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Observed and Projected Changes to the Precipitation Annual Cycle

KATE MARVEL , and NASA Goddard Institute for Space Studies, New York, New York

MICHELA BIASUTTI Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

CÉLINE BONFILS AND KARL E. TAYLOR Lawrence Livermore National Laboratory, Livermore, California

YOCHANAN KUSHNIR Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

BENJAMIN I. COOK NASA Goddard Institute for Space Studies, New York, New York

(Manuscript received 2 August 2016, in final form 8 February 2017)

ABSTRACT

Anthropogenic change is predicted to cause spatial and temporal shifts in precipitation patterns. These may be apparent in changes to the annual cycle of zonal mean precipitation P. Trends in the amplitude and phase of the P annual cycle in two long-term, global satellite datasets are broadly similar. Model-derived fingerprints of externally forced changes to the amplitude and phase of the P seasonal cycle, combined with these observations, enable a formal detection and attribution analysis. Observed amplitude changes are in- consistent with model estimates of internal variability but not attributable to the model-predicted response to external forcing. This mismatch between observed and predicted amplitude changes is consistent with the sustained La Niña–like conditions that characterize the recent slowdown in the rise of the global mean temperature. However, observed changes to the annual cycle phase do not seem to be driven by this recent hiatus. These changes are consistent with model estimates of forced changes, are inconsistent (in one ob- servational dataset) with estimates of internal variability, and may suggest the emergence of an externally forced signal.

1. Introduction gaps in theoretical understanding hinder progress in detecting precipitation changes in observations and/or Changes to the amount, distribution, timing, and fre- attributing them to anthropogenic forcing (Hegerl et al. quency of precipitation are among the most societally 2015; Sarojini et al. 2016). relevant consequences of climate change. A vast body of Expected precipitation changes are often partitioned literature (e.g., Durack et al. 2012; Hegerl et al. 2015; into a thermodynamic component resulting from in- Min et al. 2008; Polson et al. 2013b; Santer et al. 2007; creased atmospheric water vapor (Allen and Ingram Zhang et al. 2007; Marvel and Bonfils 2013) has begun to 2002; Held and Soden 2006) and a dynamic component identify projected and observed changes to various as- associated with changes to the atmospheric circulation pects of the hydrological cycle, but observational un- (Chou et al. 2009; Seager et al. 2010; Bony et al. 2013). A certainty, model error, large internal variability, and fundamental response to a simple, uniform warming can be described as a combination of 1) the convergence of Corresponding author e-mail: Kate Marvel, kate.marvel@nasa. increased moisture by the original circulation, so that gov wet regions become wetter and dry regions drier, and

DOI: 10.1175/JCLI-D-16-0572.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/28/21 02:15 PM UTC 4984 JOURNAL OF CLIMATE VOLUME 30

2) changes in the zonal mean circulation that shift the existence (Cowtan and Way 2014; Fyfe et al. 2016; locations of these wet and dry regions by expanding the Karl et al. 2015) of the hiatus as defined by global tropical belt and moving the storm tracks poleward. In mean temperature. However, multiple lines of evi- many places precipitation is tied to the annual cycle, dence suggest the recent slowdown in warming with most rain falling in a distinct wet season. Forced resembles a negative phase of the interdecadal Pacific changes in precipitation can therefore also be de- oscillation (IPO; Meehl et al. 2013; Steinman et al. scribed as changes in the amplitude (the range between 2015)orsustainedLaNiña–like conditions (Held wet and dry season precipitation) and phase (the onset 2013). These conditions are attributable to some time of the wet season) of the precipitation annual combination of internal decadal variability, natural cycle and may be attributed to thermodynamic and forcings (Santer et al. 2014; Schmidt et al. 2014), and dynamic effects. Previous work has indicated that the anthropogenic warming, and they substantially affect amplitude of the annual cycle of tropical precipitation many trends measured over the relatively short will increase under increased forcing satellite era. (e.g., Chou and Lan 2012; Dwyer et al. 2014; Tan et al. How has the seasonality of precipitation changed over 2008) and that this change can be explained in terms of the satellite era? How can previous claims of model– the wet-get-wetter response to uniform warming observation agreement on a rich-get-richer amplifica- (Dwyer et al. 2014). Models from both phases 3 and 5 tion driven by surface warming be reconciled with the of the Coupled Model Intercomparison Project hiatus? These questions require a careful comparison of (CMIP3 and CMIP5) also project a delay of the onset model-predicted and observed patterns. Here, we of the rainy season in the tropics, particularly in perform a formal detection and attribution (D&A) monsoon regions (Biasutti and Sobel 2009; Biasutti study of the amplitude and phase of the precipitation 2013; Seth et al. 2011, 2013), but the mechanisms re- annual cycle. We consider zonal mean precipitation P sponsible for this signal are less understood, as both because the averaging helps to reduce observational and local land surface effect and more general changes in model uncertainty and, as we shall see, because the circulation have been implied (Dwyer et al. 2014; Seth zonal mean facilitates physical interpretation of the et al. 2013). results. Because the thermodynamic response is relatively robust across models (Chou et al. 2009), signals associ- 2. Methods ated with this mechanism might be expected to emerge a. Datasets used sooner in the observational record. Chou et al. (2013) have indicated that, globally, the range between the wet Only two datasets provide long-term measurements and dry season increased in the 1979–2010 GPCP dataset. of precipitation over both land and ocean. These are However, the signal they identify is especially large in the the merged satellite–gauge products of the Global higher latitudes, where satellite estimates of precipitation Precipitation Climatology Project (GPCP) and the are more prone to biases. Moreover, there is little overlap Climate Prediction CenterMergedAnalysisofPre- between regions where an intensification of the annual cipitation (CMAP; see below), which provide 2.58 cycle is identified and regions in which the annual cycle gridded monthly mean data from January 1979 to De- is large. cember 2014. While both datasets provide global cov- Previous work (e.g., Durack et al. 2012; Zhang et al. erage, we consider only the region from 508Sto508N. 2007) has identified a human influence on various as- The reduced domain eliminates the high latitudes, pects of the water cycle over the twentieth century. where the datasets are considered unreliable and will However, the most recent precipitation changes have facilitate comparisons (in subsequent work) with the occurred against a backdrop of the slower-than- shorter-length TRMM dataset. There are well-known expected (compared to models) warming: the much- discrepancies between these two satellite datasets, discussed global warming hiatus (Fyfe and Gillett particularly over the oceans (Yin et al. 2004), which 2014; Hawkins et al. 2014). This muted global tem- result in substantial observational uncertainty. Fur- perature rise has been accompanied by cool temper- thermore, issues with satellite drifts and merging of atures in the equatorial Pacific (Kosaka and Xie 2013) inhomogeneous data sources may reduce their suit- and concomitant intensification of the trade winds ability for long-term trend analysis. Despite these (England et al. 2014). There has been vigorous debate shortcomings, these datasets remain the best global over the causes (Huber and Knutti 2014; Kaufmann products available. Previous studies have used only et al. 2011; Santer et al. 2014; Schmidtetal.2014), GPCP to examine large-scale trends in combined ocean relevance (Johansson et al. 2015), and even statistical and land precipitation (e.g., Marvel and Bonfils 2013;

Unauthenticated | Downloaded 09/28/21 02:15 PM UTC 1JULY 2017 M A R V E L E T A L . 4985 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Polson et al. 2013a).Here,wewillshowthatthetwo R(f, y) 5 Re[F (f, y)]2 1 Im[F (f, y)]2 (2) datasets show similar trends in the amplitude and phase 12 12 of the precipitation annual cycle, enhancing our confi- and the phase by dence in the results.   Im[F (f, y)] 365 1) GPCP u(f, y) 5 tan21 12 3 . (3) Re[F (f, y)] 2p GPCP (Huffman et al. 1997), version 2.2, begins in 12 1979 and was last updated in October 2015. It blends We also define 1979–2014 multimodel mean (MMM) retrievals from multiple satellite datasets, sounding ob- amplitude and phase climatologies from the real and servations, and land-based gauges. Post-1988, GPCP imaginary components of the annual Fourier mode of incorporates data from the Remote Sensing Systems the entire 1979–2014 time series. The MMM is calcu- (RSS) Special Sensor Microwave Imager (SSM/I) over lated first by averaging over ensemble members and the ocean (Wentz and Spencer 1998). then over models. These climatologies are shown in 2) CMAP Fig. 1. The black dashed line indicates the multimodel mean 1979–2014 precipitation climatology. Super- NOAA’s CMAP (Xie and Arkin 1997) combines land imposed on this line are circles whose area represents and low island/atoll gauge data with microwave and in- the 1979–2014 zonally averaged, MMM amplitude cli- frared observations from polar-orbiting and geosta- matology at each latitude. Colors indicate the 1979–2014 tionary satellites. We use the enhanced version, which MMM phase. incorporates the NCEP–NCAR reanalysis to fill gaps in spatial coverage (although this is less relevant for the d. Relative importance of the annual cycle domain under consideration). Figure 1 clearly shows that the amplitude and phase of b. Models the P annual cycle vary with latitude. At some latitudes the annual cycle is quite strong; at others it is muted or We use model simulations from CMIP5 (Taylor et al. even absent, and in these regions the phase of the fitted 2012). To estimate unforced internal climate variability, sinusoid may vary wildly from year to year. We want to we rely on preindustrial (piControl) simulations with no ensure that we track changes in the annual cycle only at changes in external climate forcings. For comparison those latitudes where it is meaningful. We therefore with recent observations we use historical simulations define a weighting function W(m, f) for each CMIP5 with estimated changes in anthropogenic and natural model or observational dataset m at each latitude f to forcings over the period 1860–2005, extended to 2100 by take into account the relative importance of the annual splicing with the corresponding RCP8.5 simulation in cycle. We calculate the Fourier mode with period 12 for which twenty-first-century changes in greenhouse gases the entire 36-yr time period 1979–2014 spanned by the and anthropogenic aerosols are prescribed according to observational datasets: the representative concentration pathway 8.5 (ALL18.5; 3 Van Vuuren et al. 2011). 2 2 36/12 m 5 å 2pt/12 m f F12 e P ( , t), (4) c. Amplitude and phase time series 12 t51/2

We regrid all CMIP5 simulation output and observa- where P m(f, t) is the zonal mean precipitation in tions to the GPCP grid and calculate the zonal average the CMIP5 model or observational dataset m at P(f, t) of combined land and ocean precipitation, where f m latitude and time t. From its amplitude R12 and f is latitude and t is the monthly time step. To determine um phase 12, we calculate the best-fit sinusoid the annual cycle at each latitude, we calculate the am- m 5 m p 1 um S12(t) R12 cos(2 t/12 12). We define the weighting plitude and phase of the best-fit sine wave with period function as follows: 12 months, as in Stine et al. (2009). At each latitude f and for each year y, we calculate the Fourier mode f 5 m m f 2 W(m, ) fcorrelation[S12(t), P ( , t)]g . (5) with period 12 months: The weighting function thus measures the total temporal 2 11/2 variance in the observed or simulated time series at f 5 å 2pit/12 f 1 F12( , y) e P( , t y). (1) f 12 t51/2 latitude in model or observational dataset m that can be explained by the annual cycle. CMIP5 models contain The factor of 2 accounts for positive and negative fre- errors in the location and intensity of the major features quencies. The amplitude is given by of the zonal mean precipitation (Levy et al. 2013; Marvel

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FIG. 1. (a) MMM 1979–2014 annual mean P (dashed black line), P annual cycle amplitude (area of circle), and phase (color) climatologies. (b) Proportion of total 1979–2014 time series variance explained by the annual cycle in the GPCP (blue) and CMAP (red) datasets and in ALL18.5 CMIP5 models (orange). and Bonfils 2013; Scheff and Frierson 2012), and de- ITCZ shift, but too far equatorwardtobemuchaf- fining the weighing function on a model-by-model basis fected by storm-track precipitation. downweights the latitudes in each model where the an- nual cycle is negligible. Figure 1b shows this weighting e. D&A toolkit function W(m, f) for each CMIP5 model m and the In this paper, we will use a standard set of ‘‘finger- GPCP and CMAP datasets. In the tropics, the annual printing’’ and signal detection methods presented in, for cycle explains a large fraction of observed and mod- example, Santer et al. (2005, 2011, 2013). eled variance because precipitation largely follows the seasonal movement of the ITCZ, which, in the 1) FINGERPRINTING zonal mean, generally moves toward the summer The fingerprint F(f) of climate change is the spatial hemisphere (Schneider et al. 2014). The ITCZ crosses pattern that characterizes the climate system response to the thermal equator twice in a year, resulting in a external forcing (Allen and Stott 2003; Gillett et al. 2002; weak annual cycle (and strong semiannual cycle) Hegerl et al. 1996; Stott et al. 2000; Tett et al. 2002). andadipintheweightingfunctionthere.Wintertime Following, for example, Hasselmann (1993) and Santer storms bring precipitation to the midlatitudes, re- et al. (2011), we define the fingerprint as the model- sulting in a strong seasonal cycle [and relatively large predicted response to external forcing. We calculate this values of W(m, f)] there. The observations and as follows: models show sharp decreases in the variance ex- plained by the annual cycle on the equatorward flanks d For each ALL18.5 model m, year y, and latitude f,we of the storm tracks: regions poleward of the seasonal calculate the amplitude Rm(f, y) and phase um(f, y).

Unauthenticated | Downloaded 09/28/21 02:15 PM UTC 1JULY 2017 M A R V E L E T A L . 4987 d For each model and latitude, we calculate 1979–2014 confidence interval obtained from CMIP5 ALL18.5 amplitude and phase climatologies. simulations, the signal is considered compatible with the d We subtract these climatologies to get amplitude and combination of external forcings present in those sim- phase anomalies for each latitude and model. ulations. Greenhouse gas forcing is dominant over the d Both amplitude and phase time series are weighted by satellite era, but these simulations also contain estimates the annual cycle weighting function W(m, f). of natural (solar and volcanic) and other anthropogenic d We then average over ensembles and models to obtain (aerosols and ozone depletion) forcings. This means that MMM amplitude and phase anomaly time series. The compatibility with the ALL18.5 distribution does not averaging process damps internal variability, reveal- imply attribution of the signal to any particular forcing. ing the forced response. We note that an observed signal can be compatible with d The amplitude and phase fingerprints are defined as the external forcing but too weak to emerge from the leading empirical orthogonal function of the 1860–2100 background of internal variability, or it can be detect- MMM amplitude and phase anomalies. These finger- able but not compatible with external forcing (if it is prints, which will be discussed further in section 2,are incompatible with both unforced and forced model similar to the linear trend at each latitude (not shown). distributions). The latter case is cause for concern and results from some combination of the following: model 2) SIGNAL failure to realistically capture the forced response, un- certainties in the forcings themselves, model un- Given a set of amplitude or phase anomalies from derestimation of internal variability, or observational observations O (f, y), the projection p(y) onto the fin- uncertainties. gerprint is given by p(y) 5 åfO (f, y)F(f). Physically, this measures the spatial covariance between the f. Results searched-for fingerprint and the observational or model data as a function of year. If the fingerprint is in- MODELED AND OBSERVED AMPLITUDE AND creasingly present in the data, then p(y) should increase PHASE CHANGES with time. As in Santer et al. (2011), we define the signal In this paper, we argue that the annual cycle of pre- S(L) as the L-length trend in p(y), obtained by least cipitation provides a useful lens through which to ex- squares regression. For GPCP and CMAP observations, amine current and predicted hydroclimate changes. As L 5 36 yr. an additional benefit, this framework also reduces ob- 3) NOISE servational uncertainty due to errors in the long-term satellite datasets. Figure 2a shows 1979–2014 observed To assess the significance of a signal, we need an un- and simulated P climatologies. Figures 2b–d show 1979– derstanding of how internal climate variability might 2014 trends in annual mean P (Fig. 2b), P annual cycle project onto the fingerprint. To do this, we calculate amplitude (Fig. 2c), and P annual cycle phase (Fig. 2d). weighted amplitude and phase anomalies for the CMIP5 While CMAP and GPCP broadly agree on the overall P preindustrial control runs. By concatenating the first climatology (the spatial correlation between the datasets 200 yr of each piControl simulation and projecting the is greater than 0.96 for the 508S–508N domain considered results onto the fingerprints, we obtain a long (4000 yr) here), they show very different trends in annual mean time series p (y) containing 4000/L nonoverlapping c P (spatial correlation 20.15). By contrast, the spatial L-length segments. Calculating the best-fit trends in correlation between GPCP and CMAP weighted am- these segments results in a distribution whose standard plitude trends is 0.77 and between weighted phase deviation N(L) provides a measure of noise in the trends is 0.68. This is perhaps because factors (such as trends: the likelihood of observing a given signal purely satellite drift) that lead to spurious long-term trends in a due to natural internal variability. satellite dataset are less likely to affect measures based on seasonal differences if both wet and dry seasons are 4) SIGNAL TO NOISE affected by the same biases. If the dimensionless signal-to-noise ratio (SNR) ex- The orange envelopes in Fig. 2 show the 5%–95% ceeds 1.64, the signal is considered detectable at 90% confidence intervals of these variables obtained from confidence relative to our best current multimodel es- ALL18.5 CMIP5 model ensembles. Figure 2a shows the timates of natural internal variability (Bindoff et al. severity of the double-ITCZ bias in the models but 2013). We use this two-tailed z test throughout to overall good agreement in the northern tropics and in provide a conservative estimate of significance. Simi- the midlatitudes. Figure 2b shows that simulated trends larly, if the signal-to-noise ratio lies within the 5%–95% in annual mean precipitation are more consistent with

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FIG. 2. (a) Climatologies, (b) 1979–2014 trends in annual mean P, (c) 1979–2014 trends in P annual cycle am- plitude, and (d)1979–2014 trends in P annual cycle phase. Orange shading shows the 5%–95% confidence interval determined from ALL18.5 CMIP5 models; blue (CMAP) and red (GPCP) lines show observed values.

GPCP than CMAP, as the latter lies within the model with the largest anomalies in the deep tropics where the envelope only in the dry subtropics. Figures 2c,d indicate base state precipitation is also large. The exceptions to that, while the observed amplitude and phase trends lie this overall increase are on the equatorward flanks of the within the model envelopes at most latitudes, there are storm tracks in both hemispheres. Here, the decrease in notable areas of model–observation disagreement: ob- amplitude likely reflects the predicted poleward shift of served amplitude trends in the tropics are more negative the storm tracks and subsequent reduction in wintertime than in models, and observed phase trends are more precipitation on their equatorward edges (Hu and Fu positive than in the models at certain subtropical lati- 2007; Previdi and Liepert 2007; Scheff and Frierson tudes. To assess the significance of these discrepancies, 2012). Figure 3b shows the fingerprint of externally understand the physical mechanisms that dictate the forced changes to the phase of the precipitation annual model trends, and evaluate the performance of cli- cycle, weighted by W(m, f). This mode explains 72% of mate models over the satellite era, we require a formal the variance in the 1860–2100 MMM phase time series. comparison using the framework of detection and The fingerprint is characterized by little change or phase attribution. delays (later onset of the wet season) at most latitudes, with the exception of advances (earlier wet season on- g. Model-derived fingerprints set) on the equatorward flanks of the storm tracks, Figure 3a shows the fingerprint of externally forced particularly in the Southern Hemisphere. The phase changes to the amplitude of the precipitation annual delays are strongest around 158N (at the longitude of the cycle. We note that prior to calculating the multimodel African Sahel) and around 458N and 458S (the poleward mean, the amplitude time series has been weighted by flank of the storm tracks). These results are consistent W(m, f)(Fig. 1b). This pattern explains 89% of the with the prediction that climate change will result in variance in the 1860–2100 MMM amplitude time series. shorter, more-intense wet seasons in many monsoon Corroborating previous findings (Chou et al. 2013), the regions and hence delay the monsoon onset (Biasutti annual cycle amplitude increases almost everywhere, and Sobel 2009; Biasutti 2013; Seth et al. 2011, 2013).

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FIG. 3. Fingerprints of externally forced changes to the P annual cycle (a) amplitude and (b) phase and La Niña composites for the (c) amplitude and (d) phase. The y axis shows the EOF loading in (a),(b), precipitation anomaly 2 (mm day 1) in (c), and phase anomaly (days) in (d).

Earlier wet season onsets poleward of 308Nand308S(red years and over all models yields the amplitude and phase circles) are likely to be associated with forced circulation composite patterns shown in Figs. 3c and 3d, re- shifts that push the storm tracks toward the poles (Scheff spectively. The amplitude signal is mostly restricted to and Frierson 2012; Marvel and Bonfils 2013). the equatorial region and the northern tropics, where it indicates a reduction of the annual cycle amplitude, h. La Niña composites consistent with a reduction of austral summer and in- Recent studies (e.g., Fyfe and Gillett 2014) have crease in boreal winter zonal mean tropical precipitation suggested that the global mean temperature trend from during the dry season (Lu et al. 2008). Amplitude circa 1998–2014 may be smaller than predicted by weakly increases in the southern subtropics, and changes CMIP5 models. This so-called global warming hiatus has are negligible elsewhere. Phase changes associated with been characterized by protracted La Niña–like cooling La Niña, by contrast, are not confined to the tropics. of the eastern equatorial Pacific (Kosaka and Xie 2013), They are positive (earlier) in the latitudes of the stronger-than-normal trade winds (England et al. 2014), southern ITCZ and of the northern monsoons and and distinctive precipitation and SLP patterns negative (delayed) in a narrow latitudinal band on the (Trenberth et al. 2014). This pattern has been deemed poleward flank of the storm tracks and may be associ- consistent with the IPO (Deser et al. 2004; Kosaka and ated with the poleward shifts of the Hadley cell and jets Xie 2013; Meehl et al. 2013). Naturally occurring La during a La Niña event (Lu et al. 2008). Niña conditions affect the amplitude and phase of zonal i. Signal detection results mean precipitation. To characterize these effects, we compute La Niña composite patterns as follows: For Figure 4a shows the projections of GPCP and CMAP every 200-yr piControl simulation, we calculate boreal zonal mean data onto the amplitude fingerprint. The winter (DJF) mean sea surface temperatures averaged interannual variability is similar in both datasets, and the over the Niño-3.4 region. We identify years in which this overall trend is negative. Evidently the similarity be- quantity is more than one standard deviation below the tween the searched-for fingerprint and the observations mean and designate these as La Niña years. We then has decreased over the satellite era. By contrast, Fig. 4b calculate amplitude and phase anomalies relative to the shows the observed projections onto the phase finger- piControl temporal mean amplitude and phase at each print. Here, both GPCP and CMAP increasingly re- latitude. Averaging these anomalies over all La Niña semble the model-derived fingerprint over time. To

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FIG. 4. Projections of the observations onto the (a) amplitude and (b) phase fingerprints and best-fit lines obtained through linear OLS regression. assess the significance of these trends, we compare them noise distributions. The ALL18.5 distribution is shifted to the distribution of 36-yr unforced trends that result to the right of the preindustrial control distribution, from the projection of the preindustrial control data and a signal of external forcing is detectable at 90% onto the fingerprint. We also calculate 1979–2014 trends confidence in approximately 30% of the ALL18.5 in the individual ALL18.5 ensemble members. 1979–2014 simulations. This, however, is not reflected in Figure 5a shows the unforced and ALL18.5 amplitude the observations. The CMAP signal is detectable at 90% signal distributions along with the GPCP and CMAP confidence (SNR 521.8 6 1.1): it lies in the far-left tail amplitude signals and the regression slope errors. All of the noise distribution (Fig. 5a, green bars; see also have been normalized by the standard deviation of the Table 1). The GPCP signal is negative but not detectable

FIG. 5. Signal distributions for the P annual cycle (a) amplitude and (b) phase. All signals have been normalized by the standard deviation of the preindustrial control distribution (green). The orange histograms show the CMIP5 ALL18.5 forced distributions. The observed signals are shown as vertical lines, and the shading shows the 90% confidence interval on the signals determined from the uncertainty in the regression slope.

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TABLE 1. SNRs for the P annual cycle amplitude and phase for temporal variance in the projection onto the amplitude various subdomains. Values outside the 5%–95% confidence in- fingerprint is explained by the projection onto the La terval of the CMIP5 preindustrial control distribution are consid- Niña composite. This suggests that the observations look ered detectable and are marked with an asterisk. Values outside the 5%–95% confidence interval of the CMIP5 ALL18.5 distri- less like the model-predicted anthropogenic forcing fin- bution are considered incompatible with the forced response and gerprintbecausetheylookmorelikeaLaNiña–type are marked with a dagger. mode of variability. This aligns with previous studies that have characterized the recent global mean temperature Amplitude Phase Dataset Domain SNR SNR slowdown or hiatus as compatible with sustained La Niña–like conditions. We note that this analysis does not CMAP Full (508S–508N) 21.8*,y 1.3 GPCP Full (508S–508N) 20.2y 2.2* address the source of this model–observation mismatch CMAP Tropics (108S–108N) 22.1*,y 0.6 but merely confirms that the observed trends in P am- GPCP Tropics (108S–108N) 21.0y 0.6 plitude increasingly resemble La Niña. CMAP Extratropics (108–508N, 108–508S) 0.1 1.2 Figure 6d indicates a very weak relationship between 8 8 8 8 GPCP Extratropics (10 –50 N, 10 –50 S) 2.2* 2.2* the observed projections onto the phase fingerprint and projections onto the phase La Niña composite. Less than (SNR 520.2 6 1.0). However, both GPCP and CMAP 5% of the variance in phase projection onto the finger- amplitude signals are incompatible with the 5%–95% print is explained by the projection onto La Niña. In this range of the ALL18.5 distributions, indicating that the case, similarity to the fingerprint does not appear to result recent change in observed precipitation annual cycle from similarity to a La Niña–like mode of variability. amplitude is fundamentally different from the expected This apparent discrepancy can be understood by com- responses predicted by the CMIP5 models over the ob- paring the spatial structure of the amplitude and phase servational period. Figure 5b shows the noise, forced fingerprints (Figs. 3a,b) to the spatial structure of the ob- model, and observed phase signals. Again, the ALL18.5 served changes (Figs. 2c,d). The observed amplitude model distribution is shifted to the right, and the signal is changes are large in the tropics, where both the fingerprint detectable at 90% confidence in 21% of these simula- and La Niña pattern show large changes (of opposite sign). tions. Both GPCP (SNR 5 2.2 6 0.8) and CMAP The observed phase changes, by contrast, are largest out- (SNR 5 1.3 6 0.9) show positive signals, but only GPCP side the tropics, as is the fingerprint. If we exclude latitudes is detectable at 90% confidence. The GPCP signal is also between 108Sand108N from the analysis (Table 1), the consistent with forced CMIP5 models: it lies within the amplitude signal-to-noise ratios in both GPCP and CMAP 5%–95% interval calculated from CMIP5 ALL18.5 are positive (Table 1), and a much smaller percentage distributions (orange bars). (55% for GPCP, 27% for CMAP) of the signal variance is explained by similarity to La Niña. This suggests a hint of 1) A PROTRACTED LA NIÑA? forced changes to the amplitude in the extratropics, and Our detection and attribution analysis has revealed Fig. 2c indeed shows that the observed amplitude increases that observations over the satellite era increasingly in the subtropics and decreases on the equatorward flanks resemble the expected phase fingerprint but are increasingly of the storm tracks, as in the fingerprint. The phase signal- dissimilar to the expected amplitude fingerprint (in fact, to-noise ratios in the extratropics are also positive (Table they increasingly resemble the inverse of the expected 1), and only 10% (GPCP) and 16% (CMAP) of the signal pattern). To what extent may these behaviors be variance is explained by similarity to La Niña. If, by con- explained by increasing similarity to a La Niña–like trast, we consider only the deep tropics (latitudes between mode of variability? 108Sand108N, Table 1), the amplitude SNR for GPCP and To gauge the resemblance between the observations CMAP are strongly negative. The tropical amplitude and La Niña, we project the observations onto the La Niña trends strongly resemble protracted La Niña conditions, composites derived from the piControl simulations with 98% and 85% of the signal variance explained by (Figs. 3c,d). Figure 6a shows the projection onto the La projection onto the La Niña composites. The tropics-only Niña composite amplitude pattern; Fig. 6b shows the phase signals are positive but undetectable above internal projection onto the composite phase pattern. Both data- variability and largely explained by projection onto La sets indicate upward trends in both variables, indicating Niña (68% for CMAP, 73% for GPCP). increasing similarity to the La Niña–like patterns. 2) PLACING ANNUAL CYCLE D&A RESULTS IN The relationships between the observed projections CONTEXT onto the fingerprint (Figs. 4a,b) and onto the La Niña composites (Figs. 6a,b) are shown by the scatterplots in Observed changes to the amplitude of the annual Figs. 6c,d. In both CMAP and GPCP, over 90% of the cycle are large in the tropics, do not resemble the

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FIG. 6. Projections of the observations onto the La Niña composite (a) amplitude and (b) phase patterns. Also shown are the relationships between observed projections onto the (c) amplitude fingerprint and La Niña com- posites and (d) phase fingerprint and La Niña composites.

fingerprint of external forcing, and are not compatible results will necessarily depend on the analysis method with model-simulated internal variability at 90% confi- and that a comprehensive understanding of observed and dence. However, they do resemble the La Niña com- simulated precipitation changes must synthesize multiple posite pattern. This suggests that differences between analysis frameworks. For example, Marvel and Bonfils modeled and observed trends during the warming (2013) examined changes in zonal mean precipitation, slowdown are not confined to temperature changes. attributing the observed combination of thermodynamic Instead, a signature of the warming hiatus is also ex- and dynamic precipitation changes to external forcing. pressed in zonal mean precipitation and is congruent Likewise, Polson et al. (2013a) identified an externally with other studies identifying differences between forced signal in the contrast between wet and dry regions modeled and observed trends in trade wind anomalies but allowed the location of such regions to change from (England et al. 2014) and Pacific SSTs (Kosaka and Xie season to season and year to year. Using this method, 2013). In other words, while the models and observa- observed drying trends, for example, may result from tions do not agree on changes to the precipitation am- changes in the location of the dry regions, reductions in plitude, this mismatch is consistent with a wide and water vapor availability, or some combination of the two. increasing body of literature on the warming hiatus. Our results are consistent with the conclusions of Lintner Observed changes to the phase of the annual cycle, by et al. (2012), who attributed the mismatch between contrast, are not dominated by the tropics and may sug- CMIP3 projections and recent changes in the contrast gest the emergence of a forced signal. Thus, we obtain between wet and dry occurrences to ENSO-related categorically different results depending on whether we variability. consider changes to the amplitude or phase of the annual cycle. This difference highlights an important consider- 3. Conclusions ation for detection and attribution studies. Precipitation is affected by multiple interlinked physical processes on Changes to the amplitude of the precipitation annual multiple spatial and temporal scales, and the observations cycle have implications for hydrological extremes like will always reflect some combination of externally forced and floods (Chou and Lan 2012); changes to response and internal climate variability. This means that the phase can affect growing seasons and crop yields

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(Sultan et al. 2014). We show that, in addition to their making available their model output. For CMIP the U.S. clear societal importance, trends in these quantities are Department of Energy’s Program for similar in two long-term satellite datasets. A focus on Diagnosis and Intercomparison provides coordinating seasonality changes rather than trends in the annual support and led development of software infrastructure mean therefore reduces observational uncertainty and in partnership with the Global Organization for Earth enhances confidence in the results. System Science Portals. GPCP and CMAP precipitation The zonal mean fingerprints used in this study identify data are provided by the NOAA/OAR/ESRL PSD, changes that are both thermodynamic and dynamic in Boulder, Colorado, from their website (http://www.esrl. origin. Increases in the amplitude at most latitudes likely noaa.gov/psd/). BC was supported by the NASA Mod- reflect the rich-get-richer mechanism that is prominent eling, Analysis, and Prediction program under Grant over the oceans. The decreases of the amplitude in the NNX14AB99A. subtropics may result from the (dynamical) poleward expansion of the Hadley circulation. Changes in the REFERENCES annual cycle phase are similarly affected by the avail- ability of water vapor [possibly through a temporal Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 224–232, variant of the upped-ante mechanism of Neelin et al. doi:10.1038/nature01092. (2003)] and changes in atmospheric circulation that af- ——, and P. Stott, 2003: Estimating signal amplitudes in optimal fect the position and movement of the storm tracks fingerprinting, part I: Theory. Climate Dyn., 21, 477–491, and ITCZ. doi:10.1007/s00382-003-0313-9. 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