Climatologically, the California winter precipitation is mainly a result of the inland moisture transfer from the eastern North Pacific, and heavy precipitation occurs when the moisture flow encounters the Sierra Nevada mountain range. Much of the moisture transport is contributed by the so‐called atmospheric rivers [ Zhu and Newell , 1998 ; Ralph et al. , 2006 ; Ralph and Dettinger , 2011 ; Dettinger , 2013 ], and they produce 30–50% of the total precipitation in the West Coast. Based on the water balance, the moisture transport to California should be lower (higher) during drought (flood). The amount of transported moisture is affected by atmospheric circulation and available moisture in the atmospheric. Previous studies have identified the important effect of atmospheric circulation on precipitation in California, but the respective roles of the water cycle components are still not clear. Specifically, we are interested in the roles of atmospheric circulation, evaporation in the moisture sources, and their interactions in California precipitation variability. We first identified the major moisture sources for California precipitation, which is the eastern North Pacific, and then investigate the role of ocean evaporation over these moisture sources in California droughts and floods. We performed data analysis, moisture tracking, and model simulations to investigate these issues.

During the winters of 2011–2014, California has experienced one of the most severe multiyear droughts in its history [ Griffin and Anchukaitis , 2014 ; Swain et al. , 2014 ; Robeson , 2015 ; Seager et al. , 2015 ]. The drought has just been mitigated in winter 2015/2016 by a strong El Niño, but most of California is still in a severe drought. The precipitation deficit during the drought has mainly been caused by a persistent high‐pressure system off the U.S. West Coast that suppressed the moisture convergence and precipitation in California. Studies have found that the high‐pressure system is linked to Pacific sea surface temperature anomalies [ Wang et al. , 2014 ; Hartmann , 2015 ; Seager et al. , 2015 ]. Although this is a very rare event, the probability of this kind of high‐pressure system is likely increasing with global warming [ Swain et al. , 2014 ; Wang et al. , 2014 ; Diffenbaugh et al. , 2015 ; Wang et al. , 2015 ; Yoon et al. , 2015 ]. The drought was exacerbated by the high evaporative demand over land associated with high temperature [ Shukla et al. , 2015 ], part of which has been attributed to global warming [ Williams et al. , 2015 ].

The quasi‐isentropic back trajectory method [ Dirmeyer and Brubaker, 1999 , 2007 ] is used to track the water vapor for each precipitation event during 1985–2015 backward in time. The method has been used to study various water cycle problems [ Dirmeyer and Kinter , 2010 ; Wei et al. , 2012 , 2013 ]. It is a post hoc Lagrangian method that traces the advection of moisture back in time from precipitation events. It uses upstream evaporation and precipitable water to determine the probabilistic distribution of surface evaporation supplying the precipitated water vapor. Traces start from the grid box that has precipitation and the time step when precipitation occurs, backward in space and time until all of its original precipitation is attributed to evapotranspiration (ET). The data sets used to drive the scheme include atmospheric temperature, humidity, winds, surface pressure (all from MERRA), surface ET (ET over land is from MERRA‐Land [ Reichle et al. , 2011 ]; evaporation over ocean is from MERRA but corrected by OAFlux evaporation at daily timescale; and evaporation over ice is from MERRA), and precipitation (from MERRA and corrected by CPC Unified product at daily timescale), all at 6‐hourly timescale.

The Objectively Analyzed Air‐sea Fluxes (OAFlux) products [ Yu and Weller , 2007 ] are constructed using the best possible estimates of flux‐related surface meteorology from an optimal blending of satellite retrievals and atmospheric reanalyses and the state‐of‐the‐art bulk flux parameterizations. OAFlux project used an advanced objective analysis to combine the advantages of the existing data sources and produced synthesized data sets, especially ocean surface fluxes, with improved accuracy. Monthly products (1979–2015) of ocean evaporation, surface (10 m) wind speed, and sea surface temperature (SST) at 1° resolution are used for analysis.

3 Results

3.1 Water Cycle Associated With California Precipitation The precipitation in California has a distinct seasonal cycle with the winter months (November to April) accounting for about 84% of annual total, and the recent precipitation deficit mainly occurred during this period (Figure S1 in the supporting information). Therefore, we focus on the winter months in the following analysis. We first look at the general relationship between California precipitation and atmospheric circulation. Figure 1 shows the regression of the winter GPH onto the California precipitation. Associated with high California precipitation is a consistent low‐pressure center from upper to lower troposphere off the northwest coast of the U.S. Deep pressure centers like this one are mostly associated with wave patterns forced by sea surface temperature (SST) anomalies [Lau, 1997; Hartmann, 2015]. Figure 1 Open in figure viewer PowerPoint The regression of 1979/1980–2014/2015 interannual winter (November–April) GPH onto the California precipitation. (Figures 1 a, 1 c, and 1 e) MERRA and (Figures 1 b, 1 d, and 1 f) ERA‐Interim; (a, b) 200 hPa; (c, d) 500 hPa; and (e, f) 850 hPa. The green box (30°–55°N, 160°–120°W) encloses the core region of GPH change. Missing values are shown as gray. Precipitation anomalies are associated with the water cycle anomalies. Using a back trajectory method that tracks the moisture supplying California winter precipitation (see section 2), we found that most of the moisture comes from the ocean evaporation near the West Coast (Figure 2a), and the land evapotranspiration (ET) only contributes a small portion (~5%). The ocean evaporation near the West Coast shows a significant positive correlation with the California precipitation, and the surface wind pattern associated with high precipitation shows a cyclonic flow, transporting moisture from the ocean to California (Figure 2b). Also, the ocean evaporation near the West Coast has stronger correlation with the precipitation in California than that in any other regions of the Contiguous U.S. (Figure 2c). The significant correlations with precipitation in the southeast U.S. are a result of teleconnection of large‐scale wave patterns (Figure 1). Figure 2 Open in figure viewer PowerPoint Relationship between California precipitation and its moisture supply from evaporation during winter (November–April) 1985/1986–2014/2015. (a) Climatologically mean percentage moisture contribution for California winter precipitation from the source regions. Stippling highlights the major moisture sources that contribute 95% of moisture for California winter precipitation. (b) Regression of winter ocean surface evaporation (shading; from OAFlux) and 50 m wind (arrows; from MERRA; 95% confident) onto the California precipitation. (c) Regression of the Contiguous U.S. precipitation (CPC unified) at each grid cell onto the ocean evaporation in the green box. Stippling in Figures 2 b and 2 c highlights the regions that are 95% confident. (d) Time series of 1986–2015 water year annual (October of previous year to September of current year) and winter California precipitation and the winter contribution from ocean evaporation within the green box. We selected a region (26°–47°N, 162°–114°W; green box in Figures 2a and 2b) where evaporation supplies the most moisture for and also shows the highest correlation with, California precipitation. The evaporation in this region, mostly over the ocean, contributed about 85% of the total moisture for California winter precipitation (Figures 2a and 2d), and there is a high correlation between California precipitation and total moisture from this region (Figure 2d). However, this hydrological connection does not guarantee a causal relationship; i.e., the anomalies in moisture contribution and precipitation may be both controlled by the atmospheric circulation [Wei et al., 2012]. A circulation pattern like that in Figure 2b can transport a large amount of moisture from ocean evaporation to California and produce precipitation. Prein et al. [2016] found that the frequency of this weather pattern was decreasing in the past decades, which is the main reason for the drying of the U.S. Southwest. Note that although the atmospheric rivers for the U.S. West Coast usually have a tropical origin, they only provide 30–50% of total precipitation [Dettinger, 2013] and have various shapes and sizes, so the mean moisture source for California (Figure 2a) does not extend deep into the tropics as the individual extreme cases do.

3.2 Relationships Among Water Cycle Components To investigate the relationship between California precipitation and its controlling factors, we plotted the time series of California precipitation (P) and ocean evaporation (E), surface wind speed (WS), and 500 hPa GPH near the West Coast (Figure 3a), all from observation‐based data sets. As summarized in Figure 3b, the mutual correlations between the time series of the four variables are very high; their correlations are all significant at 99% conference level. P shows a higher correlation with GPH than with E and may be affected by both. WS is a term in the calculation of E and is an important factor affecting variations of E [Yu, 2007]. As expected, WS shows a highly significant correlation with E. In the midlatitudes, SST over most regions does not have a strong correlation with ocean evaporation, but the relationship between WS and E is strong almost everywhere over the ocean (Figure S2) [Wu et al. [2006]). On the other hand, the correlations between GPH and WS are also very significant (≈−0.87). This is because high surface pressure (or high GPH) is usually associated with a stable atmosphere, small pressure gradients, and calm winds; and the opposite is true for low surface pressure. Through WS change, GPH change affects E, so it is reasonable that the correlation between GPH and E is a little weaker than that of WS and E. Thus, GPH can possibly affect P both directly by controlling the convergence of moisture and indirectly by changing WS and E and thus the available moisture in the atmosphere. The P deficits in past four winters (2011–2014) are associated with increased GPH and reduced WS and E (Figures 3a and S3). To disentangle the respective effects of GPH and E on P, we calculated the partial correlation between P and E while excluding the effect of GPH (r P,E. GPH ) and the partial correlation between P and GPH while excluding the effect of E (r P,GPH. E ). Figure 3b shows that r P,E. GPH is much smaller than r P,E , the original correlation between P and E, indicating that GPH plays a dominant role in the correlation between P and E. While compared with the original correlation between P and GPH (r P,GPH ), r P,GPH. E is a little weaker but still very significant. This demonstrates the dominant role of circulation effect on P, and E may only have a secondary effect. Figure 3 Open in figure viewer PowerPoint P), ocean evaporation (E), ocean surface wind speed (WS), and 500 hPa GPH. (a) Their anomalies time series during winter 1979/1980–2014/2015. All are from different observation‐based data sets. Ocean evaporation and surface wind speed are the averages over the ocean in the box in Figure r X,Y is the correlation between X and Y (blue bars), and r X,Y. Z is the partial correlation between X and Y while removing the effect of Z (red bars). The bars show the average correlations, while the whiskers show the maximum and minimum correlations. The correlations are calculated for all possible combinations. For example, there are two precipitation data sets, three evaporation data sets, and two GPH data sets, so there are 12 possible combinations for r P,E. GPH . Note that the correlations with GPH have reversed signs. The 90% and 99% confidence levels are for single correlations (not the means); the confidence thresholds for mean correlations would be lower than for single correlations. Relationships between California precipitation (), ocean evaporation (), ocean surface wind speed (WS), and 500 hPa GPH. (a) Their anomalies time series during winter 1979/1980–2014/2015. All are from different observation‐based data sets. Ocean evaporation and surface wind speed are the averages over the ocean in the box in Figure 2 , and GPH is the average over its core anomaly area (box in Figure 1 ). Note that the scale for GPH is inverted. The gray bar highlights the recent four winters. (b) Temporal correlation coefficients between the time series.is the correlation betweenand(blue bars), andis the partial correlation betweenandwhile removing the effect of(red bars). The bars show the average correlations, while the whiskers show the maximum and minimum correlations. The correlations are calculated for all possible combinations. For example, there are two precipitation data sets, three evaporation data sets, and two GPH data sets, so there are 12 possible combinations for. Note that the correlations with GPH have reversed signs. The 90% and 99% confidence levels are for single correlations (not the means); the confidence thresholds for mean correlations would be lower than for single correlations. We found that although wet events are usually associated with increased evaporation over the eastern North Pacific, past droughts are not always associated with reduced evaporation (Figures S3 and S4a). The strong correlation between P and E is mainly contributed by the wet years; after removing the wettest years their correlation becomes very low (Figure S4). This could be because extreme wet events in California are usually supplied by additional moisture from enhanced evaporation, in addition to favorable circulation patterns, while California droughts are mainly caused by circulation anomalies and are less associated with reduced evaporation in moisture sources. The importance of intense remote evaporation for extreme precipitation has also been found in some other regions [Dirmeyer and Kinter, 2010; Winschall et al., 2014]. This issue is further investigated by a moisture flux analysis in the next section.

3.3 Vertical Profiles of Moisture Flux Components qu, a product of specific humidity q and zonal wind speedu. If q and u are each expressed as a sum of its mean value and anomaly, then (1) The zonal moisture transfer at a certain time and location is, a product of specific humidityand zonal wind speed. Ifandare each expressed as a sum of its mean value and anomaly, then (2) The difference between zonal moisture transfer and its mean value is The three terms on the right size of equation 2 are changes in moisture transfer contributed by zonal wind change ( ), humidity change (ūΔq), and a residual term that represents the covariation of humidity and zonal wind (ΔqΔu). We choose the four wettest winters of California after 1979 (1981/1982, 1982/1983, 1994/1995, and 1997/1998) and recent four dry winters (2011/2012–2014/2015). Although only one of the four recent dry winters belongs to the four driest winters since 1979, they are all dry and have negative ocean evaporation anomalies in the eastern North Pacific (Figures 3 and S3). It is of interest to examine their water cycle. Figure 4 shows the mean changes of the specific humidity, zonal wind, and the three terms on the right‐hand side of equation 2 in the four wet winters (1981/1982, 1982/1983, 1994/1995, and 1997/1998) and recent four dry winters (2011/2012–2014/2015). It can be seen that the specific humidity in the wet winters and dry winters are similar (Figures 4a and 4b), although the wet winters have stronger ocean evaporation. The dry winters even show a little higher humidity over the ocean but a little lower humidity over California, probably because of the influence of wind patterns on moisture transport. The zonal wind from Pacific to California is much stronger in wet years (Figure 4c and 4d), which is the main reason for the differences in moisture transfer toward California (Figures 4e and 4f). The difference in moisture content in the air plays little role (Figure 4g and 4h). Figure 4 Open in figure viewer PowerPoint q), zonal wind (u), and moisture transfer components (see equation Pressure‐longitude plots (30°–40°N average) of average specific humidity (), zonal wind (), and moisture transfer components (see equation 2 ) for (a, c, e, g, and i) four wettest winters (1981/1982, 1982/1983, 1994/1995, and 1997/1998) and (b, d, f, h, and j) recent four dry winters (2011/2012–2014/2015). The region is at around the west coast of California, and California is at around 120°W. Data are from MERRA. If evaporation changes in wet and dry year lead to similar amplitudes of q change Δq (different signs), the moisture transfer anomaly uΔq is larger in wet years because of the stronger zonal wind u. This should be the main reason for the greater influence of ocean evaporation changes on California precipitation in wet years than in dry years.