section 1. Of the seven reanalyses we analyzed, JRA-25, JRA-55, ERA-40, ERAI, and MERRA showed reasonably good agreement with the reference data, but NCEP and NCEP-2 had relatively low skill scores. Limitations in the performance of the NCEP reanalyses have been documented previously (Serreze et al. 2005; Trenberth et al. 2011). The scores for the CMIP5 models varied widely, from 0.69 for MRI-CGCM3 to 0.30 for FGOALS-s2, with an average of 0.55, whereas that for the average precipitation field in the CMIP5 multimodel ensemble (MME) was 0.67. The score of MME tends to be high because of random errors canceling each other as described by previous studies, and this is often considered to be a good reason for using an MME in climate projections (Reichler and Kim 2008).
Fig. 2.
Fig. 2.

Skill scores (Taylor 2001) for the spatial climatological JJA precipitation pattern over northern Eurasia (45°–90°N, Eastern Hemisphere) for the observations, reanalyses, and CMIP5 models. The reference is GPCP2. Characters O, R, M, H, L, and 5 below the figure indicate observations, reanalyses, MME, HSM, LSM, and the other CMIP5 models, respectively.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Fig. 3.
Fig. 3.

JJA precipitation from the observation data, reanalyses, and CMIP5 models (mm day−1).

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Fig. 4.
Fig. 4.

As in Fig. 3, but with anomalies from GPCP2.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Many observed features of the moisture budget described above were well reproduced by the MME, as shown in Fig. 5. Precipitation occurs zonally at high latitudes. Moisture is transported from the Atlantic to western Eurasia, and a northeastward moisture flux can be seen along the eastern coast. The P − E trend is weakly negative over western Eurasia, and large contributions from evaporation suggest that active water recycling occurs over northern Eurasia.

Fig. 5.
Fig. 5.

As in Fig. 1, but using the MME mean.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Despite these agreements, we found systematic differences when we calculated the biases of the MME from the GPCP2 and ERAI references. Figure 6 shows the biases in the MME moisture budget, and it can be seen that the MME under- and overestimates precipitation over western and eastern Eurasia, respectively (Fig. 6a). These biases are statistically significant when compared with the intermodel variances of the CMIP5 models over most of northern Eurasia (hatched in Fig. 6a). In climate models, precipitation is produced by a convective scheme and a large-scale condensation scheme. The MME precipitation from these two schemes is compared with that of ERAI in Fig. 7. The dry bias associated with the convective scheme occurred over both western and eastern Siberia, whereas the large-scale condensation scheme created a large wet bias over eastern Siberia.

Fig. 6.
Fig. 6.

As in Fig. 1, but for the MME bias from GPCP2–ERAI data. The hatched areas have a significance level >90%.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Fig. 7.
Fig. 7.

Differences in precipitation from the (a) convective and (b) large-scale condensation schemes between MME and ERAI (mm day−1). The hatched areas have a significance level >90%.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Since the performance of the CMIP5 models varied widely (Fig. 2), we have analyzed groups of five highest scoring models (HSMs) and five lowest scoring models (LSMs). The selected models are indicated in Fig. 2. The precipitation biases for both the HSMs and LSMs are positive over western Eurasia and negative over eastern Eurasia similarly to that of MME (see appendix B).

Dry west and wet east biases were identified in all CMIP5 models analyzed (Fig. 4). We subtracted the precipitation for eastern Eurasia (45°–70°N, 105°–150°E; the right-hand black box in Fig. 6) from those for western Eurasia (45°–70°N, 15°–60°E; the left-hand black box in Fig. 6), and we plotted these values against the precipitation skill scores (Fig. 8). All models gave larger negative values for the west–east precipitation differences than the observations. In addition, the high correlation coefficient of 0.78 indicates that the precipitation reproducibility measured using the Taylor skill score corresponded with the severity of the dry west and wet east biases. The model groups of MME, HSM, and LSM also follow this relationship. Note that the observational spreads were, again, smaller than the model biases (Fig. 8). It is interesting that the reanalyses did not follow the relationship between the west–east differences and skill scores as their dots do not align along the line of the CMIP5 models. The skill scores of precipitation reproducibility in the reanalyses are not strongly related to the west–east contrast of the precipitation.

Fig. 8.
Fig. 8.

Scatterplot of the precipitation skill scores (abscissa) against the difference in precipitation between western Eurasia (45°–70°N, 15°–60°E) and eastern Eurasia (45°–70°N, 105°–150°E) (mm day−1; ordinate). The correlation coefficient for the CMIP5 models is 0.78. MME, HSM, and LSM are indicated by M, H, and L.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

We examined various atmospheric and land surface variables to attempt to understand the dry west and wet east precipitation biases. The moisture budget shown in Fig. 6 suggests that biases in the moisture supply from both evaporation and moisture convergence (P − E) contribute to the precipitation biases. The bias in the southwestward moisture flux at high latitudes in western Eurasia indicates that the modeled moisture transport from the Atlantic is very weak, whereas the northeastward moisture transport along the eastern coast from the monsoon southwesterlies is excessive (the vectors in Fig. 6c). These moisture transport biases are caused by circulation biases in the lower troposphere. As shown in Fig. 9, the MME has a warm bias over the continent, and corresponding cyclonic circulations (Fig. 6c) and surface low biases with a continental scale are identified. The surface warm and low biases are also reported in previous studies (e.g., Ma et al. 2014). The surface warming results in heating of the lower troposphere. Resulting expansion of the atmospheric column air causes the thermal low pressure system with the cyclonic circulation near the surface. The importance of the surface warming to the low-level circulations is reminiscent of the Asian monsoon circulations associated with the warm Tibetan plateau (Broccoli and Manabe 1992; Kitoh 2002).

Fig. 9.
Fig. 9.

MME bias for (a) surface air temperature (K), (b) sea level pressure (hPa), and (c) temperature (K, shading) and zonal wind (m s−1, contours) averaged at 40°–100°E from the CRU–ERAI data. The hatched areas have a significance level >90%.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Although the warm temperature bias near the eastern coast is relatively small, the MME temperature bias from ERAI around Japan is significantly negative (not shown), indicating enhancement of the land–sea contrast. Associated with this temperature gradient, the monsoon southwesterly is significantly stronger in MME.

Moreover, the eastward moisture flux bias over central Eurasia along 50°–60°N also makes some contributions to the wet bias in eastern Eurasia (Fig. 6c). This moisture transport is again associated with the surface warm bias. Figure 9c shows a latitude–height cross section of temperature and zonal wind biases averaged from 40° to 100°E. Because of the Iranian Plateau and the Tibetan Plateau, the effects of the surface warm bias reach around 700 hPa at 35°–60°N, resulting in a negative meridional temperature gradient in the lower troposphere that balances with the geostrophic westerlies.

The relationship between temperature and precipitation bias is further supported by the scatterplot of the surface air temperature averaged over northern Eurasia (40°–60°N, 20°–110°E) and the skill score for the precipitation reproducibility shown in Fig. 10a. There was a significant intermodel correlation (with a correlation coefficient of −0.74), indicating that the models with warmer surface air temperatures had poorer skill scores. Even when an outlier in the lowest scoring model (model h in Fig. 9) was excluded, the correlation remained highly significant, with a correlation coefficient of −0.50. We also plotted meridional wind differences between eastern Eurasia (45°–70°N, 105°–150°E) and western Eurasia (45°–70°N, 15°–60°E) at 850 hPa against the precipitation skill scores in Fig. 10b. Their correlation is significantly positive, confirming the importance of the continental-scale cyclonic circulation in the lower troposphere. These relationships also stand for the model groups of MME, HSM, and LSM.

Fig. 10.
Fig. 10.

Scatterplot of the precipitation skill scores (abscissa) against (a) the average surface air temperatures for central Eurasia (40°–60°N, 20°–110°E) (°C; ordinate) and (b) the meridional wind difference between western Eurasia (45°–70°N, 15°–60°E) and eastern Eurasia (45°–70°N, 105°–150°E) at 850 hPa (m s−1; ordinate). MME, HSM, and LSM are indicated by M, H, and L.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

We next examined the surface heat budget of MME compared with ERAI and CERES as shown in Figs. 11a–d. The ground temperature is determined by the surface heat budget and strongly influences the air temperature in the lower troposphere. The shortwave radiative heating corresponds well with the warm bias of the surface air temperature, whereas the latent heating also contributes to the warm bias over western Eurasia. The longwave radiative heating and the sensible heating bias are largely negative over western Eurasia, corresponding to the warm bias.

Fig. 11.
Fig. 11.

MME bias of the surface heat budget (W m−2) from CERES–ERAI data: (a) latent heat, (b) sensible heat, (c) shortwave radiation, (d) longwave radiation, and (e) shortwave cloud radiative forcing. The hatched areas have a significance level >90%.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

To further understand the shortwave radiative heating bias of MME, shortwave cloud radiative forcing (SCRF), defined as the difference between shortwave radiative heating for all skies and that for clear skies, is shown in Fig. 11e. The SCRF bias in the MME is significant and positive for most of Eurasia, as shown in Fig. 11. The CMIP5 models, therefore, underestimate cloud cover, allowing too much sunshine to reach the surface, resulting in the warm bias. As described above, the warm bias produces the continental-scale circulation biases. The cyclonic circulation biases reduce the moisture transport from the Atlantic and enhance the northeastward moisture transport along the eastern coast, contributing to the dry bias over western Eurasia and the wet bias over eastern Eurasia. The intermodel relationship between SCRF and precipitation reproducibility is shown in Fig. 12, indicating that they correlate significantly, which supports our finding that cloud cover is an important factor in determining the precipitation reproducibility. Despite the diminished cloud cover over for the majority of Eurasia, the clouds are overestimated around the far eastern part of Eurasia (135°E, 60°N). This is possibly because of the enhanced northward moisture flux (Fig. 6c) and/or may be one of the reasons for the moist bias in this region (Fig. 6a).

Fig. 12.
Fig. 12.

Scatterplot of the precipitation skill scores (abscissa) against the average shortwave cloud radiative forcing (W m−2) over central Eurasia (40°–60°N, 20°–110°E) (%; ordinate). MME, HSM, and LSM are indicated by M, H, and L.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

In the previous paragraph, we emphasized the importance of large-scale circulations associated with the surface air temperature and cloud cover biases in trying to understand the horizontal pattern of the dry west and wet east biases. These biases may be amplified in a local feedback process. In particular, the dry (wet) biases cause fewer (more) clouds with warm (cold) biases, resulting in further drying (moistening). Note, however, this local feedback process may amplify the bias, but does not explain the horizontal pattern of the dry west and wet east.

In addition to the moisture transport contribution described above, the precipitation biases are also affected by evaporation. We calculated the local evaporation–precipitation coupling index, defined as
eq3
where P′ and E′ are the JJA precipitation and evaporation anomalies, respectively, for each year, calculated from the climatological average (Zeng et al. 2010). The MME average of the index was significantly larger than that of ERAI (Fig. 13). Although evaporation in ERAI is not an observed value, MME results suggest that water recycling may be overactive in the models. The feedback process described below may, therefore, amplify the negative and positive evaporation and precipitation biases over western and eastern Eurasia, respectively. When precipitation decreases (increases), evaporation is reduced (increased) as the surface gets drier (wetter); hence, precipitation further decreases (increases) with reduced (increased) moisture supply from the surface. The reverse also happens, of course. Furthermore, this process contributes to the warm bias over western Eurasia (Fig. 9a) by reducing evaporation, and the associated circulation bias decreases and increases the precipitation in western and eastern Eurasia, respectively. The intermodel relationship between the evaporation–precipitation indices and precipitation reproducibility is significant as shown in Fig. 14, which supports the importance of the processes described here.
Fig. 13.
Fig. 13.

MME bias for the evaporation–precipitation index from the ERAI data. The hatched areas have a significance level >90%.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Fig. 14.
Fig. 14.

Scatterplot of the precipitation skill scores (abscissa) against the evaporation–precipitation indices over northern Eurasia (45°–90°N, Eastern Hemisphere) (ordinate). MME, HSM, and LSM are indicated by M, H, and L.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

As described in section 1, precipitation over northern Eurasia is associated with synoptic-scale processes (Serreze et al. 2001). To examine the synoptic activities, we calculated the meridional heat flux of high-path-filtered meridional wind and high-path-filtered temperature (υhTh) with periods shorter than 10 days using daily data. The high-frequency heat flux at 850 hPa in MME is larger than that in ERAI over the entirety of northern Eurasia (not shown). Consistently, meridional gradients of the tropospheric temperature in the models are stronger than those of ERAI (Fig. 9), providing favorable conditions for the atmospheric disturbances. However, the biases associated with the stronger atmospheric disturbances are identified over the entire northern Eurasia region, indicating that they are not directly related to the dry west and wet east biases of precipitation shown in Fig. 6a. Even when the atmospheric disturbances are overly active over western Eurasia, precipitation is underestimated because of the reduced moisture supply, as discussed above.

4. Time evolution of precipitation events

As previously mentioned, summer precipitation over northern Eurasia has large daily variations (Serreze and Etringer 2003). In this section, we examine the reproducibility of the precipitation events with a time scale of several days associated with synoptic disturbances and local convection in the CMIP5 models.

The precipitation events discussed here are defined as lag regressions with respect to the daily precipitation time series over 5° × 5° boxes in western Eurasia (55°–60°N, 35°–40°E; the left-hand red box in Fig. 15) and eastern Eurasia (55°–60°N, 125°–130°E; the right-hand red box in Fig. 15). We also tested many other definitions, in different regions, to confirm that our results were robust and not sensitive to the definition details (not shown).

The lag fields for precipitation (GPCP1DD) and geopotential height at 500 hPa (Z500; ERAI) with respect to the GPCP1DD precipitation time series are shown in Fig. 15 The precipitation event evolutions in the 5° × 5° western and eastern Eurasia boxes are shown in more detail in Fig. 16, using various atmospheric and land surface variables for GPCP1DD, ERAI, MME, HSM, and LSM. The intermodel spreads estimated as the standard deviations for the CMIP5 models are also shown in Fig. 16. The basic characteristics of the events that are described below are qualitatively similar for western and eastern Eurasia and for all datasets.

Fig. 15.
Fig. 15.

Lag regressions for precipitation (colored, mm day−1; GPCP1DD) and Z500 (contoured, m; ERAI) with respect to the precipitation time series (GPCP1DD) using a 5° × 5° box for (left) western Eurasia (55°–60°N, 35°–40°E) and (right) eastern Eurasia (55°–60°N, 125°–130°E), from (top)–(bottom) day −2 to day +1.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

Fig. 16.
Fig. 16.

Lag regressions for (a),(b) precipitation (mm day−1), (c),(d) evaporation (mm day−1), (e),(f) PE (mm day−1), (g),(h) Z500 (m), and (i),(j) atmospheric instability (°C), against the precipitation time series using a 5° × 5° box, for (left) western Eurasia (55°–60°N, 35°–40°E) and (right) eastern Eurasia (55°–60°N, 125°–130°E). The abscissa shows the lag time (day). Red solid, red dashed, black solid, black dashed, and black dashed-dotted lines show the GPCP1DD, APHRODITE, ERAI, MME, HSM, and LSM, respectively. The shaded areas are the intermodel spreads (±1σ).

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

The observed precipitation events started to develop around day −2, reaching their maxima at day 0, and decayed by around day +2 (Fig. 15 and the red lines in Figs. 16a,b). The anomalous moisture for the events is mostly supplied by moisture convergence (P − E; Figs. 16e,f), while evaporation made no contribution (Figs. 16c,d). However, in section 3, we showed that the seasonal average moisture budget had a small convergence (and a negative convergence in the west), as well as a large contribution from evaporation (Figs. 1b,c). This can be interpreted as evaporation constantly supplying moisture, regardless of the precipitation events (Figs. 16c,d) that make up the seasonal average shown in Fig. 1b. The moisture supplied by evaporation accumulates because of strong moisture convergence, over a short time scale of several days (Figs. 16e,f), causing the precipitation events. The net seasonal average moisture convergence is small (Fig. 1c) because moisture convergence is largely negative on fine days, without precipitation events (not shown).

The moisture convergence of the precipitation events may be associated with low pressure systems and convective activities. The low pressure systems in the lag regression fields shown in Fig. 15 can be seen to the west of the precipitation maxima and slowly move eastward, and the Z500 trough deepens (Figs. 16g,h). At the same time, atmospheric instability, measured as the difference in moist static energy between 850 and 500 hPa, is largely removed by the precipitation events (Figs. 16i,j). These results are consistent with those of previous studies in which summer precipitation over northern Eurasia has been considered to be a combination of synoptic-scale processes and local convection (Serreze and Etringer 2003).

The precipitation events described above are reproduced well in the CMIP5 models, except for their magnitudes (Figs. 16a,b). The precipitation maxima for the MME, HSM, and LSM events are 2.3, 2.5, and 1.8 mm day−1, respectively, over western Eurasia, somewhat weaker than the observed values in GPCP1DD (2.9 mm day−1) and ERAI (2.7 mm day−1). The intermodel variance for the 22 CMIP5 models is 0.5 mm day−1, and the MME biases from the observations are statistically significant. Conversely, the MME, HSM, and LSM maxima over eastern Eurasia are all 3.7 mm day−1 (with an intermodel variance of 0.6 mm day−1), somewhat larger than the values of 3.0 mm day−1 for GPCP1DD and 3.4 mm day−1 for ERAI. These precipitation event biases are consistent with the dry west and wet east biases in the seasonal average precipitation discussed in section 3. In regard to the moisture budget, the precipitation biases for the events correspond well with the P − E biases (Figs. 16c,d), with almost no contribution from evaporation (Figs. 16e,f). The MME, HSM, and LSM seem to reproduce the precipitation processes for low pressure systems and convective activities reasonably well, including their west–east contrast described above for ERAI, although the inter-reanalysis spreads may be very large for quantitative evaluations.

The evaluation of the precipitation events above is performed over 5° × 5° boxes in western Eurasia (55°–60°N, 35°–40°E) and eastern Eurasia (55°–60°N, 125°–130°E). Here, we briefly examine the events in the other regions of the northern Eurasia. Figure 17 shows the maximum precipitation intensity of the events and the duration time with precipitation larger than 10% of the maximum intensity (see Fig. 16a for an example of the maximum intensity and the duration time). In the APHRODITE observations, the maximum intensity shows large values over western Eurasia and along the eastern coast. The duration time is relatively longer over the regions to the west of 80°E and relatively shorter in eastern Eurasia around 50°N, 110°E. Although the detail distributions differ, these basic regional dependencies of events are reasonably captured in MME.

Fig. 17.
Fig. 17.

The maximum precipitation intensity (colored; mm day−1) and the duration time (hatching) of the events with precipitation >10% of the maximum intensity on each grid (see Fig. 16a for an example of the maximum intensity and the duration time) for (a) APHRODITE and (b) MME. Blue and red hatchings show the duration time shorter and longer than 3.7 days, respectively.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

5. Summary and discussion

We have assessed the reproducibility of summer precipitation in northern Eurasia using the CMIP5 climate models compared with several observational and reanalysis datasets, and we discussed the processes that determine the reproducibility.

The CMIP5 climate models under- and overestimate the precipitation in western and eastern Eurasia, respectively, in the summer seasonal average fields (Fig. 6a). Dry west and wet east precipitation biases are found in all CMIP5 models, and the reproducibility measured using the Taylor skill score is largely determined by the severity of the west–east precipitation bias (the correlation coefficient is 0.78; Fig. 7). The reanalyses of JRA-25, JRA-55, ERA-40, ERAI, and MERRA have fairly good skill scores, and the NCEP and NCEP-2 datasets perform poorly, as suggested in previous studies (Serreze et al. 2005; Trenberth et al. 2011).

The processes in which the dry west and wet east biases appeared were examined, and the following two possible causes were suggested: very little cloud cover and very strong local evaporation–precipitation coupling. The CMIP5 MME values underestimated the cloud cover over Eurasia, allowing too much sunshine to reach the surface, resulting in a warm bias there. The associated cyclonic circulation biases in the lower troposphere weaken moisture transport to western Eurasia from the Atlantic and enhance the northward moisture flux from the East Asian monsoon along the eastern coast. Once the dry west and wet east biases appear in the models, they are amplified by very strong evaporation–precipitation coupling. A negative precipitation bias causes less evaporation and a drier surface, and reduced evaporation decreases the moisture supply for precipitation in turn. The reverse also happens. The importance of these processes is supported by the intermodel relationships of the precipitation skill score with respect to SCRF (cloud cover) and evaporation–precipitation coupling indices over northern Eurasia (Figs. 10, 12, and 14). It is noteworthy that similar significant intermodel relationships are found when the precipitation differences in western and eastern Eurasia (the ordinates in Fig. 8) are used instead of the precipitation skill scores. These intermodel relationships are not influenced by observational uncertainties, demonstrating the advantage of using intermodel comparisons to improve our understanding of the processes that suffer from large observational uncertainties, such as winter precipitation (Serreze and Etringer 2003).

The CMIP5 models qualitatively reproduce precipitation events with a time scale of several days associated with synoptic disturbances and local convection. Precipitation events occur with strong moisture convergence, associated with low pressure systems and local convection, whereas evaporation supplies background moisture at a more constant level. However, the magnitudes of the precipitation events are very small over western Eurasia and very large over eastern Eurasia, consistent with the seasonal average field biases. The precipitation event biases correspond with the anomalous moisture convergence biases. The amount of precipitation in the events is possibly influenced by the large-scale moisture supply of the seasonal average.

We described the physical processes that relate the model precipitation biases to the cloud cover and local evaporation–precipitation coupling errors. This improves our understanding of the representation of precipitation in climate models published by Kattsov et al. (2007), in which the summer precipitation biases for northern Eurasian watersheds are presented. The problems related to cloud cover and evaporation–precipitation couplings, shown to be important for northern Eurasian precipitation, are consistent with the model deficiencies that have often been discussed in relation to other aspects of the climate system (Trenberth et al. 2003; Bony and Dufresne 2005; Ruiz-Barradas and Nigam 2005).

It is still difficult to prove the causality of the relationships described here because differences among CMIP models arise from so many different schemes (radiation, convection, cloud, land, turbulence, and so on) and are associated with different parameters that it is impossible to separate the impacts of different processes. For that to be achieved, complementary studies, with well-organized sensitivity experiments of a model (e.g., varying cloud cover and evaporation–precipitation coupling strengths), will be required. Modifying the cloud scheme parameters, which affect the lifetime of cloud droplets, may affect the cloud cover and strength of the evaporation–precipitation coupling. Decreasing the evaporation efficiency in the land surface schemes also appears to be effective in weakening the evaporation–precipitation coupling.

This study has not discussed the diurnal cycles of precipitation. In previous studies, precipitation in climate models is shown to occur too often with too little intensity, which is likely to be caused by too weak convective entrainment rates (e.g., Dai 2006; Hirota et al. 2014). However, our preliminary analyses have suggested that the severity of the biases in diurnal cycles does not correlate with the west–east precipitation biases in the seasonal average field. On the other hand, detail distribution of the seasonal average precipitation biases (Fig. 6a) is likely to be related to orography. For example, maxima of the precipitation overestimate are located near the Chersky mountain range (65°N, 140°E) and the Sayan Mountains (55°N, 95°E). Diurnal cycles and orographic effects of precipitation over northern Eurasia will be examined in the future.

Acknowledgments

This study was supported by the Green Network of Excellence Program of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, and by the Environment Research and Technology Development Fund (2-1503) of the Ministry of the Environment, Japan. We acknowledge the WCRP’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. The U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordination and support for CMIP and led the development of the software infrastructure, in partnership with the Global Organization for Earth System Science Portals. We also acknowledge the “Data Integration and Analysis System” fund for National Key Technology from MEXT. The Grid Analysis and Display System was used to plot the figures.

APPENDIX A

A Moisture Budget in ERAI

A moisture budget of an atmospheric column in reanalysis data can be written as
eq4
where W is the vertically integrated water (vapor + liquid + ice), C is the vertically integrated moisture convergence, E is evaporation, P is precipitation, and A is an analysis increment. Precipitation and evaporation in the JJA climatological average field from ERAI are shown in Figs. 3f and 1b, respectively, whereas the tendency of water and the moisture convergence are shown in Fig. A1. Note that this moisture convergence
eq5
is calculated and analyzed in the ERAI assimilation system, so it does not include calculation errors from the vertical integration in pressure p and from the nonlinear multiplication of time-averaged wind u and specific humidity q. The tendency is negligible in the seasonal average fields, and the moisture convergence is very similar to PE shown in Fig. 1c, including the major features described in section 3. This suggests that the analysis increment, which is not available in ERAI, is small and supports the use of PE instead of vertically integrated moisture convergence. We have also compared the moisture flux directly calculated in the assimilation system with a product of daily wind and specific humidity. They are similar to each other (not shown), indicating that the moisture flux associated with subdaily variabilities is small.
Fig. A1.
Fig. A1.

(a) Tendency of vertically integrated water and (b) convergence of vertically integrated moisture flux from ERAI (mm day−1). The vectors in (b) show the vertically integrated moisture flux (kg kg−1 m s−1). Values are very small everywhere in (a).

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

APPENDIX B

Biases in the HSMs and the LSMs

The groups of five highest scoring models (HSMs) and five lowest scoring models (LSMs) are examined. The HSM and LSM biases for precipitation, surface air temperature, SCRF, and the evaporation–precipitation index are shown in Fig. 2. In both the HSMs and LSMs, precipitation is under- and overestimated over western and eastern Eurasia (Figs. B1a,b), respectively, with the associated warm bias (Figs. B1c,d), positive SCRF bias (Figs. B1e,f), and overly strong evaporation–precipitation coupling (Figs. B1g,h) identified. While HSMs and LSMs are qualitatively similar, these biases are larger in LSM.

Fig. B1.
Fig. B1.

(a),(c),(e),(g) HSM and (b),(d),(f),(h) LSM biases for (a),(b) precipitation (mm day−1), (c),(d) surface air temperature (°C), (e),(f) shortwave cloud radiative forcing (W m−2), and (g),(h) the evaporation–precipitation index. The references are (a),(b) GPCP2, (c),(d) CRU, (e),(f) CERES, and (g),(h) ERAI. The hatched areas have a significance level >90%.

Citation: Journal of Climate 29, 9; 10.1175/JCLI-D-15-0480.1

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