Consistency in Global Climate Change Model Predictions of Regional Precipitation Trends

Transcription

1 Paper No. 9 Page 1 Copyright Ó 2009, Paper ; 57,648 wors, 10 Figures, 0 Animations, 1 Tables. Consistency in Global Climate Change Moel Preictions of Regional Precipitation Trens Bruce T. Anerson* Department of Geography an Environment, Boston University, Boston, Massachusetts Catherine Reifen an Ralf Toumi Space an Atmospheric Physics Group, Department of Physics, Imperial College of Science, Technology, an Meicine, Lonon, Unite Kingom Receive 11 July 2008; accepte 6 July 2009 ABSTRACT: Projections of human-inuce climate change impacts arising from the emission of atmospheric chemical constituents such as carbon ioxie typically utilize multiple integrations (or ensembles) of numerous numerical climate change moels to arrive at multimoel ensembles from which mean an meian values an probabilities can be inferre about the response of various components of the observe climate system. Some responses are consiere reliable in as much as the simulate responses show consistency within ensembles an across moels. Other responses particularly at regional levels an for certain parameters such as precipitation show little intermoel consistency even in the sign of the projecte climate changes. The authors results show that in these regions the consistency in the sign of projecte precipitation variations is greater for intramoel runs (e.g., runs from the same moel) than intermoel runs (e.g., runs from ifferent moels), inicating that knowlege of the internal ynamics of the climate system can provie aitional skill in making projections of climate change. Given the consistency provie by the governing ynamics of the moel, the authors test whether persistence from an * Corresponing author aress: Bruce T. Anerson, Department of Geography an Environment, Boston University, 675 Commonwealth Ave., Boston, MA aress: brucea@bu.eu DOI: /2009EI273.1

2 Paper No. 9 Page 2 iniviual moel trajectory serves as a goo preictor for its own behavior by the en of the twenty-first century. Results inicate that, in certain regions where intermoel consistency is low, the short-term trens of iniviual moel trajectories o provie aitional skill in making projections of long-term climate change. The climate forcing for which this forecast skill becomes relatively large (e.g., correct in 75% of the iniviual moel runs) is equivalent to the anthropogenic climate forcing impose over the past century, suggesting that observe changes in precipitation in these regions can provie guiance about the irection of future precipitation changes over the course of the next century. KEYWORDS: Regional climate change; Climate moels; Anthropogenic forcing 1. Introuction Unerstaning the future response of the global climate system to human emissions of raiatively active gases such as CO 2 an methane [terme greenhouse gases (GHGs)] has become a timely an compelling concern. This interest is particularly acute at the regional level, which is where impacts upon natural an socioeconomic systems will be realize (Parry et al. 2007). At these scales, it is well known that climate forecasts base upon the use of multiple simulations or ensembles of moel preictions, which are then average to prouce ensemble means, provie better forecast skill than any one iniviual forecast (e.g., Tebali an Knutti 2007); the same hols true for multimoel ensemble means generate using multiple simulations from multiple moels. However, in certain regions, the (in)consistency between iniviual moel forecasts for the en of the twenty-first century limits the utility of multimoel ensemble-mean forecasts because the uncertainty in the mean value of the forecast (as etermine from the sprea of the iniviual moel forecasts) oes not iscount the possibility of no change or even a change of opposite sign (Giorgi an Francisco 2000; Räisänen 2001; Covey et al. 2003; DelSole 2004; Neelin et al. 2006; Räisänen 2007). Effectively, this problem arises because the iniviual moel realizations of climate change for these regions are consiere equally plausible outcomes, an ifferences between these outcomes are large. This problem is particularly evient for estimates of how the regional hyrologic cycle may vary with global-scale climate change over the course of the twenty-first century (Allen an Ingram 2002; Murphy et al. 2004; Hel an Soen 2006; Sun et al. 2007; Solomon et al. 2007). At the same time, the actual climate system is one realization of its own internal moel system; that is, there is an unerlying (albeit unobtainable) moel system that is appropriate for the actual climate evolution. For this reason, previous researchers have use statistical methos to ientify which moels best represent the actual climate system; through various weightings base upon the agreement between historical observations an simulations (Krishnamurti et al. 2000; Giorgi an Mearns 2002; Robertson et al. 2004; Shukla et al. 2006), the aim has been to improve the consistency in forecasts by statistically ientifying the correct moel system. Unfortunately, given finite historical an future observe measures of the actual climate system, it may be extremely ifficult to ientify which numerical moeling system best captures the internal behavior of the actual climate system (Ju an Smith 2004). However, we can still quantify the internal consistency within a given

3 Paper No. 9 Page 3 moel system, an across moel systems, to etermine how strongly the evolution of iniviual realizations from a given moel is constraine by the internal ynamics of the moel itself, an by extension how strongly the observe climate system may also be constraine by its own internal ynamics (Räisänen 2001). If we o fin that the iniviual realizations of a given moel system are constraine by the internal ynamics of the unerlying moel (e.g., there is internal consistency between the forecasts from a given moel system), we can then etermine whether this constraine behavior is self-containe within the evolution of the iniviual realizations themselves. In particular, we can test whether moel persistence from an iniviual moel trajectory which by efinition is governe by the same moel ynamics throughout its evolution serves as a goo preictor for its own behavior by the en of the twenty-first century. While we may not know the true moel system, much less the single realization, that the actual climate system is following, we can still test whether information from a single realization of the climate system (simulate or observe) can be use to preict its own behavior base upon how each of the iniviual realizations from the various moel systems performs in preicting its own behavior. In this sense, we are intereste in testing if persistence is a goo preictor for the long-term behavior of the moel systems, uner the assumption that the evolution of the observe climate system which is effectively a single realization governe by its own internal ynamics has similar persistence as that foun in the numerical moeling systems. Moel long-memory persistence stuies have previously been performe for regional historical temperature (e.g., Syroka an Toumi 2001) an precipitation (e.g., Tomsett an Toumi 2001). Here we expan upon these to examine projections of regional precipitation variations using moel-generate climate simulations force by anthropogenic emissions of raiatively active chemical constituents. Because the sprea of the iniviual moel forecasts of regional precipitation ten to be large, we follow the lea of the Solomon et al. (Solomon et al. 2007) consistency analyses an focus on the sign of these projecte precipitation changes, not necessarily the magnitue. Section 2 escribes the moel systems an atasets use in this stuy while section 3 iscusses the skill metric use throughout this paper. Section 4 examines how the skill of regional precipitation projections changes as a function of location, time, an preictor. Section 5 summarizes the results of this stuy. 2. Data For this stuy, we use couple atmosphere ocean lan surface moel output prouce from seven ifferent numerical couple-climate moel systems, force by projecte changes in greenhouse gas concentrations an anthropogenic aerosols over the next 100 years ( ) that stabilize at an (equivalent) CO 2 concentration of 720 ppm (parts per million) by the year These seven moels are chosen because they each have three or more iniviual simulations or ensemble members force by the projecte changes in greenhouse gas concentrations an anthropogenic aerosols. Generally, for a given moel system, a long-run (multicentury) control simulation of the couple-climate moel is performe in which the raiatively active chemical constituents (incluing CO 2 an other greenhouse gas concentrations, sulfate aerosols, an volcanic particulates) an solar activity are fixe at their preinustrial levels (generally esignate as 1860). Then multiple

4 Paper No. 9 Page 4 Table 1. Name an characteristics of moel simulations use in this analysis. Name No. in ensemble Horizontal resolution (atmosphere only) Vertical levels CGCM3 5 T47 (about 3.758) 31 CGCM2 5 T42 (about 2.88) 30 CCSM3 4 T85 (about 1.48) 26 PCM1 4 T42 (about 2.88) 26 ECHAM 4 T63 (about ) 31 GISS-EH 3 Lat lon: MIROC(meres) 3 T42 (about 2.88) 20 integrations of the same couple-climate moel are initialize using ifferent time perios taken from the control simulation an force by the same historical, twentieth-century (generally ) changes in atmospheric chemical constituents an solar activity. (The time perios chosen from the control simulations to initialize the twentieth-century simulations iffer for each moel system; for instance, some moels use time perios that are 20 years apart while others use time perios that are 100 years apart.) The iniviual twentieth-century simulation output at year 2000 is then use to initialize an iniviual simulation (using the same couple-climate moel) force by projecte changes in greenhouse gas concentrations an anthropogenic aerosols over the next 100 years ( ). We will be using moel ata in which the future changes in raiatively active chemical constituents follows the A1B emissions projection terme an emissions scenario from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES; Nakicenovic et al. 2000), which as mentione correspons to a stabilization of CO 2 concentrations at 720 ppm by the year The moels use in this stuy inclue the Canaian Centre for Climate Moelling an Analysis s T47-resolution Couple General Circulation Moel, version 3 (CGCM3; 5 ensemble members); the Meteorological Research Institute s Couple General Circulation Moel version 2 (CGCM2; 5 ensemble members); the Parallel Climate Moel version 1 (PCM1; 4 ensemble members); the Community Climate System Moel, version 3 (CCSM3; 4 ensemble members); ECHAM (4 ensemble members); the Goar Institute for Space Stuies Moel E/ Hybri Coorinate Ocean Moel (HYCOM) (GISS-EH; 3 ensemble members); an the Moel for Interisciplinary Research on Climate 3, meium-resolution version [MIROC(meres); 3 ensemble members] (Solomon et al. 2007). All ata from the moel runs are taken from the Program for Climate Moel Diagnosis an Intercomparison (PCMDI) an are mae available through the Worl Climate Research Programme s (WCRP s) Couple Moel Intercomparison Project (CMIP3) multimoel ataset. Details about each moel are provie online ( www-pcmi.llnl.gov/ipcc/moel_ocumentation/ipcc_moel_ocumentation.php) an summarize in Table 1. For this investigation, we will be examining annualan seasonal-mean precipitation values an their long-term variations. Hence, we first compute the annual an 3-month means for each fiel at each gri point. We then apply a 20-year running mean to each fiel at each gri point. All figures are base upon these 20-year running mean gripoint values unless note otherwise. In aition, in orer to compare across moel systems, we interpolate all gripoint values to the highest-resolution moel gri (CCSM3), which has a T85 (approximately 1.48) resolution.

5 Paper No. 9 Page 5 3. Methos Throughout this paper, skill of moel forecasts whether concurrently or lagge are base upon a moifie version of the Hanssen Kuipers skill score (Hanssen an Kuipers 1965). This measure of skill is base upon a two-imensional contingency table of binomial outcomes between preicte an observe events. In its traitional formulation, it gives the ifference between the number of hits (i.e., forecaste events that occurre) an false alarms (i.e., forecaste events that i not occur). It oes not account for the number of observe nonevents that were correctly preicte or the number of observe events that were misse. For our stuy, however, we consier below-normal precipitation amounts (i.e., negative precipitation trens) to be an equally vali forecast as above-normal precipitation amounts (i.e., positive precipitation trens). Hence, we etermine the consistency between moel forecasts base upon the total number of correct preictions (either of positive or negative precipitation trens) minus the total number of incorrect preictions (again, either of positive or negative precipitation trens). For a traitional contingency table, this calculation woul be equivalent to taking the total number of hits H an correct nonevents N then subtracting off the total number of false alarms F an misses M an normalizing by the number of preictions: Skill 5 (H 1 N) (F 1 M) H 1 N 1 F 1 M : (1) This measure of skill can range from 21 to 1, with numbers above 0 representing fractional improvement upon chance. Numerically, the skill score for a given number (n) of forecasts an observations can be calculate as P n sign(f i ) 3 sign(o i ) i 5 1 Skill 5, (2) n where F i an O i are the ith pair of forecaste an observe values, respectively, an sign( ) inicates the sign of the operan (e.g., 1 for positive values an 21 for negative values). In aition, because we are ealing with finite numbers of preictions in some cases as small as 28 it is necessary to evise a metho for testing for significance of the results. To o this, we construct a stochastic moel in which 28 pairs of values are ranomly selecte from a normal istribution centere on zero, one representing the forecaste value an the other representing the observe value. The test-statistic skill score is calculate for these 28 pairs as P 28 sign(f ran ) 3 sign(o ran ) i i i 5 1 Skill ran 5, (3) 28 where F i ran an O i ran are the ith pair of ranomly selecte forecaste an observe values, an sign( ) is the same as above. A similar estimate is mae

6 for ifferent sets of pairs. The probability istribution function of skill scores from these sets of 28 forecasts an observations is then calculate to etermine the likelihoo certain skill scores coul arise by chance. From these calculations, we fin that skill scores above 0.3 are significantly ifferent from those expecte by chance at the 95% confience level. Similarly, if we ranomly select 28 forecast values an use them to preict a single (ranom) observe value, we also fin that that skill scores above 0.3 are significantly ifferent from those expecte by chance at the 95% confience level. Hence, for all gripoint estimates of skill (in which only 28 sets of ensemble members are available), the minimum significant skill score is set to 0.3, although values above 0 are shown as well. To test the ifference in skill scores between two preictors, a similar methoology is use except that two ranomly istribute forecast values are use as separate preictors for the same ranomly istribute observe value. The skill score for the two separate sets of 28 forecasts observation pairs is calculate an then we fin the ifference; as above we repeat the analysis for ifferent sets. We fin that ifferences in skill scores above are significantly ifferent from those expecte by chance at the 95% confience level; those above are significant at the 90% level. Hence, for gripoint estimates the minimum significant (absolute) skill-score ifference is set to 0.25, although values above 0.20 are shown as well. 4. Results Earth Interactions Paper No. 9 Page Intermoel an intramoel projections of precipitation trens Previous stuies have investigate couple global climate moels ability to simulate historical regional precipitation variations (Zhang et al. 2007) an foun that observe historical trens along certain latituinal bans can be reprouce by multimoel ensemble-mean estimates generate from 10 ifferent moels (5 of which are inclue in this stuy). Other stuies have compare simulate trens with observations across ifferent regions an time perios, some of which appear reproucible (Bhen an von Storch 2008; Barnett et al. 2008) while others are not (Lambert et al. 2005; Allan an Soen 2007). While this ability (or inability) of moels to simulate past regional precipitation variations can hamper the irect use of these same moels to project future changes, below we argue that we can still obtain information from the simulate output about the possible future behavior of the observe climate system, even if the simulations show large isparities in their projections of historical (an future) precipitation changes at regional scales (Allen an Ingram 2002). To start, Figure 1 shows the projecte changes in ensemble mean (EM) gripoint precipitation values between the perios an , as foun in the seven ifferent moel simulations, as well as in the multimoel ensemble mean (MMEM). These trens are qualitatively consistent with those foun in other MMEM projections (Murphy et al. 2004; Zhang et al. 2007; Sun et al. 2007; Solomon et al. 2007) incluing ecreasing trens across most of the subtropics in the Northern an Southern Hemispheres an increasing trens over the mian high latitues of both hemispheres. In aition, there are overall increases in the equatorial an tropical regions of most moels. At the same time, there are

7 Paper No. 9 Page 7 Figure 1. Projecte changes in ensemble mean annual precipitation uner the A1B emissions scenario for the (a) CGCM3.1, (b) ECHAM, (c) CGCM2.1, () CCSM3, (e) MIROC(meres), (f) GISS-EH, an (g) PCM1.0 moel systems, plotte on their native gri. (h) Also shown is the multimoel ensemble mean, plotte on a common T128 resolution gri. Changes are calculate as the ifference between the ensemble-mean precipitation amounts average from 2080 to 2100 an from 2000 to Values are presente as a fraction of the interannual stanar eviation of the 20-year running mean gripoint values for the full perio ( ). Stipple regions in (h) represent gri points in which 6 of 7 (85%) of the moels show the same sign change in precipitation. The box represents area-averaging omain use in Figure 2.

8 Paper No. 9 Page 8 Figure 2. Time evolution of projecte changes in area-average annual precipitation for central Africa uner the A1B emissions scenario for the (a) CGCM3.1, (b) ECHAM, (c) CGCM2.1, () CCSM3, (e) MIROC(meres), (f) GISS-EH, an (g) PCM1.0 moel systems. See Figure 1 for location of area-averaging region; only lan-base gri points are consiere. Colore lines represent iniviual ensemble members from the given moel system; thick black lines represent ensemble mean for the given moel system. Units are kg m 22 s 21. Values represent 20-year running means centere on the given ate; all lines shifte such that initial values start at 0. (h) Time evolution of ensemble-mean area-average annual precipitation for central Africa uner the A1B emissions scenario. Colore lines represent ensemble means from iniviual moel systems (equivalent to thick, black lines in previous panels); the thick black line represents multimoel ensemble mean for all moel systems. moel-base ifferences in the sign of projecte precipitation trens in many regions such as much of Africa, Australia, the Amazon, an the eastern Unite States again in agreement with MMEM estimates of consistency between moel preictions (Murphy et al. 2004; Sun et al. 2007; Solomon et al. 2007). To highlight the ifficulty in preicting the long-term trens in precipitation for these regions, Figure 2 shows the evolution of the area-average precipitation amounts over central Africa (see Figure 1 for area-averaging region) from the seven ifferent moel systems. While three of the EM area-average precipitation amounts are positive [CCSM3, MIROC(meres), an PCM1], two show only small

9 Paper No. 9 Page 9 changes (CGCM2 an GISS-EH) an another two inicate ecreasing precipitation amounts (CGCM2 an ECHAM) over this region. In aition, of 28 separate forecasts (from all seven moel systems), only 16 (57%) have the same sign as the MMEM value. In all, it is apparent that the MMEM projections of precipitation changes across this region are of little value when consiering the range of plausible scenarios as provie by the ifferent moel systems. At the same time, four of the moel systems show high intraensemble consistency [CGCM3, CCSM3, MIROC(meres), an PCM1], with all ensemble members showing the same sign change in precipitation by the perio; in aition, three of the four ensemble members from the ECHAM moel system show the same sign change in precipitation. To better quantify whether a similar improvement in consistency hols in other regions, we estimate the gripoint consistency of precipitation variations using the moifie skill score escribe in section 3. Here, we consier each of the moel realizations at each gri point to be one equally plausible outcome of anthropogenic forcing of climate change; from these we get estimates of what the possible observe value of gripoint precipitation changes for the perio may be (Räisänen an Palmer 2001). We then calculate the MMEM projection of precipitation changes at each gri point an use this as the forecast value. We can then estimate how large the forecaste value skill is, given the range of plausible realizations of the climate system: P 28 sign(o i ) 3 sign(f MMEM ) i 5 1 Skill MMEM 5, (4) 28 where O i is the observe value provie by the ith iniviual moel realization (at a given gri point), F MMEM is the forecaste value provie by the MMEM value (at the same gri point), an sign( ) is as above. Because the forecast value is the same for each of the forecast/observation comparisons, the skill score is a measure of how much the observations, that is, the plausible realizations of future climate change, iffer from one another. Figure 3a shows the results of this comparison. In many regions, particularly the high latitues of the Northern Hemisphere, along with most of northern Eurasia, the skill score is 1.0, inicating that every moel realization (out of 28) is proucing the same sign change in precipitation for the perio In other regions, the skill score falls below the 95% confience interval (skill, 0.3) an actually becomes negative. The first implication of these results is that using the MMEM forecast is a poor preictor for etermining the sign of precipitation trens, given the range of plausible realizations rawn from the iniviual moel runs. Again, this lack of skill arises solely from the lack of consistency in the iniviual realizations (since the forecast is the same for each of these forecast/observation comparisons). We see that consistency is low along the storm-track regions of the North an South Atlantic an Pacific Ocean basins, as well as at the bounaries between the tropics (subtropics) where the expansion (contraction) of the ITCZ (subtropical highs) can prouce iffering trens in precipitation across moels (Neelin et al. 2006; Allan an Soen 2007). In aition, there are regions of low consistency over

10 Paper No. 9 Page 10 Figure 3. (a) The skill of using the sign of the multimoel ensemble-mean precipitation anomalies to preict the sign of the precipitation anomalies in the 28 iniviual moel realizations; anomalies calculate as eviations from the values. Skill base upon a moifie Hanssen Kuipers skill score (see text for etails). Only values greater than 0 shown here; skill scores that are significantly ifferent from chance at the 95% level are shae in color. (b) Same as in (a), except when using the ensemble-mean precipitation anomalies from a given moel system to preict the sign of the precipitation anomalies in the iniviual moel realizations from that moel system only. much of the tropical lanmasses, incluing the Amazon in South America, the Sahel in Africa, an across Inonesia. There is also low consistency over southern Africa an Australia. Qualitatively, regions of low consistency, as etermine by the low skill scores in Figure 3a, match those erive from the MMEM projections (Figure 1h; also Figure 2 from Murphy et al. 2004; Figure 9c from Sun et al. 2007; Figure from Solomon et al. 2007). As mentione, in these regions the low skill score between the MMEM projection of precipitation trens an the range of plausible realizations of these trens (as foun in the iniviual moel runs) suggest that the MMEM projection of precipitation is of little value. However, the MMEM projection is not the only projection available. In Figure 3b, we compare the EM precipitation trens from each moel with its own iniviual moel realizations. We then calculate the overall skill score for the intraensemble preictions/observations: " # Skill EM 5 P 7 P n j j 5 1 i 5 1 sign(o j i ) 3 sign(fem j ) 28, (5) where O i j is the observe value provie by the ith iniviual moel realization from the jth moel (at a given gri point), F j EM is the forecaste value provie by the EM value from the jth moel (at the same gri point), n j is the number of iniviual realizations provie by the jth moel, an sign( ) is as above. In this case, there are still 28 iniviual realizations of the climate system, but now the projections for each realization are not the same but are base upon a priori

11 Paper No. 9 Page 11 Figure 4. Difference in the skill of using the sign of the ensemble-mean precipitation anomalies from a given moel system to preict the sign of the precipitation anomalies in the iniviual moel realizations from that moel system only, compare with the skill of using the sign of the multimoel ensemble-mean precipitation anomalies. Skill is base upon a moifie Hanssen Kuipers skill score (see text for etails). Only values greater than 0.2 (90% confience level) are shown here; values in color are significant at 95% confience level (>0.25). The boxes inicate regions that are analyze further. knowlege of the moel system generating a given realization, hence there are seven ifferent forecast values (one from each moel system). Here we are testing whether, given the right moel system, there is consistency in the relation between EM moel projections an the iniviual realizations of the given moel system. As is evient in Figure 3a, the skill improves markely across the globe an is significant (at the 95% confience interval) everywhere. In aition, in many regions where the MMEM projection ha little skill in forecasting any given realization of the climate system for instance over the Amazon or southern Africa the intraensemble moel skill is significantly greater. In these regions, then, there is little consistency in projections of future climate change between moel systems; however, there is much greater consistency of these projections within the iniviual moel systems. To quantify the improvement in consistency that can be gaine by using the correct moel system to estimate changes of climate for a given realization, as oppose to using the MMEM projection, Figure 4 shows the ifference in skill between the skill scores in Figures 3b an 3a. As mentione, there is significant improvement (.0.25) in skill over the 1) Amazon basin in South America, over 2) central an 3) southern Africa, an over the 4) islan states of Inonesia. There is also improvement over many ocean regions, such as the storm track of the North Atlantic an the subtropical regions of the Pacific. Our focus here, however, will be upon the four regions mentione previously. Figure 5 shows the evolution of the EM area-average precipitation amounts for these four regions, taken from the seven ifferent moel systems; in each case we

12 Paper No. 9 Page 12 Figure 5. Time evolution of projecte changes in area-average ensemble-mean annual precipitation for (a) Amazon basin, (b) central Africa, (c) South Africa, an () Inonesia uner the A1B emissions scenario. See Figure 4 for location of area-averaging regions. For all regions except Inonesia, only lan-base gri points are consiere. Colore lines represent iniviual moel system ensemble means; the thick black lines represent multimoel ensemble mean. Units are kg m 22 s 21. Values represent 20- year running means centere on the given ate; all lines shifte such that initial values start at 0. only use lan-base gri points, except for Inonesia where all gri points within the box are inclue in the area average. In all four regions there is a significant sprea in both the magnitue an sign of the projecte changes across the moel systems (with the exception of central Africa, where only one moel projects a ecrease in area-average precipitation). At the same time, it appears that many of the moel systems follow a quasi-monotonic evolution through time such that the signs of the initial trens in the evolution of the system match the signs of the final trens. While this generalization oes not hol for all moels an all regions [see the ECHAM projections over the Amazon for instance or the MIROC(meres) projections over central an southern Africa], it oes suggest that it is possible to use

13 the intervening trens in the evolution of the climate system to project the sign of the longer-term trens, as represente by the state of the system uring To test this hypothesis, we take each of the EM gripoint moel estimates at a given time an use the sign of the precipitation anomaly (compare with the initial state) as a preiction for the gripoint precipitation anomaly for each realization of that moel system: Skill EM j (t) 5 P n j i 5 1 sign O j i h i ( ) 3 sign O EM(t) j n j, (6) where O j i ( ) is the observe value provie by the ith iniviual moel realization from the jth moel (at a given gri point), O EM j (t) isthe EM value from the jth moel at time t, n j is the number of iniviual realizations provie by the jth moel, an sign( ) is as above. To calculate the skill of this preiction system for a given region, all gripoint preiction/observation pairs within the region are inclue in the hits / misses statistics, hence there are significantly more preictions inclue in these estimates (generally n j is on the orer of 3 5 preictions at separate gri points). In this sense, we are etermining whether persistence of short-term EM gripoint precipitation trens serve as goo preictors for the iniviual long-term realizations of the given moel system. We can also test the skill of the preictions if no a priori knowlege is available regaring which moel system prouce the given realization; in this case the MMEM gripoint precipitation anomaly is use as the preictor for each of the moel realizations: ( ) Skill MMEM (t) 5 Earth Interactions Paper No. 9 Page 13 P7 P n j j 5 1 i 5 1 sign O j i ( ) 3 sign ½ OMMEM (t) Š P 7 j 5 1 n j, (7) where all variables are the same as in Equation (6) except for O MMEM (t), which is the MMEM value at time t (at the given gri point). Figure 6 inicates that, in the four regions consiere here, short-term trens o have significant preictive skill for etermining the sign of long-term trens of the various plausible realizations of precipitation, but only if it is known what moel system to use as the preictor; without this a priori knowlege, the MMEM values at intervening perios show only slight improvement (above chance) in preicting the plausible long-term trens in precipitation given by the iniviual ensemble members. This result highlights that a priori information about the appropriate moel system (or the observe climate system) can improve the forecast capabilities for these regions. It is also important to highlight that the improvement in moel forecast capability is not the same across moels. For instance, the CGCM2 EM oes only about as well as the MMEM at preicting the en state of its own ensemble members over central Africa. This result oes not inicate that the CGCM2 is a worse-performing moel but instea simply inicates that the internal consistency among ensemble members is lower than for other moels.

14 Paper No. 9 Page 14 Figure 6. Skill of using the persistence of the sign of intervening gripoint precipitation anomalies at the given time as preictors for the sign of the gripoint precipitation anomalies. Thin, colore lines represent the skill of the ensemble-mean values from a given moel system in preicting the sign of the values of iniviual moel realizations from that moel system only. Line colors are the same as in Figure 5. The thick black lines represent the average of all the moel systems ensemble-mean skills. The thick re lines represent the skill of the multimoel ensemble-mean values in preicting the sign of the values of iniviual moel realizations from all moel systems. Skill calculate separately for (a) Amazon basin, (b) central Africa, (c) South Africa, an () Inonesia. See Figure 4 for location of area-averaging regions. For all regions except Inonesia, only lan-base gri points are consiere. Preictor values represent the ifference between the 20-year mean value centere on the given ate an the initial 20-year mean value; by construction all lines start at Iniviual moel projections of precipitation trens We note that, in each of the cases shown in Figure 6, the forecast estimate was erive from an ensemble of moel realizations. However, in the actual climate system, the observe evolution only represents one realization of its own internal

15 Paper No. 9 Page 15 ynamics. Determining which moel climate system the actual climate system maps onto may be extraorinarily ifficult (an may actually change from region to region). In aition, as results show, ifferent moel systems can have iffering internal consistency in the behavior of their iniviual realizations. At the same time, it woul be of interest to etermine whether the short-term evolution of iniviual realizations of the climate system can be use as preictors for their own evolution. In this sense, the a priori knowlege of the governing moel system is self-containe within the evolution of the iniviual moel realization itself. To test for this ability, for each moel system we take each of the gripoint anomalies from an iniviual moel realization at a given time an use the sign of the precipitation anomaly (compare with the initial state) as a preiction for its own gripoint precipitation anomaly: Skill iniv j (t) 5 P n j i 5 1 sign O j i ( ) 3 sign O j i (t) n j, (8) where O i j ( ) is the observe value provie by the ith iniviual moel realization from the jth moel (at a given gri point), O i j (t)isthevalue from the ith iniviual moel realization from the jth moel at time t, n j is the number of iniviual realizations provie by the jth moel, an sign( ) is as above. As before, the skill is calculate using all gri points within a given region, so n j is much larger than 28. Results are shown in Figure 7. As before, the evolution of iniviual moel realizations, an their consistency in use as preictors for the final state of the moel realization, epens upon the unerlying moel system. As an extreme example, iniviual realizations of gripoint precipitation over Inonesia from the ECHAM moel system appear to be very poor preictors of their final states. Again, this oes not inicate that the ECHAM moel is a poor one, simply that the short-term moel trens in this region o not necessarily serve as goo preictors for the final en state. However, if we calculate the average skill score for all moel realizations, we fin that in all four regions it lies above 0.5 by 2050 an in some regions (the Amazon, southern Africa, an Inonesia once the ECHAM moel is remove) it is above 0.5 by Because these time series are plotte at the center of the 20-year averaging perio, these results suggest that short-term trens (e.g., uring the perio) can be use as preictors for the sign of the tren in these regions uring While these results o not soun promising, it shoul be note that uring the perio, the greenhouse gas concentrations, as represente by the concentrations of CO 2 in the A1B scenario, are expecte to be about 90 ppm higher than the initial perio (e.g., an increase from 390 to 484 ppm). This increase of 90 ppm is nearly equivalent to the observe increase in CO 2 concentrations uring the perio (i.e., from 280 to 370 ppm). In this sense, these results suggest that the observe trens in precipitation over the last 100 years may serve as preictors for the sign of future climate change over the next 100 years. However, to confirm this hypothesis it will be necessary to compare historical realizations of the climate system with future realizations to see whether similar increases in skill are foun, a stuy that is beyon the scope of this paper (but is presently being carrie out).

16 Paper No. 9 Page 16 Figure 7. Skill of using the persistence of the sign of intervening gripoint precipitation anomalies from iniviual moel realizations at the given time as preictors for the sign of gripoint precipitation anomalies from the same realization. Colore lines represent the skill average over each realization from a given moel system; line colors are the same as in Figure 5. The thick black lines represent the mean of all the moel systems skills. Skill is calculate separately for (a) Amazon basin, (b) central Africa, (c) South Africa, an () Inonesia. See Figure 4 for location of areaaveraging regions. For all regions except Inonesia, only lan-base gri points are consiere. The thick re line in () represents the mean of all the moel systems skills after removing the ECHAM moel. Preictor values represent the ifference between the 20-year mean value centere on the given ate an the initial 20-year mean value; by construction all lines start at 0 an en at 1. Next, we perform a similar analysis for all gri points in orer to etermine what regions of the globe show consistency in their short-term an long-term trens of precipitation. To o so requires selecting a specific time perio upon which to base the preictions, at which point it is possible to etermine the skill of these preictions. Base upon Figure 7, we select the perio as the preiction perio. We then use the sign of the MMEM gripoint estimates uring the

17 Paper No. 9 Page 17 perio as the same preictor for the gripoint precipitation anomalies taken from the iniviual realizations of all the moel systems (Figure 8a). For comparison, we also use the EM gripoint moel estimates uring the perio as a preiction for the gripoint precipitation anomalies taken from the iniviual realizations of the given moel system (Figure 8b). Finally, we use the iniviual moel realization gripoint anomaly estimates uring the perio as a preiction for their own gripoint precipitation anomalies (Figure 8c). As expecte, in the regions we have been stuying short-term trens from the MMEM gripoint estimates serve as poor preictors for the long-term tren of precipitation, given the range of plausible iniviual realizations of the long-term climate evolution. In other regions, however, the skill is significant an matches that of the actual MMEM gripoint preictions from 2080 to 2100 (see Figure 3a). In comparison, the skill provie by the short-term EM anomalies in preicting the iniviual moel realizations of its own ensemble members is greater than the MMEM preictions almost everywhere. In particular the improvement is large over the regions of interest here, namely, the Amazon basin, central an southern Africa, an Inonesia. As before, these results suggest that if a priori knowlege is available regaring which moel system to use as the preictor for a given realization, the short-term trens in the EM precipitation for these regions can serve as significant preictors for the longer-term evolution of iniviual ensemble members. As before, there is only one observe evolution of the actual climate system itself. However, base upon Figure 8c, it appears that, for many regions, short-term iniviual realizations of climate change can be use as preictors for their own long-term evolutions. Even in regions where fully couple moel preictions of trens in precipitation show little intermoel consistency, the short-term evolution of a single realization appears to provie aitional information about its future state. While not perfect, by construction the skill associate with these preictions averages about 0.5 in the regions examine earlier; that is, about 75% of the moel preictions capture the correct sign of future trens in precipitation. In aition, even outsie regions examine here there appears to be skill foun in the short-term evolution of the climate system, which provies information about its long-term behavior. For example, over Australia, western Inia, an the western Unite States regions in which MMEM climate projections inicate inconsistency in the overall tren of precipitation short-term trens can provie some guiance regaring the longer-term evolution. Figure 9 shows the ifference in skill provie by the sign of the precipitation anomalies from iniviual moel realizations in preicting their own en state, as compare with the skill provie by the sign of the precipitation anomalies from the MMEM values. As mentione, short-term trens in iniviual moel realizations appear to provie enhance skill over much of Africa, the Amazon basin, Australia, an the western Unite States. At the same time, it is apparent that in certain regions particularly the high-latitue regions of North America an Eurasia the MMEM provies a much better estimate of the long-term behavior of the climate system than that affore by any one moel realization. Hence, in many regions care must be place in projecting observe short-term trens into the future, particularly in the presence of nonlinear an/or nonstationary behavior (DelSole 2005).

18 Paper No. 9 Page 18 Figure 8. Skill of using the sign of the precipitation anomalies to preict the sign of the precipitation anomalies in the 28 iniviual moel realizations; anomalies calculate as eviations from the values. Sign of gripoint precipitation anomalies etermine from (a) multimoel ensemble-mean gripoint value, (b) ensemble-mean gripoint value from the moel system that generate the iniviual moel realization, an (c) the iniviual moel realization itself. Skill is base upon a moifie Hanssen Kuipers skill score (see text for etails). Only values greater than 0 shown here; skill scores that are significantly ifferent from chance at the 95% level are shae in color. The boxes inicate regions that are analyze in previous figures Projections of seasonal-mean precipitation trens Given that the signs of future anthropogenic-inuce climate changes can vary between seasons (Solomon et al. 2007), it is of interest to see how seasonally base results may iffer. Generally, the skill of the MMEM precipitation anomalies from

19 Paper No. 9 Page 19 Figure 9. Difference in the skill of using the sign of the precipitation anomalies from iniviual moel realizations to preict the sign of the precipitation anomalies from the same realization, compare with the skill of using the sign of the multimoel ensemble-mean precipitation anomalies; anomalies calculate as eviations from the values. Skill is base upon a moifie Hanssen Kuipers skill score (see text for etails). Only values greater than 60.2 (90% confience level) are shown here; values in color are significant at 95% confience level (60.25). the perio in preicting the sign of any one of the iniviual moel realizations is worse for seasonal-mean values [here December February (DJF), March May (MAM), June August (JJA), an September November (SON)] than for the annual means (not shown). In aition, the MMEM skill (an hence intermoel consistency) seems to ecrease the most over high-latitue regions uring hemispheric summer, incluing over most of Eurasia an North America uring June August an southern Africa an Australia uring December February. This iscrepancy most likely arises because of ifferences in convective schemes an in the strength of lan atmosphere coupling in the various moel systems. If we compare the intermoel consistency with the intramoel consistency erive by using the EM precipitation anomalies from the perio as the preictor for the iniviual ensemble members themselves we fin that the skill improves if a priori knowlege of the appropriate moel system is incorporate into the preiction. This is particularly true over North America, the Sahel, an southwestern Asia (incluing northern Inia, Pakistan, an Afghanistan) uring the June August perio; southern Africa an Australia uring the December February perio; the Amazon basin uring the March May an September November perios; an Inonesia an northern Australia uring all four seasons. In aition, we fin that the intramoel consistency of precipitation trens is significant at all gri points, regarless of season (not shown). Finally, we can etermine whether the persistence of using short-term trens from iniviual moel realizations provies aitional skill in forecasting the sign of long-term trens in seasonal-mean precipitation as compare with using the

20 Paper No. 9 Page 20 Figure 10. (a) Difference in the skill of using the sign of the DJF precipitation anomalies from iniviual moel realizations to preict the sign of the DJF precipitation anomalies from the same realization, compare with the skill of using the sign of the DJF multimoel ensemble-mean precipitation anomalies; anomalies are calculate as eviations from the values. Skill is base upon a moifie Hanssen Kuipers skill score (see text for etails). Only values greater than 60.2 (90% confience level) are shown here; values in color are significant at 95% confience level (60.25). (b) () Same as in (a), except for MAM, JJA, an SON precipitation anomalies. MMEM estimates (Figure 10). As before the short-term trens are erive from the various preictor fiels uring the time perio. While the short-term trens from the iniviual realizations o not provie as much skill in preicting their own long-term seasonal-mean evolution as compare with the skill foun in annualmean precipitation (not shown), they o provie better skill than the MMEM values over many regions, particularly over the tropics an subtropics. In aition, the improvement in skill tens to follow the seasonal cycle in precipitation. For instance, there is ae skill in forecasting December February precipitation changes over the southern portions of Africa, when precipitation tens to be greatest; similarly, over central Africa, there is ae skill uring June August, again when the seasonal cycle of precipitation peaks in this region. Over the Amazon, improve skill is foun uring the onset (September November) an retreat (March May) of the monsoon rains. Over the southern portions of North America an Europe, skill appears to increase most uring summer (June August) an into fall (September November). As before, results inicate that the MMEM estimates from 2030 to 2050 still serve as the best preictors for precipitation in the high-latitue regions of North America an Eurasia, particularly uring March May. However, in other

21 Paper No. 9 Page 21 regions it appears that these MMEM forecasts can be augmente, or constraine, by historical an future observations of short-term trens. 5. Summary Here we have analyze the consistency in global climate change moel preictions of regional precipitation trens. Results inicate that, while certain regions show high inter- an intramoel consistency in projections of regional precipitation responses to anthropogenic-inuce climate change, other regions show little consistency in even the sign of these changes. In these regions, the large sprea of iniviual moel realizations, each of which is consiere an equally plausible evolution of the actual climate system, preclues the use of multimoel ensemble means to make preictions of the response of the climate system to anthropogenic emissions of raiatively active chemical constituents. However, given a priori knowlege of the unerlying (moel) climate system, the skill of the projections is markely improve, inicating that intramoel consistency is robust, even if intermoel consistency is not. This result suggests that the evolution of the system is constraine by the internal ynamics of the moel itself; this result also suggests that the time-epenent behavior of a single realization of the climate system, which by efinition is governe by the same moel ynamics throughout its evolution, may be able to provie information about its own long-term time evolution. To test this hypothesis we examine four specific regions the Amazon basin in South America, the central an southern portions of Africa, an Inonesia where intramoel consistency between ensemble members significantly improves compare with intermoel consistency. First, we quantify the skill of using the sign of the gripoint precipitation anomalies erive from the ensemble-mean value of a given moel system at some intervening time as the preictor for the sign of the precipitation anomalies at the en of the simulation perio (e.g., ) from iniviual realizations of the same moel. We fin this skill is significantly improve compare with the skill of using intervening gripoint precipitation anomalies erive from the multimoel ensemble-mean value. In aition, shortterm trens of iniviual moel realizations also provie improve skill in preicting their own state by the en of the simulation perio in the four regions consiere here, approximately 75% of the iniviual moel gripoint trens uring the perio correctly preict the sign of their own gripoint trens uring the final 20 years of the simulation perio. While these results suggest that only after 501 years of climate forcing oes the short-term tren consistently match the long-term tren in these regions, the climate forcing associate with this 50-year perio (capture by an approximate 100-ppm increase in carbon ioxie concentrations) is similar to that impose over the last 100 years ( ). These results suggest that precipitation trens uring this historical perio may provie guiance regaring the sign of future precipitation trens over the next 100 years; however, care must be taken in using short-term precipitation trens as preictors for longer-term trens because results are sensitive to the moel system, geographic location, an time of year. At the same time, they o suggest that the actual evolution of the climate system, vis-à-vis precipitation changes, oes inherently contain information about its own future evolution that can be use to augment moel-base climate change projections.