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2Climate PredictionThis part of the report begins by reviewing the concept of predictability, starting with a summary of the historical background for climate prediction. Lorenz’s work on weather prediction in the 1960s and 1970s is a foundation for present efforts. Progress in the 1980s extended prediction timescales, exploiting improved observational awareness of ENSO variability in the tropical Pacific and its associated teleconnections. Future improvements in prediction quality depend upon the ability to identify and understand patterns of variability and specific processes that operate on ISI timescales. Various processes in the atmosphere, ocean, and land offer sources of predictability; several are introduced in the following sections.
Gaps in our present understanding of predictability are summarized to lay the foundation for discussion later in the report on how the future improvements are likely to be realized. In going forward, it will be necessary to assess the incremental skill gained from new sources of predictability. The methodologies to be used to quantitatively estimate prediction skill, validate models, and verify forecasts are discussed. THE CONCEPT OF PREDICTABILITYLorenz in 1969 defined predictability as “a limit to the accuracy with which forecasting is possible” (Lorenz, 1969a). Atmospheric PredictabilityLorenz noted that practical predictability was a function of: (1) the physical system under investigation, (2) the available observations, and (3) the dynamical prediction models used to simulate the system. He noted in 2006 that the ability to predict could be limited by the lack of observations of the system and by the dynamical models’ shortcomings in their forward extrapolations. While estimates of the predictability of day-to-day weather have been made by investigating the physical system, analyzing observations, and experimenting with models , no single approach provides a definitive and quantitative estimate of predictability.
BOX 2.1WEATHER AND CLIMATE FORECASTS AND THE IMPORTANCE OF INITIAL CONDITIONSForecasts are computed as “initial value” problems: they require realistic models and accurate initial conditions of the system being simulated in order to generate accurate forecasts. Lorenz (1965) showed that even with a perfect model and essentially perfect initial conditions, the fact that the atmosphere is chaotic causes forecasts to lose all predictive information after a finite time. He estimated the “limit of predictability” for weather as about two weeks, an estimate that still stands: it is generally considered not possible to make detailed weather predictions beyond two weeks based on atmospheric initialization alone. Lorenz’s discovery was initially only of academic interest since, at that time, there was little quality in operational forecasts beyond two days, but in recent decades forecast quality has improved, especially since the introduction of ensemble forecasting.
Useful forecasts now extend to the range of 5 to 10 days (see ). Tropical atmosphere could be predicted at longer lead-times as well. Observational, theoretical, and modeling studies (Horel and Wallace, 1981; Sarachik and Cane, 2010) demonstrated that there were relationships between variability observed in the tropical oceans and the variability of the extratropical atmosphere. It became clear that longer-range forecasts of atmospheric quantities could be made using predictions of the coupled ocean-atmosphere system.Although operational, extended forecasts continued to focus on surface temperature and precipitation over continents, the atmospheric initial conditions were no longer considered important for making these forecasts; atmospheric ISI prediction was now considered a boundary value problem (Lorenz, 1975; Chen and Van den Dool, 1997; Shukla, 1998; Chu, 1999). Boundary forcing, initially from the ocean but later from the land and cryosphere (Brankovic et al., 1994), was used as the source of predictive information.
This was appropriate because coupled models of the atmosphere, ocean, and land surface were still in their infancy and were not competitive with statistical prediction models (Anderson et al., 1999).Given this context, researchers asked: if there exists a perfect prediction of ocean or land conditions, how well could the state of the mid-latitude atmosphere be predicted (Yang et al., 2004)? This question has been addressed observationally by estimating the signal-to-noise ratio. In this case the portion of the climate variance related to the lower boundary forcing is the signal, the portion of the climate variance related to atmospheric internal dynamics is the noise, and the ratio of the two represents one possible measure of predictability (e.g., Kang and Shukla, 2005). Such studies can lead to overly optimistic estimates of predictability because they assume that the boundary conditions are predicted perfectly.There is an additional problem with this boundary-forced approach. These estimates assume that feedbacks between the atmosphere and the ocean do not contribute to the predictability. However, coupling between the atmosphere and the ocean can also be important in the evolution of SST anomalies (Wang et al., 2004; Zheng et al., 2004; Wu and Kirtman, 2005; Wang et al.
2005; Kumar et al., 2005; Fu et al., 2003, 2006; Woolnough et al., 2007). Because the boundary-forced approach ignores this atmosphere-ocean co-variability (or any other climate system component couplings), these boundary-forced predictability estimates are of limited use. Climate System PredictabilityThe techniques for estimating predictability shown in can be applied to the coupled prediction problem (e.g., Goswami and Shukla, 1989; Kirtman and Schopf, 1998).
However, each method is still subject to limitations similar to those mentioned in. Given the complexity of the climate system, estimates based on analytical closure are somewhat intractable (i.e., how can error growth rates from a simple system of equations relate to the real climate system?); approaches based on observations are limited by the relatively short length of the observational record, combined with the difficulty in identifying controlled analogs for a particular state of the climate.
Non-stationarity in the climate system further reduces the chance that observed analogs would become useful in the foreseeable future, if ever. Model-based estimates are thus the most practical, but are still limited by the ability to measure the initial conditions for the climate and the mathematical representation of the physical processes.As discussed in, most efforts to estimate prediction quality (or hindcast quality) are relatively recent, and involve analysis of numerous model-generated predictions for a similar. Time period (Waliser, 2005; Waliser, 2006; Woolnaugh et al. 2007; Pegion and Kirtman, 2008; Kirtman and Pirani, 2008; Gottschalck et al. For example, Kirtman and Pirani (2008) reported on the WCRP Seasonal Prediction Workshop in Barcelona where the participants discussed validating and assessing the quality of seasonal predictions based on a number of international research projects on dynamical seasonal prediction (e.g., SMIP2/HFP, DEMETER, ENSEMBLES, APCC). This collection of international projects includes a variety of different experimental designs (i.e., coupled vs.
Uncoupled), different forecast periods, initial condition start dates, and levels of data availability. Despite these differences, there was an attempt to arrive at consensus regarding the current status of prediction quality. Several different deterministic and probabilistic skill metrics were proposed, and it was noted that no single metric is sufficiently comprehensive. This is particularly true in cases where forecasts are used for decision support. Nevertheless, the workshop report includes an evaluation of multi-model prediction for Nino3.4 SSTA, 2m-temperature and precipitation in 21 standard land regions (Giorgi and Francisco, 2000). While it was recognized that the various skill metrics used were incomplete and that there were difficulties related to the different experimental designs and protocols, the consensus was clear that multi-model skill scores were on average superior to any individual model (Kirtman and Pirani, 2008). Systematic efforts along the above lines for the intraseaosnal time scale have only recently begun with the development of an MJO forecast metric and a common approach to its application amongst a number of international forecast centers (Gottschalck et al.
2010) as well as the establishment of a multi-model MJO hindcast experiment (see ). Overview of Physical FoundationsClimate reflects a complex combination of behaviors of many interconnected physical and (often chaotic) dynamical processes operating at a variety of time scales in the atmosphere, ocean, and land. Its complexity is manifested in the varied forms of weather and climate variability and phenomena, and in turn, in their fundamental (if unmeasurable) limits of predictability, as defined above.
Yet, embedded in the climate system are sources of predictability that can be utilized. Three categories can be used to characterize these sources of weather and climate predictability: inertia, patterns of variability, and external forcing. The actual predictability associated with an individual phenomenon typically involves interaction among these categories.The first category is the “inertia” or “memory” of a climate variable when it is considered as a quantity stored in some reservoir of nonzero capacity, with fluxes (physical climate processes) that increase or decrease the amount of the variable within the reservoir over time, e.g., soil moisture near the land-atmosphere interface. Taking the top meter of soil as a control volume and the moisture within that volume as the climate variable of interest, the soil moisture increases with water infiltrated from the surface (rainfall or snowmelt), decreases with evaporation or transpiration, and changes further via within-soil fluxes of moisture through the sides and bottom of the volume.
For a given soil moisture anomaly, the lifetime of the anomaly. (and thus our ability to predict soil moisture with time) will depend on these fluxes relative to the size of the control volume. Soil moisture anomalies at meter depth have inherent time scales of weeks to months.
As panel (a) of shows, soil moisture anomalies exist considerably longer than the precipitation events that cause them.Arguably, many variables related to the thermodynamic state of the climate system have some inertial memory that can be a source of predictability. Surface air temperature in a small regional control volume, for example, is a source of predictability that is very short given the efficiency of the processes (winds, radiation, surface turbulent fluxes, etc.) that affect it. If the air temperature at a given location is known at noon, its value at 12:05 PM that day can be predicted with a very high degree of certainty, whereas its predicted value days later is much more uncertain. In stark contrast, the inertial memory of ocean heat content can extend out to seasons and even years, depending on averaging depth. Examples of other variables with long memories include snowpack and trace gases (e.g., methane) stored in the soil or the ocean.The second category involves patterns of variability—not variables describing the state of the climate and their underlying inertia, but rather interactions (e.g., feedbacks) between variables in coupled systems. These modes of variability are typically composed of amplification and decay mechanisms that result in dynamically growing and receding (and in some cases oscillating) patterns with definable and predictable characteristics and lifetimes. With modes of variability, predictability does not result from the decay of an initial anomaly associated with fluxes into and out of a reservoir, as in the first category, but rather with the prediction of the next stage(s) in the life cycle of the dynamic mode based on its current state and the equations or empirical relationships that determine its subsequent evolution.
In many examples related to inertia or memory within the climate system, the atmosphere plays a “passive” and dissipative role in the evolution of the underlying anomaly. On the other hand, for the patterns of variability or feedbacks discussed here, the atmosphere plays a more active role in amplifying or maintaining an anomaly associated with processes occurring in the ocean or on land.“Teleconnections” is a term used to describe certain patterns of variability, especially when they act over relatively large geographic distances. Teleconnections illustrate how interaction among the atmosphere, ocean, and land surface can “transmit” predictability in one region to another remote region. For example, during ENSO events, features of the planetary scale circulation (e.g., the strength and location of the mid-latitude jet stream) interact with anomalous convection in the tropical Pacific. These interactions can lead to anomalous temperature and precipitation patterns across the globe (panel b of ). Thus, predictions of tropical Pacific sea surface temperature due to ENSO can be exploited to predict air temperature anomalies in some continental regions on the time scales of months to seasons. For air temperature, this teleconnection pattern offers enhanced predictability compared to memory alone, which would only be useful for minutes to hours.
It should be noted that the predictability of teleconnection responses (in the above example, air temperature in a location outside of the tropical Pacific) will be lower than that of the source (in the above example, tropical Pacific SST) because of dynamical chaos that limits the transmission of predictability.The third category involves the response of climatic variables to external forcing, and it includes some obvious examples. Naturally, many Earth system variables respond in very predictable ways to diurnal and annual cycles of solar forcing and even to the much longer cycles associated with orbital variations. Other examples of external forcing variations that can provide. Examples of Predictability Sourcesprovides a quick glimpse of various predictability sources in terms of their inherent time scales. This view, based on time scale, is an alternative or complement to the three-category framework (inertia, patterns of variability, and external forcing).
Provided in the present section is a broad overview of predictability sources relevant to ISI time scales. Some of the examples will be discussed more comprehensively in later chapters.It is important to realize that the timescales associated with sources of predictability often arise from a combination of inertia and feedback processes. Also, it should be noted that the. FIGURE 2.4 Processes that act as sources of ISI climate predictability extend over a wide range of timescales, and involve interactions among the atmosphere, ocean, and land. CCEW: convectively coupled equatorial waves (in the atmosphere); TIW: tropical instability wave (in the ocean); MJO/MISV: Madden-Julian Oscillation/Monsoon intraseasonal variability; NAM: Northern Hemisphere annular mode; SAM: Southern Hemisphere annular mode; AO: Arctic oscillation; NAO: North Atlantic oscillation; QBO: quasi-biennial oscillation, IOD/ZM: Indian Ocean dipole/zonal mode; AMOC: Atlantic meridional overturning circulation. For the y-axis, “A” indicates “atmosphere;” “L” indicates “land;” “I” indicates “ice;” and, “O” indicates “ocean.”timescales in indicate the timescale of the variability associated with a particular process.
This is distinct from the timescale associated with a prediction. For example, ENSO exhibits variability on the scale of years; however, information about the state of ENSO can be useful for making ISI predictions on weekly, monthly, and seasonal time scales.As discussed in (see Committee Approach to Predictability), it can be difficult to quantify the intrinsic predictability associated with any of the individual processes depicted in (i.e., for what lead-time is an ENSO prediction viable? And to what extent would that prediction contribute to skill for predicting temperature or precipitation in a particular region?).
As mentioned earlier (see Climate System Predictability), prediction experiments form the foundation of our understanding. However, these experiments are rarely definitive in quantifying such limits of predictability. For example, for ENSO, there are three competing theories (inherently nonlinear; periodic, forced by weather noise; and the damped oscillator) that underlie various models of ENSO, each with its own estimate of predictability (see Kirtman et al., 2005 for a detailed discussion). At this time we are unable to resolve which theory is correct. Since all yield results that are arguably “consistent” with observational estimates.
To further complicate the understanding of the limits of predictability for ENSO, there are important interactions with other sources of predictability that may enhance or inhibit the predictability associated with ENSO (see ). ENSO is just one example of how understanding what “sets” the predictability associated with a particular process is a critical challenge for the ISI prediction community. The challenge of improving forecast quality necessitates enhancing the individual building blocks (see ) that make up our predictions systems, but it also requires a deeper understanding of the physical mechanisms and processes that are the sources of predictability. Upper ocean heat contentOn seasonal-to-interannual time scales upper ocean heat content is a known source of predictability. The ocean can store a tremendous amount of heat.
The heat capacity of 1 m 3 of seawater is 4.2 x 10 6 joules m -3 K -1 or 3,500 times that of air and 1.8 times that of granite. Sunlight penetrates the upper ocean, and much of the energy associated with sunlight can be absorbed directly by the top few meters of the ocean. Mixing processes further distribute heat through the surface mixed layer, which can be tens to hundreds of meters thick. As Gill (1982) points out, with the difference in heat capacity and density, the upper 2.5 m of the ocean can, when cooling 1ºC, heat the entire column of air above it that same 1ºC. The ocean can also transport warm water from one location to another, so that warm tropical water is carried by the Gulf Stream off New England, where in winter during a cold-air outbreak, the ocean can heat the atmosphere at up to 1200 W m -2, a heating rate not that different from the solar constant. Stewart (2005) shows that a 100 m deep ocean mixed layer heated 10ºC seasonally stores 100 times more heat than 1 m thick layer of rock heated that same 10ºC; as a result the release of the heat from the ocean mixed layer can have a large impact on the atmosphere. Thus, the atmosphere acts as a “receiver” of any anomalies that have been stored in the ocean, and predictions of the evolution of air temperature over the ocean can be improved by consideration of the ocean state.
Soil moistureSoil moisture memory spans intraseasonal time scales. Memory in soil moisture is translated to the atmosphere through the impact of soil moisture on the surface energy budget, mainly through its impact on evaporation. Soil moisture initialization in forecast systems is known to affect the evolution of forecasted precipitation and air temperature in certain areas during certain times of the year on intraseasonal time scales (e.g., Koster et al., 2010). Model studies (Fischer et al., 2007) suggest that the European heat wave of summer 2003 was exacerbated by dry soil moisture anomalies in the previous spring. Anomalies during and following the snowmelt season, anomalies that are of direct relevance to water resources management and that in turn could feed back on the atmosphere, potentially providing some predictability at the seasonal time scale.
The impact of October Eurasian snow cover on atmospheric dynamics may improve the prediction quality of northern hemisphere wintertime temperature forecasts (Cohen and Fletcher, 2007). The autumn Siberian snow cover anomalies can be used for prediction of the East Asian winter monsoon strength (Jhun and Lee, 2004; Wang et al., 2009). VegetationVegetation structure and health respond slowly to climate anomalies, and anomalous vegetation properties may persist for some time (months to perhaps years) after the long-term climate anomaly that spawned them subsides. Vegetation properties such as species type, fractional cover, and leaf area index help control evaporation, radiation exchange, and momentum exchange at the land surface; thus, long-term memory in vegetation anomalies could be translated into the larger Earth system (e.g. Zeng et al., 1999).
Land heat contentThermal energy stored in land is released by molecular diffusion and thus over all time scales, but with a rate of release that decreases with the square root of the time scale. In practice, there is strong diurnal storage (up to 100 W m -2) of heat energy and a still significant amount over the annual cycle (up to 5 W m -2). This is particularly strong in relatively unvegetated regions where solar radiation is absorbed mostly by the soil, since vegetation has much less thermal inertia, or in higher latitudes where soil water seasonally freezes. Polar sea iceSea ice is an active component of the climate system and is highly coupled with the atmosphere and ocean at time scales ranging from synoptic to decadal. When large anomalies are established in sea ice, they tend to persist due to inertial memory and to positive feedback in the atmosphere-ocean-sea ice system. These characteristics suggest that some aspects of sea ice may be predictable on ISI seasonal time scales. In the Southern Hemisphere, sea ice concentration anomalies can be predicted statistically by a linear Markov model on seasonal time scales (Chen and Yuan, 2004).
The best cross-validated skill is at the large climate action centers in the southeast Pacific and Weddell Sea, reaching 0.5 correlation with observed estimates even at 12-month lead time, which is comparable to or even better than that for ENSO prediction. We have less understanding of how well sea ice impacts the predictability of the overlying atmosphere. Patterns of VariabilityDifferent components of the climate system, each with their own inertial memory, interact with each other in complex ways. The dynamics of the feedbacks and interactions can lead to the development of predictable modes, or patterns, of variability.It should be noted that the descriptions for the patterns of variability provided in the following subsections describe their “typical” behavior, focusing on commonalities among observed events and the mechanisms that drive the phenomena. In reality, the manifestation or impact of a pattern may differ from these “typical” cases since the various patterns of variability can be affected by one another as well as by the unpredictable “noise” inherent to the climate system, especially in the atmosphere.
For example, not all ENSO events have the same features, and in some cases, these differences among events can be understood from interactions between ENSO and the MJO (see the MJO case study in ). Low-frequency equatorial waves in the atmosphere and oceanThe equator provides an efficient wave guide by which tropical dynamical energy is organized, propagated, and dissipated.
In the atmosphere, equatorial Kelvin and Rossby waves and mixed Rossby-Gravity waves (Matsuno, 1966) are observed. Due to the moist and vertically unstable nature of the tropics, these low-frequency waves are often associated with convection and are referred to as convectively-coupled equatorial waves (CCEWs) (Wheeler and Kiladis, 1999; Kiladis et al., 2009). The spatial scales of these disturbances can be quite large (on the order of thousands of kilometers), and their time scales for propagating across ocean basins can be of the order of days to weeks. Shows a time-longitude plot of equatorial outgoing longwave radiation (OLR) anomalies, produced following a wavenumber-frequency analysis. OLR is a good proxy for deep tropical convection, and the colors in show areas of enhanced (hot colors) or suppressed (cool colors) convection. These patterns in OLR correspond to characteristic types of waves (green, blue, and black ovals), illustrating that variability in the tropical atmosphere is consistent with the simplified theory of Matsuno (Kiladis et al., 2009).
Also demonstrates the manner in which these waves are manifest in relation to the typical background variability. Although complicated by their coupling to atmospheric convection, the organization and propagation of these low-frequency waves provides an element of predictability for the tropical atmosphere and possibly the extra-tropics via teleconnections.Analogous to the discussion of the atmosphere above, the equatorial ocean supports the presence of equatorial wave modes, such as the Kelvin, Rossby and mixed-Rossby gravity modes. One simplifying aspect for their presence in the ocean is that, in contrast to the atmosphere, no convection or phase changes are involved. Because the equivalent depth of the ocean is considerably smaller than that for the atmosphere, its propagation speeds are much slower, and thus the time scale (and the predictability that arises from it) is much longer (e.g., a Kelvin wave takes about 2–3 months to cross the Pacific Ocean). These waves play a crucial role in the ocean thermocline adjustment and ENSO turnaround, as discussed below.
FIGURE 2.5 Some atmospheric waves offer an important source of predictability. The time-longitude diagram depicts the speed and direction of propagation that Kelvin waves (green ovals), Rossby waves (black ovals), and waves associated with the MJO (blue ovals) can exhibit in the tropics. The dense shading, which often overlaps with the position of the ovals, corresponds to anomalies in outgoing longwave-radiation (OLR); positive OLR anomalies indicate clear skies and suppressed convection; negative OLR anomalies indicate enhanced convection. SOURCE: Adapted from Wheeler and Weickmann (2001). Madden-Julian Oscillation (MJO)Another fundamental mode of tropical convectively-coupled wave-like variability is the Madden-Julian Oscillation (MJO; Madden and Julian, 1972, 1994). MJOs operate on the planetary scale, with most of the convective disturbance and variations occurring in the Indo-Pacific warm pool regions. The typical time scale of these quasi-periodic disturbances is of the order of 40–50 days.
They tend to propagate eastward in boreal winter and north and/or northeastward in boreal summer. They strongly influence the onsets and breaks of the Australian and Asian monsoons and are sometimes referred to as monsoon intraseasonal variability (MISV) or oscillation (MISO).
As with the CCEWs mentioned above, they are thought to be a source of both local predictability and predictability in the extra-tropics. The MJO and its associated predictability are discussed in more detail in of this report. Illustrates composite MJO events for boreal summer (May–October). FIGURE 2.6 Characteristic rainfall patterns (mm per day) before, during, and following an MJO event during the boreal summer (May–October).
Dry anomalies are indicated by “cool” colors (green, blue, purple) and wet anomalies are indicated by “hot” colors (yellow, orange, red). SOURCE: Waliser et al. (2005).with variations in depth on the order of tens of meters and in temperature of the order of a degree. This process can impart a feedback onto the atmospheric wave processes which influences their subsequent evolution (e.g. Amplitude, propagation speed). Annular Modes (Northern or Southern, NAM or SAM)The Annular Modes, also refereed to as the Arctic Oscillation in the Northern Hemisphere, or the Antarctic Oscillation in the Southern Hemisphere, are dominant modes of variability outside the tropics.
They are established on a weekly time scale due to atmospheric internal dynamics (such as mean flow-wave interaction or stratosphere-troposphere interaction). They offer some predictability on seasonal time scales through longer-timescale persistence of stratospheric winds (Baldwin and Dunkerton, 1999). The modes can influence surface temperature and precipitation, especially the frequency of extreme events (Thompson and Wallace, 2001).The manifestation of the Arctic Oscillation in the Atlantic sector is commonly referred to at the North Atlantic Oscillation (NAO). An index for the NAO is typically formed from the difference in sea-level pressure between the Azores and Iceland.
High index values correspond to stronger westerly flow across the North Atlantic, an intensification and northward shift of the storm track (Rogers, 1990) and warmer and wetter winters in northern Europe (Hurrell 1995). Covarying with the NAO there is an associated tripole pattern of sea surface temperature anomalies (Deser and Blackmon 1993). FIGURE 2.7 Characteristic pattern of anomalous sea level pressure (SLP; in hPa) associated with the positive polarity of the Arctic Oscillation (AO) in the winter. Blue indicates lower than normal SLP and red indicates higher than normal SLP; this phase of the AO exhibits an enhanced westerly jet over the Atlantic Ocean in the mid-latitudes. The North Atlantic Oscillation can be thought of as the portion of the AO pattern that resides in the Atlantic sector. SOURCE: Adapted from Thompson and Wallace (2000).The NAO is the single largest contributing pattern to European interannual variability and plays an important role in predictions of European winter climate.
However, the ability to predict the NAO on seasonal timescales is limited in current generation of models used for seasonal forecasting. There is some evidence that variability in the Atlantic Gulf Stream can influence the long-term variability of the NAO (Wu and Gordon, 2002). In addition, there is evidence of forcing of the NAO by ENSO (Bronniman et al., 2007; Ineson and Scaife 2009) and the stratospheric Quasi-Biennial Oscillation (Boer and Hamilton 2008). Stratosphere-Troposphere Interaction, Quasi-Biennial Oscillation (QBO)Since the stratosphere can interact with the troposphere, knowledge of the state of the stratosphere can serve almost as a boundary condition when attempting to simulate the troposphere. The stratospheric circulation can be highly variable, with a time scale much longer than that of the troposphere. The variability of the stratospheric circulation can be characterized mainly by the strength of the polar vortex, or equivalently the high latitude westerly winds.
Stratospheric variability peaks during Northern winter and Southern late spring. When the flow just above the tropopause is anomalous, the tropospheric flow tends to be disturbed in the same manner, with the anomalous tropospheric flow lasting up to about two months (Baldwin et al., 2003a, 2003b).
Generally, the surface pressure signature looks very much like the North Atlantic Oscillation or Northern Annular Mode. Surface temperature signals are also similar to those from the NAO and SAM and there are associated effects on extremes (Thompson et al., 2002).
In sensitive areas such as Europe in winter, experiments suggest that the influence of stratospheric variability on land surface temperatures can exceed the local effect of sea surface temperature.Sudden stratospheric warming events serve as an extreme example of how the stratosphere could serve as a source of predictability. During a sudden warming event the polar vortex abruptly (over the course of a few days) slows down, leading to an increase in polar stratospheric temperature of several tens of degrees Kelvin.
Although attenuated, over the course. Of the following weeks the warming signal migrates downward into the troposphere with signals that can be detected in the surface climate approximately a month following the warming event (Limpasuvan et al., 2004).Additionally, the Quasi-Biennial Oscillation (QBO) of the stratospheric circulation offers a source of predictability for the tropospheric climate. The stratospheric QBO in the tropics arises from the interaction of the stratospheric mean flow with eddy fluxes of momentum carried upward by Rossby and gravity waves that are excited by tropical convection.
The result is an oscillation in the stratospheric zonal winds having a period of about 26 months. While our weather and climate models do not often resolve or represent the QBO well, it is one of the more predictable features in the atmosphere, and it has been found to exhibit a signature in surface climate (Thompson et al., 2002). Tropical Instability Waves (TIWs) in the oceanTIWs are most prevalent in the eastern Pacific Ocean and are evident in SST and other quantities such as ocean surface chlorophyll and even boundary-layer cloudiness, particularly just north of the equator. They arise from shear-flow and baroclinic instabilities and result in westward propagating wave-like features having length scales on the order of 1000s of km and time scales of about 1–2 weeks.
There is evidence that they may affect the overlying atmosphere. (Hoffman et al., 2009). That the strong SST gradients associated with the TIWs affect the surface winds has been documented by Chelton et al. (2009) suggest that atmospheric models should improve the realism of their coupling between the atmosphere and ocean mesoscale variability in SST in order to correctly capture small scale variability in the wind field. States, even though this area is not involved in the dynamics underlying ENSO.
In essence, the northeast United States is a passive recipient of ENSO predictability through a global-scale teleconnection process.ENSO itself can be related to other patterns of variability. For example, westerly wind bursts associated with the MJO may help to trigger ENSO events (see the ENSO case study in ). Also, Yuan (2004) describes a teleconnection process between ENSO and the Antarctic Dipole, a separate climate mode. ENSO forcing triggers the Antarctic Dipole, with implications for sea ice prediction at seasonal timescales. Indian Ocean Dipole/Zonal Mode (IOD/IOZM)A coupled mode of interannual variability has been found in the equatorial Indian Ocean in which the normally positive SST gradient is significantly weakened or reversed for a period on the order of a season (Saji et al., 1999; Webster et al., 1999). It can result in significant regional climate impacts, such as in east Africa and southern Asia.
The independence of this mode and its connections to ENSO are still being investigated, but in any case the Indian Ocean Dipole/Zonal Mode (IOD/IOZM), like ENSO, appears to offer an intermittent source of interannual predictability. Similar to ENSO, the IOD/IOZM involves equatorial SST-wind-thermocline/upwelling feedback (Bjerknes, 1969); however, in contrast to ENSO, it also involves off-equatorial, SST-convection-atmospheric Rossby wave interaction (Li et al., 2003; Wang et al., 2003). The latter is strongly regulated by seasonal reversal of the monsoon circulation, hence the IOD/IOZM lasts only a season or two. The off-equatorial, SST-convection-Rossby wave interaction can maintain cooling of western North Pacific SST and anomalous anticyclonic circulation during the decaying phase of ENSO, providing a source of predictability for the East Asian summer monsoon (Wang et al., 2000). Greenhouse gases (CO 2, etc.)Greenhouse gases have a direct impact on the radiation balance of the atmosphere: increases in greenhouse gases warm the global climate. The non-stationarity associated with this climate change is an important component of climate forecasts even on ISI timescales.
For example, the NOAA Climate Prediction Center uses optimal climate normals and other empirical techniques to capture this non-stationarity in climate forecasts (Huang et al., 1996a; Livezey et al., 2007). However, regional details of this climate change are difficult to model numerically due to the myriad important feedbacks that need to be taken into account. These include feedbacks due to the enhancement of water vapor in the warming atmosphere and the associated changes in cloudiness and snow/ice amount, all of which can affect the radiation budget. In addition, there are feedbacks from the carbon cycle itself (including the release of additional greenhouse gases in northern latitudes as permafrost melts), the ocean thermohaline circulation, changes in the biosphere, and so on. Anthropogenic AerosolsAtmospheric aerosols, which affect the radiation budget of the Earth, include major human-related components that change with the nature of human activities, and thus which may be predictable.
The human-related components include sulfate aerosols from fossil fuel combustion and organic aerosols from biomass burning and land use change.The effect of changes in aerosols on precipitation at ISI timescales could be important. Bollasina and Nigam (2009) have shown that elevated aerosol concentrations over the Indian subcontinent can accompany periods of reduced cloudiness, increased downward shortwave radiation, and ultimately a delayed onset of the monsoon. However, the role of aerosols as the “cause” of a decrease or delay in precipitation is not yet confirmed—more research on sub-seasonal timescales is required to isolate the effect of aerosols from the influence of the large-scale synoptic flow and associated changes in precipitation. Fluctuations in solar outputThe sun provides the energy that powers the Earth’s climate system.
Its output varies slightly with an 11-year cycle that is highly predictable because it is nearly periodic. Larger changes may occur on longer time scales, but in the absence of measurements, these changes cannot be quantified beyond a statement that they appear to be small compared to the signal seen from greenhouse gases. As discussed by Haigh et al. (2005), it is likely that the mechanism that links solar fluctuations to surface climate involves the communication of anomalies between the stratosphere and troposphere, which is discussed in the “” section in this chapter. Gaps in Our KnowledgeOur understanding of ISI climate predictability—both of its sources and extent—is still far from complete. Numerous gaps still exist in our observations of climate processes and variability, in our inclusion of the wide range of relevant processes in models, and in our knowledge of the sources of predictability:.We cannot yet claim to have identified all of the reservoirs, linkages, and teleconnection patterns associated with predictability in the Earth system. For many of the predictability sources we have identified, we cannot claim to understand fully the mechanisms that underlie them.
The observational record contains many non-stationary trends that may relate to predictability but are not yet adequately explained. The science is proceeding but is encumbered by the overall complexity of the system.The models that have been used to evaluate the known sources of predictability and to make forecasts are known to be deficient in many ways. Many key processes associated with predictability occur at spatial scales that cannot be resolved by current models. Examples in the atmosphere include cumulus convection, boundary-layer turbulence, and cloud-aerosol microphysics; examples in the ocean include horizontal transports associated with eddies and vertical mixing. In addition, processes associated with the coupling of the ocean or the land surface to the atmosphere through the exchanges of heat, fresh water, and other constituents can be difficult to resolve.
The models thus rely on parameterizations, which are simple approximations that often have to be “tuned”, making them undependable in untested situations.
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