In Table 3 CR-BR indicates negative correlation between cosmic ray flux and background radiation i.e. positive correlation between cosmic ray intensity and cloudiness or/and aerosol content. This effect is considered as a manifestation of the *optical* mechanism of the solar-climate link. It is believed that the optical mechanism is caused by the influence of atmospheric ionization on production of new aerosol particles and cloud microphysics. Abbreviation CR-AC indicates a link between cosmic ray flux and atmospheric circulation. Despite the evidence for the reality of a link between cosmic rays and weather and climate the time-period of the experimental studies is rather short – usually no more than 30– 40 years. Even within this short time interval results are equivocal – e.g. some powerful flares were accompanied by increase of aerosol content in the atmosphere while some other flares did not show the effect. Extending data on low cloudiness (International Satellite Cloud Climatology Project – ISCCP) towards 2007 substantially reduces correlation between GCR and low-level clouds (see Fig. 15 in Gray et al., 2010). Moreover Roldugin and Tinsley (2004) reported that during 1978–1989 atmospheric transparency above 45 stations, situated at former USSR territory, decreased (not increased) during Forbush decreases, when sulfate aerosol loading to the stratosphere was high. However, Calgovic et al. (2010) find no response of global cloud cover to Forbush decreases at any altitude and latitude. Because of the lack of experimental data and some controversy in experimental results more information is needed before definite conclusions may be drawn. We can use the paleodata

from the University of Kansas in (central Greenland, GISP2 H core, 72°N, 38°W, height 3230 m) for this purpose. Both glaciochemical series cover the time period of 1576–1991 and have extremely high time resolution (approximately 20 samples per year) (Dreschhoff and Zeller, 1994). As we have noted above nitrate concentration in ice could serve as a proxy of atmosphere's ionization. The conductivity of ice is connected with its acidity and, hence,

in time with conductivity bursts ( Fig. 7). Ogurtsov (2011) analyzed such events in 1789, 1859, 1895, 1896, and 1908 and showed that the abrupt increase in the concentration of sulfate aerosols in the stratosphere due to additional ionization as a result from precipitation of solar cosmic ray energetic particles is one of the most probable factors that cause simultaneous origination of powerful peaks in the ice conductivity and nitrate concentration. Dreschhoff and Zeller (1994) have related the phenomenon to the direct effect of ionization on the formation of nacreous or polar stratospheric clouds (PSCs), which are largely composed of nitric acid (HNO3) droplets. Thus, coincidences of peaks in both

reflects concentration of the sulfate aerosol in the atmosphere. Some [NO3

physical state of the Earth's atmosphere have been obtained (Table 3).

on nitrate concentration [NO3

Available assessments of с differ by more than an order of magnitude (see Table 1). This is caused by the complexity of climatic system which has a lot of feedbacks. Many of these feedbacks are currently not well known. If we consider the estimations of IPCC (2007) we obtain that the increase of solar brightness in the 20th century could have resulted from corresponding global warming by 0.10-0.640 C. The difference between the lower and upper limits of the solar induced increase in the global temperature reaches a factor six. If we use the sensitivity evaluations not only of IPCC but also of other authors the difference will be even more. Such uncertainty does not allow us to draw any decisive conclusions about the role of the Sun in global warming. It may be significant as well as negligible. This result is in agreement with the IPCC (2007) considering current level of scientific understanding of TSIclimate link as low.

Variation in ultra-violet (UV) solar radiation, which effectively influences ozone layer, is another way to provide a link between solar luminosity and climate. The amplitude of the 11-year variation in UV radiation reaches several percent (ca. 6% at 20 nm). Ozone is the main gas involved in the radiative heating of the stratosphere. Solar-induced changes in ozone can therefore affect the radiative balance of the stratosphere with indirect effects on the troposphere and global circulation pattern. Possible reaction of climatic system to the UV solar variability was analyzed by Haigh (1996), Shindell et al. (1999), Gray et al. (2010).

#### **5. Cosmic ray flux and terrestrial climate**

The Sun can influence terrestrial weather and climate not only directly through variations of TSI but indirectly – via modulation of corpuscular radiation entering atmosphere. As we have noted above, the idea that cosmic ray flux could directly influence the weather was proposed by Ney (1959) and developed by Dickinson (1975). However the total energy input of the solar-modulated particles is very small as compared to the energy of atmospheric processes – see Table 2 (the data were taken from Vitinsky et al., 1986; Pudovkin, 1996 and Borisenkov, 1977).

Table 2. Characteristic energy of the terrestrial manifestations of solar activity and atmospheric processes.

It is evident from Table 2 that the energy coming into terrestrial magnetosphere and the atmosphere is a few orders of magnitude less than the energy of meteorological processes. Energy input from the very large solar flare of August 1972 is also small – ca. 1024 erg. For this reason a linear relationship between cosmic rays and the Earth's climate seems very

caused by the complexity of climatic system which has a lot of feedbacks. Many of these feedbacks are currently not well known. If we consider the estimations of IPCC (2007) we obtain that the increase of solar brightness in the 20th century could have resulted from corresponding global warming by 0.10-0.640 C. The difference between the lower and upper limits of the solar induced increase in the global temperature reaches a factor six. If we use the sensitivity evaluations not only of IPCC but also of other authors the difference will be even more. Such uncertainty does not allow us to draw any decisive conclusions about the role of the Sun in global warming. It may be significant as well as negligible. This result is in agreement with the IPCC (2007) considering current level of scientific understanding of TSI-

Variation in ultra-violet (UV) solar radiation, which effectively influences ozone layer, is another way to provide a link between solar luminosity and climate. The amplitude of the 11-year variation in UV radiation reaches several percent (ca. 6% at 20 nm). Ozone is the main gas involved in the radiative heating of the stratosphere. Solar-induced changes in ozone can therefore affect the radiative balance of the stratosphere with indirect effects on the troposphere and global circulation pattern. Possible reaction of climatic system to the UV solar variability was analyzed by Haigh (1996), Shindell et al. (1999), Gray et al. (2010).

The Sun can influence terrestrial weather and climate not only directly through variations of TSI but indirectly – via modulation of corpuscular radiation entering atmosphere. As we have noted above, the idea that cosmic ray flux could directly influence the weather was proposed by Ney (1959) and developed by Dickinson (1975). However the total energy input of the solar-modulated particles is very small as compared to the energy of atmospheric processes – see Table 2 (the data were taken from Vitinsky et al., 1986; Pudovkin, 1996 and

Energy brought in the Earth's atmosphere by TSI 1.6×1029 Energy brought in the Earth's magnetosphere by solar wind up to 3×1023

during magnetic storms up to 4×1024

It is evident from Table 2 that the energy coming into terrestrial magnetosphere and the atmosphere is a few orders of magnitude less than the energy of meteorological processes. Energy input from the very large solar flare of August 1972 is also small – ca. 1024 erg. For this reason a linear relationship between cosmic rays and the Earth's climate seems very

GeV ~1022 Energy brought in the Earth's atmosphere by the water vapor (latent heat) 4×1028 Energy release by thunder clouds up to 1027 Power of the cyclone (anticyclone) up to 1025

Table 2. Characteristic energy of the terrestrial manifestations of solar activity and

Energy brought in the Earth's magnetosphere by the fast solar wind

Energy brought in the Earth's atmosphere by the flux of GCR with Е>0.1

с differ by more than an order of magnitude (see Table 1). This is

Type of energy Power

(erg/day)

Available assessments of

climate link as low.

Borisenkov, 1977).

atmospheric processes.

**5. Cosmic ray flux and terrestrial climate**

unlikely. However, the effect of cosmic radiation on the atmosphere could be very nonlinear. For example, minor changes in the physical and chemical parameters of the atmosphere, which does not require a lot of energy, could appreciably change its optical properties, disturb the energy balance and stimulate powerful atmospheric processes. Indeed, Pudovkin and Veretenenko (1992) have studied changes in the atmospheric transparency following the geomagnetic disturbances, caused by the fluxes of solar and galactic cosmic particles. They showed that corresponding changes in energy balance of the lower atmosphere can reach 1.5×1026 erg/day. Starkov and Roldugin (1995) have obtained even a larger value – ca. 1027 erg/day. These estimations testify that cosmic radiation in fact can influence terrestrial weather. Since the intensity of cosmic rays is driven by solar activity the proposed mechanism of indirect solar-climate link appears quite plausible. A lot of experimental evidences of the reality of a link between the fluxes of cosmic particles and the physical state of the Earth's atmosphere have been obtained (Table 3).

In Table 3 CR-BR indicates negative correlation between cosmic ray flux and background radiation i.e. positive correlation between cosmic ray intensity and cloudiness or/and aerosol content. This effect is considered as a manifestation of the *optical* mechanism of the solar-climate link. It is believed that the optical mechanism is caused by the influence of atmospheric ionization on production of new aerosol particles and cloud microphysics. Abbreviation CR-AC indicates a link between cosmic ray flux and atmospheric circulation.

Despite the evidence for the reality of a link between cosmic rays and weather and climate the time-period of the experimental studies is rather short – usually no more than 30– 40 years. Even within this short time interval results are equivocal – e.g. some powerful flares were accompanied by increase of aerosol content in the atmosphere while some other flares did not show the effect. Extending data on low cloudiness (International Satellite Cloud Climatology Project – ISCCP) towards 2007 substantially reduces correlation between GCR and low-level clouds (see Fig. 15 in Gray et al., 2010). Moreover Roldugin and Tinsley (2004) reported that during 1978–1989 atmospheric transparency above 45 stations, situated at former USSR territory, decreased (not increased) during Forbush decreases, when sulfate aerosol loading to the stratosphere was high. However, Calgovic et al. (2010) find no response of global cloud cover to Forbush decreases at any altitude and latitude. Because of the lack of experimental data and some controversy in experimental results more information is needed before definite conclusions may be drawn. We can use the paleodata on nitrate concentration [NO3 -] and the conductivity of an ice core extracted by specialists from the University of Kansas in (central Greenland, GISP2 H core, 72°N, 38°W, height 3230 m) for this purpose. Both glaciochemical series cover the time period of 1576–1991 and have extremely high time resolution (approximately 20 samples per year) (Dreschhoff and Zeller, 1994). As we have noted above nitrate concentration in ice could serve as a proxy of atmosphere's ionization. The conductivity of ice is connected with its acidity and, hence, reflects concentration of the sulfate aerosol in the atmosphere. Some [NO3 -] peaks coincide in time with conductivity bursts ( Fig. 7). Ogurtsov (2011) analyzed such events in 1789, 1859, 1895, 1896, and 1908 and showed that the abrupt increase in the concentration of sulfate aerosols in the stratosphere due to additional ionization as a result from precipitation of solar cosmic ray energetic particles is one of the most probable factors that cause simultaneous origination of powerful peaks in the ice conductivity and nitrate concentration. Dreschhoff and Zeller (1994) have related the phenomenon to the direct effect of ionization on the formation of nacreous or polar stratospheric clouds (PSCs), which are largely composed of nitric acid (HNO3) droplets. Thus, coincidences of peaks in both

Table 3. Experimental evidences for a link between solar modulated cosmic radiation and physical state of the Earth's atmosphere. Abbreviation A means data of actinometrical stations, M – data of meteorological stations, L – data of lidar observations, R– radiosonde data, S – satellite data.

Solar Activity, Space Weather and the Earth's Climate 59

studied glaciochemical series are manifestations of the effect that has been experimentally registered with lidar and satellite equipment for the last 25 years. This proves that the relationship between the aerosol concentration and the ionization rate in the stratosphere is real and makes it possible to extrend the interval where this connection exists for more than 200 years. The result provides new evidence of the reality of optical mechanism of the Sun's

3080 3100 3120 3140 3160 3180 3200

1896 1895

2480 2520 2560 2600

2480 2520 2560 2600

2200 2240 2280 2320 2360

1908

2200 2240 2280 2320 2360

Fig. 7. Concentration of ions (thin black line) and conductivity (thick grey line): (a) in 1859 (the Carrington optical flare), (b) at the end of the 19th century, and (c) in 1908 (the

Optical mechanism could also provide solar-climate relationship over longer time scale. The effect of unphysical time lag between long-term variation in global temperature and Wolf number can also be explained in the framework of this mechanism. It has been established that long-term (T>30 yr) variation in global temperature resemble the variation in sunspot number since the mid-19th century which might be attributed to a solar-climatic connection (see Fig. 8). However, smoothed temperature outstrips solar activity that violates the causeeffect relationship. Friis-Christensen and Lassen (1991) have shown that this phenomenon could be explained by using solar cycle length (SCL) as an indicator of solar activity instead of sunspot numbers - SCL lead sunspot number by 1– 3 cycles. However, physical processes responsible for this link have not been established so far, which calls for the search for

Conductivity 10-2

\*S/cm

160

Sample No

240

320

3080 3100 3120 3140 3160 3180 3200

1859

A

B

C

Nitrate concentration, relative units

Tunguska event). 100 relative units = 90.3 ng/g NO3−.

alternative explanations.

influence on weather.

Table 3. Experimental evidences for a link between solar modulated cosmic radiation and physical state of the Earth's atmosphere. Abbreviation A means data of actinometrical stations, M – data of meteorological stations, L – data of lidar observations, R– radiosonde

data, S – satellite data.

studied glaciochemical series are manifestations of the effect that has been experimentally registered with lidar and satellite equipment for the last 25 years. This proves that the relationship between the aerosol concentration and the ionization rate in the stratosphere is real and makes it possible to extrend the interval where this connection exists for more than 200 years. The result provides new evidence of the reality of optical mechanism of the Sun's influence on weather.

Fig. 7. Concentration of ions (thin black line) and conductivity (thick grey line): (a) in 1859 (the Carrington optical flare), (b) at the end of the 19th century, and (c) in 1908 (the Tunguska event). 100 relative units = 90.3 ng/g NO3−.

Optical mechanism could also provide solar-climate relationship over longer time scale. The effect of unphysical time lag between long-term variation in global temperature and Wolf number can also be explained in the framework of this mechanism. It has been established that long-term (T>30 yr) variation in global temperature resemble the variation in sunspot number since the mid-19th century which might be attributed to a solar-climatic connection (see Fig. 8). However, smoothed temperature outstrips solar activity that violates the causeeffect relationship. Friis-Christensen and Lassen (1991) have shown that this phenomenon could be explained by using solar cycle length (SCL) as an indicator of solar activity instead of sunspot numbers - SCL lead sunspot number by 1– 3 cycles. However, physical processes responsible for this link have not been established so far, which calls for the search for alternative explanations.

Solar Activity, Space Weather and the Earth's Climate 61

stratospheric optical depth. The advancing development of century-long cyclicity in temperature is a result of the following chain of physical processes: solar activity fluxes of ionizing particles (SCR and GCR) ionization number of ultra-fine particles in stratosphere concentration of stratospheric aerosol atmospheric transparency background solar radiation surface temperature. Thus the effect of positive time shift between century-long variations in temperature and sunspot number could be explained in the framework of optical mechanism of solar-climate link. The most serious problem of the proposed mechanism is the lack of knowledge about the physical processes responsible for a link between ionization and the number of condensation nuclei (CN) and cloud condensation nuclei (CCN). Ion-induced particle formation is a plausible mechanism to provide this link. New aerosols initially appear as small (<2 nm) clusters containing a few molecules. These clusters either may grow by further condensation of nearby gas molecules or evaporate. Above a certain critical size, the cluster is thermodynamically stable and its probability to grow by further condensation is larger than the probability to break up by evaporation. Atmospheric ions enhance the process of clustering of condensable vapor. The presence of a charge stabilizes the embryonic cluster through Coulomb attraction, and reduces the critical size (Yu and Turco, 2000, 2001). The term *ion-induced nucleation* (IIN) describes the formation of new ultra-fine particles from the gas phase, in which ions take a part. The theory of this process was developed in the works of Arnold (1982, 2006), Kazil and Lovejoy (2004) and Yu (2006). It has been shown that although INN is connected directly with ionization, it depends also on the physical parameters of the atmosphere, particularly on the concentration of sulfuric acid vapor [H2SO4] and temperature. High values of [H2SO4] (about 107 cm-3 or more) and low (<-500 C) temperature are necessary for effective ion-induced generation of ultra-fine aerosol particles. Therefore solar activity affects the atmosphere in the framework of INN mechanism not directly but its influence is mediated by a few internal terrestrial phenomena – volcanic activity, meteorology etc. Such a complex solar-atmosphere link might be the cause of instability of solar-climatic

The charge also accelerates the early growth process, due to an enhanced collision probability. As a result the charge appreciably helps the sub-critical embryos to survive and grow to CN and CCN sizes. Thus, despite the IIN mechanism is believed to work mainly under clear sky conditions, it can also communicate solar activity to cloudiness. Experimental evidence for IIN have been obtained by Eichkorn et al. (2002); Lee et al. (2003) and Svensmark et al. (2007). It was shown that changes in the global atmospheric circuit can influence cloud microphysics (Tinsley, 2000). The global circuit causes a vertical current density in fair (non-thunderstorm) weather regions. This ionospheresurface fair weather current density passes through clouds causing local droplet and aerosol charging at their boundaries, where sharp gradients in air conductivity can occur. Modulation of the global circuit by solar-induced changes in atmospheric ionization (both GCR and SCR effect) provides an alternative plausible route by which solar changes can be transmitted to the lower atmosphere (Tinsley, 2000, 2008). Three potential near-cloud mechanisms of the influence of the conduction current density on clouds have been

a. *electroscavenging* is a result of electrically-enhanced efficiency of the collision between

b. *Electrofreezing* is connected to the possible influence of electric properties of aerosol on the rate of freezing of thermodynamically unstable super-cooled water droplets (Tinsley

charged particles with liquid droplets (e.g. Tinsley et al., 2008).

correlations.

proposed:

and Dean, 1991).

Fig. 8. A – gray curve - Wolf numbers smoothed by the Furier-filter (frequencies above 0.04 years-1 are suppressed). Black curve - annual temperature of Northern hemisphere (Jones et al., 2001) smoothed by 15 years. B – gray curve - the length of a solar cycle smoothed on three points. Black curve - the smoothed annual temperature of Northern hemisphere.

Ogurtsov et al. (2004) have revealed a negative correlation between century-type variations in NO3 – concentration in Greenland ice and sunspot number. This study found that the Gleissberg cycle in nitrate leads the corresponding variation in sunspot number by 15–18 years during the last 3 centuries. Since [NO3-] in polar ice is connected with the stratosphere's ionization it is reasonable to assume that the century-long cycle in stratospheric ionization advances the corresponding cycle in solar activity. Experimental estimation of the aerosol optical depth of the atmosphere, performed by Bryson and Goodman (1980) using data of 42 actinometrical stations situated within 250–650 N belt, showed the presence of distinct multidecadal variation (Fig. 9). Long-term changes of atmospheric transparency are generally the result of the corresponding change in the optical depth of the stratosphere. The century-type periodicity in the stratospheric aerosol transparency correlates negatively with the corresponding cycle in sunspots and lead it by 20-25 yrs (Figure 9B). Aerosol in the stratosphere is basically sulfate with a composition of about 75% H2SO4 and 25% H2O. Volcanoes are the main source of SO2 in stratosphere, from which H2SO4 is generated. Long-term variation in volcanic activity lags behind aerosol transparency instead of advancing it (Fig. 9C). Volcanic SO2 loading into the stratosphere estimated by Kondratyev (1999) does not correlate well with the aerosol optical depth (Fig. 9D). This means that long-term variation of the sulfate aerosol abundance in the stratosphere is influenced not only by volcanic SO2 input but also by one additional factor. Ogurtsov (2007) has hypothesized that this factor might be a century-long cycle in stratospheric ionization, which, in turn, is caused by superposition of the century-scale variations in GCR and SCR intensity. I.e. long-term variation of aerosol optical depth of stratosphere is driven by corresponding change in solar-cosmic corpuscular radiation, which produces century-scale cycle in stratospheric ionization advancing the Gleissberg periodicity in sunspot number. The estimation by Ogurtsov (2007) using energy-balance model showed that the observed variation in the atmosphere's transparency during the 20th century is enough to provide the global temperature change. Thus, according to Ogurtsov (2007) the positive time lag between long-term variations in surface temperature and Wolf number could not be linked to the change in solar cycle length but to the variation in

1880 1900 1920 1940 1960 1980

12 yrs

12-15 yrs

1880 1900 1920 1940 1960 1980

Fig. 8. A – gray curve - Wolf numbers smoothed by the Furier-filter (frequencies above 0.04 years-1 are suppressed). Black curve - annual temperature of Northern hemisphere (Jones et al., 2001) smoothed by 15 years. B – gray curve - the length of a solar cycle smoothed on three points. Black curve - the smoothed annual temperature of Northern hemisphere.

Ogurtsov et al. (2004) have revealed a negative correlation between century-type variations

Gleissberg cycle in nitrate leads the corresponding variation in sunspot number by 15–18 years during the last 3 centuries. Since [NO3-] in polar ice is connected with the stratosphere's ionization it is reasonable to assume that the century-long cycle in stratospheric ionization advances the corresponding cycle in solar activity. Experimental estimation of the aerosol optical depth of the atmosphere, performed by Bryson and Goodman (1980) using data of 42 actinometrical stations situated within 250–650 N belt, showed the presence of distinct multidecadal variation (Fig. 9). Long-term changes of atmospheric transparency are generally the result of the corresponding change in the optical depth of the stratosphere. The century-type periodicity in the stratospheric aerosol transparency correlates negatively with the corresponding cycle in sunspots and lead it by 20-25 yrs (Figure 9B). Aerosol in the stratosphere is basically sulfate with a composition of about 75% H2SO4 and 25% H2O. Volcanoes are the main source of SO2 in stratosphere, from which H2SO4 is generated. Long-term variation in volcanic activity lags behind aerosol transparency instead of advancing it (Fig. 9C). Volcanic SO2 loading into the stratosphere estimated by Kondratyev (1999) does not correlate well with the aerosol optical depth (Fig. 9D). This means that long-term variation of the sulfate aerosol abundance in the stratosphere is influenced not only by volcanic SO2 input but also by one additional factor. Ogurtsov (2007) has hypothesized that this factor might be a century-long cycle in stratospheric ionization, which, in turn, is caused by superposition of the century-scale variations in GCR and SCR intensity. I.e. long-term variation of aerosol optical depth of stratosphere is driven by corresponding change in solar-cosmic corpuscular radiation, which produces century-scale cycle in stratospheric ionization advancing the Gleissberg periodicity in sunspot number. The estimation by Ogurtsov (2007) using energy-balance model showed that the observed variation in the atmosphere's transparency during the 20th century is enough to provide the global temperature change. Thus, according to Ogurtsov (2007) the positive time lag between long-term variations in surface temperature and Wolf number could not be linked to the change in solar cycle length but to the variation in

concentration in Greenland ice and sunspot number. This study found that the

0,0

0,2

in NO3 – T0

C

B

А

0,0

0,2

11,5 11,0 10,5 10,0 9,5

Solar cycle length

Wolf number

Years

40

60

80

stratospheric optical depth. The advancing development of century-long cyclicity in temperature is a result of the following chain of physical processes: solar activity fluxes of ionizing particles (SCR and GCR) ionization number of ultra-fine particles in stratosphere concentration of stratospheric aerosol atmospheric transparency background solar radiation surface temperature. Thus the effect of positive time shift between century-long variations in temperature and sunspot number could be explained in the framework of optical mechanism of solar-climate link. The most serious problem of the proposed mechanism is the lack of knowledge about the physical processes responsible for a link between ionization and the number of condensation nuclei (CN) and cloud condensation nuclei (CCN). Ion-induced particle formation is a plausible mechanism to provide this link. New aerosols initially appear as small (<2 nm) clusters containing a few molecules. These clusters either may grow by further condensation of nearby gas molecules or evaporate. Above a certain critical size, the cluster is thermodynamically stable and its probability to grow by further condensation is larger than the probability to break up by evaporation. Atmospheric ions enhance the process of clustering of condensable vapor. The presence of a charge stabilizes the embryonic cluster through Coulomb attraction, and reduces the critical size (Yu and Turco, 2000, 2001). The term *ion-induced nucleation* (IIN) describes the formation of new ultra-fine particles from the gas phase, in which ions take a part. The theory of this process was developed in the works of Arnold (1982, 2006), Kazil and Lovejoy (2004) and Yu (2006). It has been shown that although INN is connected directly with ionization, it depends also on the physical parameters of the atmosphere, particularly on the concentration of sulfuric acid vapor [H2SO4] and temperature. High values of [H2SO4] (about 107 cm-3 or more) and low (<-500 C) temperature are necessary for effective ion-induced generation of ultra-fine aerosol particles. Therefore solar activity affects the atmosphere in the framework of INN mechanism not directly but its influence is mediated by a few internal terrestrial phenomena – volcanic activity, meteorology etc. Such a complex solar-atmosphere link might be the cause of instability of solar-climatic correlations.

The charge also accelerates the early growth process, due to an enhanced collision probability. As a result the charge appreciably helps the sub-critical embryos to survive and grow to CN and CCN sizes. Thus, despite the IIN mechanism is believed to work mainly under clear sky conditions, it can also communicate solar activity to cloudiness. Experimental evidence for IIN have been obtained by Eichkorn et al. (2002); Lee et al. (2003) and Svensmark et al. (2007). It was shown that changes in the global atmospheric circuit can influence cloud microphysics (Tinsley, 2000). The global circuit causes a vertical current density in fair (non-thunderstorm) weather regions. This ionospheresurface fair weather current density passes through clouds causing local droplet and aerosol charging at their boundaries, where sharp gradients in air conductivity can occur. Modulation of the global circuit by solar-induced changes in atmospheric ionization (both GCR and SCR effect) provides an alternative plausible route by which solar changes can be transmitted to the lower atmosphere (Tinsley, 2000, 2008). Three potential near-cloud mechanisms of the influence of the conduction current density on clouds have been proposed:

Solar Activity, Space Weather and the Earth's Climate 63

They assumed that microwave radiation of the Earth ionosphere during solar flares and geomagnetic storms might influence the condensation processes in the troposphere and thus

Our understanding of possible physical mechanisms of influence of the flux of solar-cosmic corpuscles on the atmosphere of the Earth and influence of solar and geomagnetic activity

The last few decades were marked by considerable successes in helioclimatology. Both satellite and ground-based observations have brought a lot of new evidence for a link between solar activity and the phenomena of the lower atmosphere. The progress in theory, experiment and modeling has also significantly increased our knowledge of the Sun and solar-terrestrial connections. Nevertheless, the absolutely conclusive proof of the reality of the solar-climate link is still missing. The lack of facts and understanding about the connecting processes at work is the main cause of this shortcoming. Data obtained by experimental observation are quite precise but rather short in time scale. Paleoproxies are much longer but their uncertainty generally increases as a function of time from the present.

on weather and climate has considerably improved during the last 2-3 decades.

Thus, in solar-climatic research we face a kind of "uncertainty principle":

*X T*

Moreover, it has been shown that the Sun-climate connection, even if it actually exists, may be realized by an indirect and nonlinear way. As a result the search for a link between solar activity and weather and climate has turned out to be a more difficult task than was previously anticipated. Quite possibly further future research in helioclimatology will

1. Further gathering and accumulation of the information together with its subsequent systematization. This concerns both instrumental monitoring of solar-cosmic and geophysical parameters and work on constructing new paleoreconstructions of solar activity and climate. The development of methods of paleoastrophysics and

2. The improvement of the methods of statistical analysis, particularly, methods aimed at the search and detection of nonlinear interrelations between the different time series. 3. Further laboratory research of the physical processes which possibly provide a link

4. Increasing our understanding about the climatic system and improvements of methods

M. G. Ogurtsov expresses his thanks to the exchange program between the Russian and Finnish Academies (project No. 16), to the program of the Saint-Petersburg Scientific Center of RAS for 2011, and to RFBR grants No. 09-02-00083, 10-05-00129, 11-02-00755 for financial support. R. Jalkanen and M. Lindholm want to thank Academy of Finland for its financial

where ∆*X* is the uncertainty of the data, and ∆*T* is the length of the *X* time series.

const, (7)

**6. Conclusion and prospects for further research**

follow the guidelines envisaged below:

of climatic modeling.

**7. Acknowledgment**

support (SA 138937).

paleoclimatology is an integral part of this work.

between solar activity and the low atmosphere.

influence the weather.

Fig. 9. A – aerosol optical thickness of a middle latitude (250-450) stratosphere after Bryson and Goodman (1980) annually interpolated. Thin curve - raw data, thick curve - data smoothed by 11 years; B – gray curve - Wolf number smoothed by the Fourier filter (frequencies above 0.04 years-1 are suppressed), black curve - aerosol optical thickness of a stratosphere, smoothed by 11 years; C – gray curve - volcanic explosive index after Briffa et al. (1998) smoothed by 25 years. Black curve – smoothed aerosol transparency; D – gray columns – SO2 loading to stratosphere after Kondratyev (1999).

**c.** *Electrocoalescence* is connected to the possible increase in coalescence between charged droplets, which could influence droplet size or number (Harrison and Ambaum 2009).

As we have noted above, all the mechanisms work at the boundaries of super-cooled and liquid clouds. Direct experimental evidence for a link between atmospheric electricity and cloud microphysics were obtained by Harrison and Ambaum (2009) as well as Nicoll and Harrison (2009). It should be considered also that the global circuit is dependent on atmospheric column resistance, which, in turn is influenced not only by ionization but also by concentration of sulfate aerosol in the stratosphere, concentration of cosmic dust in upper atmosphere and, probably other factors. The contribution of these factors might obscure solar impact on the vertical current density.

A new mechanism for the connection between solar-modulated corpuscular radiation and physical processes in the lower atmosphere was proposed by Avakyan and Voronin (2010).

1880 1900 1920 1940 1960 1980

1880 1900 1920 1940 1960 1980

Fig. 9. A – aerosol optical thickness of a middle latitude (250-450) stratosphere after Bryson and Goodman (1980) annually interpolated. Thin curve - raw data, thick curve - data smoothed by 11 years; B – gray curve - Wolf number smoothed by the Fourier filter (frequencies above 0.04 years-1 are suppressed), black curve - aerosol optical thickness of a stratosphere, smoothed by 11 years; C – gray curve - volcanic explosive index after Briffa et al. (1998) smoothed by 25 years. Black curve – smoothed aerosol transparency; D – gray

**c.** *Electrocoalescence* is connected to the possible increase in coalescence between charged droplets, which could influence droplet size or number (Harrison and Ambaum 2009). As we have noted above, all the mechanisms work at the boundaries of super-cooled and liquid clouds. Direct experimental evidence for a link between atmospheric electricity and cloud microphysics were obtained by Harrison and Ambaum (2009) as well as Nicoll and Harrison (2009). It should be considered also that the global circuit is dependent on atmospheric column resistance, which, in turn is influenced not only by ionization but also by concentration of sulfate aerosol in the stratosphere, concentration of cosmic dust in upper atmosphere and, probably other factors. The contribution of these factors might obscure

A new mechanism for the connection between solar-modulated corpuscular radiation and physical processes in the lower atmosphere was proposed by Avakyan and Voronin (2010).

20 D

0,02

0,00

Aerosol optical thickness

of the atmosphere

solar impact on the vertical current density.

0,00

0,02 -0,015 0,000 0,015 -0,02

0,00

0,03

А

B ~8 yrs

C

columns – SO2 loading to stratosphere after Kondratyev (1999).

0,3 0,6 0,9

10

Years.

75

~20 yrs

number

Wolf

Earth's albedo

SO2

Volcanic

activity

index

,

1012 g

50

0,312 0,313 0,314 They assumed that microwave radiation of the Earth ionosphere during solar flares and geomagnetic storms might influence the condensation processes in the troposphere and thus influence the weather.

Our understanding of possible physical mechanisms of influence of the flux of solar-cosmic corpuscles on the atmosphere of the Earth and influence of solar and geomagnetic activity on weather and climate has considerably improved during the last 2-3 decades.

#### **6. Conclusion and prospects for further research**

The last few decades were marked by considerable successes in helioclimatology. Both satellite and ground-based observations have brought a lot of new evidence for a link between solar activity and the phenomena of the lower atmosphere. The progress in theory, experiment and modeling has also significantly increased our knowledge of the Sun and solar-terrestrial connections. Nevertheless, the absolutely conclusive proof of the reality of the solar-climate link is still missing. The lack of facts and understanding about the connecting processes at work is the main cause of this shortcoming. Data obtained by experimental observation are quite precise but rather short in time scale. Paleoproxies are much longer but their uncertainty generally increases as a function of time from the present. Thus, in solar-climatic research we face a kind of "uncertainty principle":

$$\frac{\Delta X}{\Delta T} \approx \text{const.}\tag{7}$$

where ∆*X* is the uncertainty of the data, and ∆*T* is the length of the *X* time series.

Moreover, it has been shown that the Sun-climate connection, even if it actually exists, may be realized by an indirect and nonlinear way. As a result the search for a link between solar activity and weather and climate has turned out to be a more difficult task than was previously anticipated. Quite possibly further future research in helioclimatology will follow the guidelines envisaged below:

#### **7. Acknowledgment**

M. G. Ogurtsov expresses his thanks to the exchange program between the Russian and Finnish Academies (project No. 16), to the program of the Saint-Petersburg Scientific Center of RAS for 2011, and to RFBR grants No. 09-02-00083, 10-05-00129, 11-02-00755 for financial support. R. Jalkanen and M. Lindholm want to thank Academy of Finland for its financial support (SA 138937).

Solar Activity, Space Weather and the Earth's Climate 65

Chylek, P. and Lohmann, U. (2008). Aerosol Radiative Forcing and Climate Sensitivity

*Research Letters*, Vol.35, L23703, doi:10.1029/2008GL033888, ISSN 0094-8276 Cook, E.R. & Kairiukstis, L.A. (1989). *Methods of Dendrochronology: Applications in the*

Crowley, T.J. & Lowery, T.S. (2000). How Warm Was the Medieval Warm Period? *Ambio*,

Dansgaard, W. (1954). The O18-abundance in Fresh Water. *Geochimica et Cosmochimica Acta*,

De Jager, C. (2005). Solar Forcing of Climate: Solar Variability. *Space Science Reviews*, Vol.120,

Dergachev, V.A.. (1994). Radiocarbon chronometer. *Priroda*, No. 1, pp. 3-15, ISSN (in

Dickinson, E. (1975). Solar Variability and the Lower Atmosphere. *Bulletin of American*

Dreschhoff, G.A.M.; Zeller, E.J. & Parker, B.C. (1983). Past Solar Activity Variation Reflected

Dreschoff, G.A.M. & Zeller, E.J. (1994). 415-year Greenland Ice Core Record of Solar Proton

Wakeffield, (Ed.), 1-24, Nebraska Academy of Sciences, Lincoln, Nebraska Dreschoff, G.A.M. & Zeller, E.J. (1998). Ultra-high Resolution Nitrate in Polar Ice as Indicator of Past Solar Activity. *Solar Physics*, Vol. 177, pp. 365-374, ISSN 0038-0938 Douglass, A. E. (1914). A Method of Estimatig Rainfall by the Growth of Trees. In,: *The*

Dobrovolny, P.; Moberg, A.; Brazdil, R.; Pfister, C.; Glaser, R.; Wilson, R.; van Engelen, A.;

Eddy, A. (1976). The Maunder Minimum. *Science*, Vol. 192, No.4245, pp.1189-1202, ISSN

Eichkorn, S.; Wilhelm, S.; Aufmhoff, H.; Wohlfrom, K. H. & Arnold, F. (2002). Cosmic Ray-

*Letters*, Vol. 29, No.14, 1698, DOI: 10.1029/2002GL015044, ISSN 0094-8276 Esper, J.; Cook, E.R. & Schweingruber, F.H. (2002). Low-Frequency Signals in Long Tree-

Farquhar, G.D.; O'Leary, M.H. & Berry, J.A. (1982). On the relationship between carbon

*Australian Journal of Plant Physiology*, Vol.9, pp.121-137, ISSN 0310-7841

1500. *Climatic Change*, Vol.101, No.1-2, pp. 69-107, ISSN 1573-1480

295, No.5563 , pp. 2250-2253, ISSN 1095-9203

in Nitrate Concentration in Antarctic Ice. In: *Weather and climate response to solar variations*, B.M. Mc Cormac, (Ed.), 225-236, Colorado Associated University Press,

Events Dated by Volcanic Eruptive Episodes. In*: TER-QUA Symposium Series 2*, D.

*climatic factor*, E. Huntington, (Ed.), 101-122, Carnegie Institute Washington

Limanowka, D.; Kiss, A.; Halickova, M.; Mackova, J.; Luterbacher, J. & Bohm, R. (2010). Monthly, Seasonal and Annual Temperature Reconstructions for Central Europe Derived from Documentary Evidence and Instrumental Records Since AD

Induced Aerosol-Formation: First Observational Evidence from Aircraft-Based Ion Mass Spectrometer Measurements in the Upper Troposphere. *Geophysical Research*

ring Chronologies for Reconstructing Past Temperature Variability. *Science*, Vol.

isotope discrimination and the intercellular carbon dioxide concentration in leaves.

*Environmental Sciences*, Kluwer Academic Publishers, Dordrecht

*Meteorological Society*, Vol.56, pp.1240-1248, ISSN 1520-0477

0094-8276

Russian).

Boulder

Publications.

1095-9203

Vol. 29, pp. 51-54, ISSN 0044-7447

pp.197–241, ISSN 0038-6308

Vol. 6, No. 5-6, pp. 241-260, ISSN 0016-7037

*Journal of Geophysical Research*, Vol.112, D24S04, doi:10.1029/2007JD008740, ISSN

Deduced From the Last Glacial Maximum to Holocene Transition, *Geophysical*

#### **8. References**

Andronova, N.G. & Schlesinger, M.E. (2000). Causes of Global Temperature Changes

Arnold, F. (1982). Ion Nucleation – a Potential Source for Stratospheric Aerosols. *Nature*, Vol.

Arnold, F. (2006). Atmospheric Aerosol and Cloud Condensation Nuclei Formation: A

Avakyan, S. V. & Voronin, N. A. (2010). Radio-Optical and Optical Mechanisms of the

Bard, E.; Raisbeck, G.; Yiou, F. & Jouzel, J. (2000). Solar Irradiance During the Last 1200

Bazilevskaya, G.A.; Usoskin, I.G.; Fluckiger, E.O.; Harrison, R.G.; Desorgher, L.; Butikofer,

Beer, J.; Mende, W. & Stellmacher, R. (2000). The Role of the Sun in Climate Forcing,

Beltrami, H. (2002). Climate from Borehole Data: Energy Fluxes and Temperatures Since

Bond, G.; Kromer, B.; Beer, J.; Muscheler, R.; Evans, M.; Showers, W.; Hoffmann, S.; Lotti-

Borisenkov, E.P. (1977). Development of Fuel and Energy Base and its Influence on Weather

Briffa, K.R.; Jones, P.D.; Schweingruber, F.H. & Osborn, T.J. (1998). Influence of Volcanic

Briffa, K.R. (2000). Annual Climate Variability in the Holocene: Interpreting the Message of Ancient Trees. *Quaternary Science Reviews*, Vol. 19, pp. 87-105, ISSN 0277-3791 Briffa, K.R. & Osborn, T.J. (2002). Blowing Hot and Cold. *Science,* No.5563 , Vol.295, pp.

Bryson, R.A. & Goodman, B.M. (1980). Volcanic Activity and Climatic Changes. *Science*,

Calogovic, J.; Albert, C.; Arnold F.; Beer J.; Desorgher L. & Flueckiger, E. O. (2010), Sudden

Chylek, P.; Lohmann U.; Dubey M.; Mishchenko M.; Kahn, R. & Ohmura, A. (2007). Limits

cosmic ray decreases: No change of global cloud cover. *Geophysical Research Letters*,

on Climate Sensitivity Derived From Recent Satellite and Surface Observations.

*Quaternary Science Reviews*, Vol.19, pp. 403-415, ISSN 0277-3791

doi:10.1029/2002GL015702, ISSN 0094-8276

*Nature*, Vol. 393, No.6684, pp. 450-455, ISSN 0028-0836

Vol.37, L03802, doi:10.1029/2009GL041327, ISSN 0094-8276 Chizhevsky, A.L. (1973). *Terrestrial echo of solar storms*, Mysl', Moscow (in Russian)

Vol.207, No.7 , pp. 1041-1044, ISSN 1095-9203

*Technology*, Vol.77, No. 2, pp. 150-152 , ISSN 1070-9762

During the 19th and 20th Centuries. *Geophysical Research Letters*, Vol. 27, No.14, pp.

Possible Influence of Cosmic Rays. *Space Science Reviews*, No.1-4, pp. 169-186, ISSN

Influence of Space Factors on Global Climate Warming. *Journal of Optical*

Years Based on Cosmogenic Nuclides. *Tellus B*, Vol. 52, No.3, pp. 985-992, ISSN

R.; Krainev, M.B.; Makhmutov, V.S.; Stozhkov, Y.I.; Svirzhevskaya, A.K.; Svirzhevsky, N.S. & Kovaltsov, G.A. (2008). Cosmic Ray Induced Ion Production in the Atmosphere. *Space Science Reviews,* DOI 10.1007/s11214-008-9339-y, ISSN 0038-

1500. *Geophysical Research Letters*, Vol.29, No.23, pp. 2111,

Bond, R.; Hajdas, I. & Bonani, G. (2001).Persistent Solar Influence on North Atlantic Climate During the Holocene. *Science*, Vol.294, No.5549, pp. 2130 – 2136, ISSN

and Climate. *Meteorology and Hydrology*, No.2, pp.3-14, ISSN 0130-2906, (in

Eruptions on Northern Hemisphere Summer Temperature Over the Past 600 Years.

**8. References**

0038-6308

1600-0889

6308

1095-9203.

Russian)

2227-2228, ISSN 1095-9203

2137-2140, ISSN 0094-8276

299, pp. 134-137, ISSN 0028-0836

*Journal of Geophysical Research*, Vol.112, D24S04, doi:10.1029/2007JD008740, ISSN 0094-8276

Solar Activity, Space Weather and the Earth's Climate 67

Jalkanen, R.; Tuovinen, M.; (2001). Annual Needle Production and Height Growth: Better

Jokipii, J.R. (1991). Variation of the cosmic-ray flux in time, In: *The Sun in time*. C.P. Sonnett,

Jones, P.D.; Briffa, K.R.; Barnett, T.P. & Tett, S.F.B. (1998). High-Resolution Palaeoclimatic

Jones, P.D., Parker, D.E., Osborn, T.J. & Briffa, K.R. (2001). Global and hemispheric

Johnsen, S.J.; Dansgaard, W.; Clausen, H.B. & Langway, C.C. (1972). Oxygen Isotope Profiles

Kazil, J. & Lovejoi, E.R.(2004). Tropospheric ionization and aerosol production: a model

Kocharov, G.E.; Ostryakov, V.M.; Peristykh, A.N. and Vasil'ev, V.A. (1995). Radiocarbon

Kocharov, G.E.; Akhmetkereev, S.Kh. & Peristykh, A.N. (1990). On the solar flare effect in

Kocharov, G.E.; Koudriavtsev, I.V.; Ogurtsov, M. G.; Sonninen, E. & Jungner, H. (2000). The

Konstantinov, B.P. & Kocharov, G E. (1965). *Astrophysical Phenomena and Radiocarbon*. Doklady Akademii Nauk SSSR, Vol.165, pp. 63–64, ISSN 0869-5652 (in Russian) Kostantinov, A.N.; Levchenko, V.A.; Kocharov, G.E.; Mikheeva, I.B.; Cecchini, S.; Galli, M.;

Kurt, V.; Belov, A.; Mavromichalaki, H.; & Gerontidou, M. (2004). Statistical analysis of solar

Kondratyev, K.Ya. & Nikolsky, G.A. (1983). The Solar Constant and Climate. *Solar Physics*,

Kondratyev, K.Ya. (1999). *Climatic effects of aerosol and clouds*. Springer/Praxis U.K.,

Tree Rings. *Radiocarbon*, Vol. 34, No. 2, pp. 247-253, ISSN 0033-8222

*Solar Physics*, Vol.188, pp.187-190, ISSN 0038-0938

No. 12, pp. 825-829, ISSN 1063-7329

22-2255-2004, ISBN 0992-7689

Chichester

Vol. 89, pp. 215-222, ISSN 0038-0938

Laboratory, US Department of Energy, Oak Ridge, Tennessee, USA

pp.39–44, ISSN 1125-7865

455-471, ISSN 0959-6836

434, ISSN 0028-0836

ISSN 0148-0227

159, pp. 381-395.

press

Climate Predictors Than Radial Growth at Treeline? *Dendrochronologia*, Vol.19, No,

M.S. Giampapa & M.S. Matthews, (Eds.), 205-220, Tuscon, University of Arizona

Records for the Last Millennium: Interpretation, Integration and Comparison with General Circulation Model Control-Run Temperatures. *The Holocene*, Vol. 8.4, pp.

temperature anomalies – land and marine records. In: *Trends: A compendium of data on global change*, Carbon dioxide information analysis center, Oak Ridge National

Through the Antarctic and Greenland Ice Sheets. *Nature*, Vol.235, No.5339, pp.429 –

study. *Journal of Geophysical Research*. Vol. 109. D19206, doi:10.1029/2004JD004852,

Content Variations and Maunder Minimum of Solar Activity. *Solar Physics*, Vol.

the atmospheric radiocarbon. In: *Possibilities of the methods of measurement of ultraminor amount of isotopes*, G.E. Kocharov, (Ed.), 45-70, PhTI, Leningrad (in Russian) Kocharov, G.E.; Ogurtsov, M.G. & Dreschoff, G.A.M. (1999). On the Quasi-Five-Year

Variation of Nitrate Abundance in Polar Ice and Solar Flare Activity in the Past.

Nitrate Content of Greenland Ice and Solar Activity. *Astronomy Reports*, Vol. 44,

Nanni, T.; Povinec, P.; Ruggiero, L. & Salomoni, A. (1992). Theoretical and Experimental Aspects of Solar Flares Manifestation in Radiocarbon Abundance in

proton events. *Annales Geophysicae*, Vol.22, No.6, pp.2255-2271, doi:10.5194/angeo-

Ferretti, M.; Innes, J.L.; Jalkanen, R.; Saurer, M.; Schäffer, O.; Spiecker, H. & Wilpert, K.

Forster, P. M. & Gregory, J. M. (2006). The Climate Sensitivity and Its Components

Frame, D.J.; Booth, B.B.B.; Kettleborough, J.A.; Stainforth, D.A.; Gregory, J.M.; Collins, M. &

Friis-Christensen, E. & Lassen, K. (1991). Length of Solar Cycle: an Indicator of Solar Activity

Fröhlich, C. & Lean, J. (1998).The Sun's Total Irradiance: Cycles and Trends in the Past Two

Gagen, M.; Zorita, E.; McCarroll, D.; Young, G.H.F.; Grudd, H.; Jalkanen, R.; Loader, N.J.;

Gray, L. J.; Beer, J.; Geller, M.; Haigh, J.D.; Lockwood, M.; Matthes, K.; Cubasch, U.;

Harrison, R.G. & Ambaum, M.H.P. (2009). Observed Atmospheric Electricity Effect on Clouds. *Environmental Research Letters*, Vol.4, 014003, ISSN 1748-9326 Helama, S.; Timonen, M.; Lindholm, M.; MeriläInen, J. & Eronen, M. (2005). Extracting

Herron, M. (1982). Impurity Sources of F, Cl, NO3 and SO4 in Greenland and Antarctic

Hilasvuori, E.; Berninger , F.; Sonninen, E.; Tuomenvirta, H. & Jungner, H. (2009). Stability

Hoyt, D.V. & Schatten, K.H. (1993). A Discussion on Plausible Solar Irradiance Variations,

Hoyt, D. & Sсhatten, K. H. (1998). Group Sunspot Numbers: a New Solar Activity

Idso, S.B. (1998). CO2-Induced Global Warming: a Skeptic's View of Potential Climate

IPCC (2007). WGI Fourth Assessment Report: Climate Change 2007: *The Physical Science*

Reconstruction. *Solar Physics*, Vol.179, pp. 189-219, ISSN 0038-0938

change. *Climate Research*, Vol. 10, pp. 69 – 82, ISSN 0936-577x

*Dendrochronologia*, Vol. 20, No.1-2, pp. 159-174, ISSN 1125-7865

*Geophysical Research Letters*, Vol.32, L09702, ISSN 0094-8276

39–52. doi:10.1175/JCLI3611.1., ISSN 0894-8755

Fritts, H. (1976). *Tree rings and climate*, Academic Press, London

Vol. 25, No.23 , pp. 4377– 4380, ISSN 0094-8276

L05701. doi: 10.1029/2010GL046216, ISSN 0094-8276

Vol.48, RG4001, doi:10.1029/2009RG000282, ISSN 8755-1209

9203

ISSN 0899-8418

pp.469-480, ISSN 0267-8179

*Basis: Summary for Policymakers*, Paris

0094-8276

0094-8276

(2002). Air pollution and environmental chemistry what role for tree-ring studies?

Diagnosed from Earth Radiation Budget Data. *Journal of Climate*, Vol.19, No.1, pp.

Allen, M.R. (2005). Constraining Climate Forecasts: the Role of Prior Assumptions.

Closely Associated With Climate. *Science*, Vol.254, No5032, pp. 698-700, ISSN 1095-

Decades and Associated Climate Change Uncertainties. *Geophysical Research Letters*,

Robertson, I. & Kirchheler, A. (2011). Cloud Response to Summer Temperatures in Fennoscandia over the Last Thousand Years. *Geophysical Research Letters*, Vol.38,

Fleitmann, D.; Harrison, G.; Hood, L.; Luterbacher, J.; Meehl, G. A.; Shindell, D.; van Geel B. & White, W. (2010). Solar influences on climate. *Reviews in Geophysics*,

Long-Period Climate Fluctuations from Tree-Ring Chronologies Over Timescales of Centuries to Millennia. *International Journal of Climatology*, Vol. 25, pp.1767-1779,

Precipitation*. Journal of Geophysical Research*, Vol.87, No.C4, pp. 3052–3060, ISSN

of Climate Signal in Carbon and Oxygen Isotope Records and Ring Width From Scots Pine (*Pinus sylvestris* L.) in Finland. *Journal of Quaternary Science*, Vol.24, No.5,

1700-1992 . *Journal of Geophysical Research*, Vol. 98, No.A11, pp. 18895-18906, ISSN

Solar Activity, Space Weather and the Earth's Climate 69

Mayewski, P.A.; Lyons, W.B.; Twickler, M.S.; Buck, C.F. & Whitlow, S. (1990). An Ice Record

Mc Cracken, K. G.; Dreschhoff, G. A. M.; Smart, D. F. & Shea, M.A. (2001A). Solar Cosmic

Mc Carroll, D.; Jalkanen, R.; Hicks, S.; Tuovinen, M.; Gagen, M.; Paweliek, F.; Eckstein, D.;

Nagovitsyn, Yu.A. (1997). A Nonlinear Mathematical Model for the Solar Cyclicity and

Nagovitsin, Yu.A.; Ivanov, V.G.; Miletsky, E.V. & Volobuev, D. (2003). Solar Activity

Nagovitsyn, Yu.A.; Ivanov, V.G.; Miletsky Kaunas, E.V. & Volobuev, D.M. (2004). ESAI Data

Nagovitsyn, Yu.A. (2005). To the Description of Long-Term Variations in the Solar Magnetic

Ney, E.P. (1959). Cosmic radiation and weather. *Nature*, Vol.183, pp. 451-452, ISSN 0028-0836 Nicoll, K.A., & Harrison, R.G. (2009). Vertical Current Flow through Extensive Layer

Obridko, V.N. (2008). Magnetic Fields and Active Complexes. In: *Plasma HelioGeoPhysics*

Ogurtsov, M.G.; Kocharov, G.E.; Lindholm, M.; Meriläinen, J.; Eronen, M. & Nagovitsyn,

Reconstructions. *Solar Physics*, Vol. 205, No.2, P. 403-417, ISSN 0038-0938

*Geophysical Research*, Vol.106, No.A10, pp. 21599-21610, ISSN 0148-0227 Mc Cracken, K.G.; Dreschhoff, G.A.M.; Zeller, E.J.; Smart, D.F. & Shea, M.A. (2001B). Solar

No.D7, pp. 12839-12847, ISSN 0148-0227

No. 6284, pp. 554-556., ISSN 0028-0836

23, No. 6, pp. 742 – 748, ISSN 1063-7737

2, pp. 103-112, ISSN 0038-0938

9221-1040-2, Moscow, (in Russian)

Magnus University, ISBN 9955-530-89-8, Kaunas

0227

0938

1063-7737

1221, ISSN 1364-6826

an Expanded View of Climate Change. *Journal of Geophysical Research,* Vol. 98,

of Atmospheric Response to Antropohenic Sulphate and Nitrate. *Nature*, Vol. 346,

Ray Events for the Period 1561-1994: 2. The Gleissberg periodicity. *Journal of*

Cosmic Ray Events for the Period 1561-1994. 1. Identification in Polar Ice, 1561- 1950. *Journal of Geophysical Research*, Vol. 106, No. A10, pp. 21585-21598, ISSN 0148-

Schmitt, U.; Autio, J. & Heikkinen, O. (2003). Multiproxy dendroclimatology: a pilot study in northern Finland. *The Holocene*, Vol.13, No.6, pp. 829-838, ISSN 0959-6836 Moberg, A.; Sonechkin, D.M.; Holmgren, K.; Datsenko, M.M. & Karlen, W. (2005). High

Variable Northern Hemisphere Temperatures Reconstructed from Low- and High-Resolution Proxy Data. *Nature*, Vol. 433, No.7026, pp. 613-617, ISSN 0028-0836 Mordvinov, A.V.; Makarenko, N.G.; Ogurtsov, M.G. & Jungner, H. (2004). Reconstruction of

magnetic activity of the Sun and changes in its irradiance on a millennium timescale using neurocomputing. *Solar Physics*, Vol. 224, pp. 247-253, ISSN 0038-

Prospects for Reconstructing the Solar Activity in the Past. *Astronomy Letters*, Vol.

Reconstruction from Proxy Data. In.: *Proc. of International conference-workshop "Cosmogenic climate forcing factors during the last millennium*". 41-50, Vytautas

Base and Some Properties of Solar Activity in the Past. *Solar Physics*, Vol. 224, No. 1-

Flux: the Sunspot Area Index. *Astronomy Letters*, Vol.31, No.8, pp. 557-562, ISSN

Clouds. *Journal of Atmospheric and Solar-Terrestrial Physics,* Vol. 71*,* No. 12, pp. 1219-

*monograph*, L.M. Zelenyi and I.S. Veselovsky (Eds.), 41-60, Fizmatlit, ISBN 978-5-

Yu.A (2002). Evidence of Solar Variation in Tree-Ring-Based Climate

Laken, B. A.; Kniveton, D. R. & Frogley, M. R. 2010. Cosmic Rays Linked to Rapid Mid-

Lean, J.; Beer J. & Bradley, R. (1995). Reconstruction of Solar Irradiance since 1610:

Lee, S.-H.; Reeves, J. M.; Wilson, J. C.; Hunton, D. E.; Viggiano, A. A.; Miller, T. M.;

Legrand, M.R.; Stordal, F.; Isaksen, I.S.A. & Rognerud, B. (1989). A Model Study of the

Legrand, M.R. & Kirchner, S. (1990). Origins and Variation of Nitrate in South Polar

Lindzen, R.S. & Giannitsis, C. (1998). On the climatic implications of volcanic cooling. *Journal of Geophysical Research,* Vol. 103, No. D6, pp. 5929-5941, ISSN 0148-0227 Lindzen, R.S.; Chou, M.-D. & Hou, A.Y. (2001). Does the Earth Have an Adaptive Infrared

Lindzen, R. S. & Choi, Y.-S. (2009). On the Determination of Climate Feedbacks from ERBE

Loehle, C.A. (2007). 2000-year global temperature reconstruction based on non-treering proxies. *Energy and Environment*, Vol. 18, No. 7-8, pp.1049-1058, ISSN 0958-305X Logan, J.A. (1983). Nitrogen Oxides in the Troposphere: Global and Regional Budgets. *Journal of Geophysical Research*, Vol.88, No.C15, pp. 10785-10805, ISSN 0148-0227 Lu, H.; Jarvis, M.J.; Graf, H.-F.; Young, P.C. & Horne, R.B. (2007) Atmospheric Temperature

18253, ISSN 1680-7367

1889, ISSN1095-9203

0227

0477

0227

0094-8276

3195-3198, ISSN 0094-8276

*Tellus*, Vol. 41B, pp. 413-426, ISSN

Latitude Cloud Changes. *Atmospheric 1 and Physics Discussions*, Vol. 10, pp. 18235–

Implications for Climate Change. *Geophysical Research Letters*, Vol. 22, No.23, pp.

Ballenthin, J. O. & Lait, L. R. (2003). Particle Formation by Ion Nucleation in the Upper Troposphere and Lower Stratosphere. *Science*, Vol.301, No.5641, pp.1886-

Stratospheric Budget of Odd Nitrogen, Including Effects of Solar Cycle Variations.

Precipitations. *Journal of Geophysical Research,* Vol. 95, pp. 3493-3507, ISSN 0148-

Iris? *Bulletin of the American Meteorological Society*, Vol. 82, pp. 417-432, ISSN 1520-

Data. *Geophysical Research Letters.* Vol. 36, L16705, doi:10.1029/2009GL039628, ISSN

Responses to Solar Irradiance and Geomagnetic Activity. *Journal of Geophysical Research*, Vol. 112, No. D11, D11109, doi:10.1029/2006JD007864, ISSN 0148-0227 Mann, M.E.; Bradley, R.S. & Hughes, M.K. (1999). Northern Hemisphere Temperatures

During the Past Millennium: Inferences, Uncertainties, and Limitations. *Geophysical*

Surface Temperature Reconstructions Using Terrestrial Borehole Data. *Journal of Geophysical Research,* Vol. 108, No.D7, 4203, doi:10.1029/2002JD002532, ISSN 0148-

Geomagnetic Disturbances on the Formation of Aerosol Layers in the Stratosphere.

Legrand, M.R. & Steffenson, J. (1993). Greenland Ice Core «Signal» Characteristics:

Mann, M. & Hughes, M. (2002). Tree Ring Chronologies and Climate Variability. *Science*,

Mann, M.E.; Rutherford, S.; Bradley, R.S.; Hughes, M.K. & Keimig, F.T. (2003). Optimal

Marichev, V.N.; Bogdanov, V.V.; Zhivet'ev, I.V. & Shvetsov, B.M. (2004). Influence of the

Geomagnetism and Aeronomy, Vol.44, No.6, pp. 779-786, ISSN 0016-7932 Marsh, N. & Svensmark, H. (2000). Low Cloud Properties Influenced by Cosmic Rays. *Physical Review Letters*, Vol. 85, No. 23, pp. 5004-5007, ISSN 0031-9007 Mayewski, P.A.; Meeker, L.D.; Morrison, M.C.; Twickler, M.S.; Whitlow, S.; Ferland, D. A.;

*Research Letters*, Vol. 26, No.6, pp. 759-762, ISSN 0094-8276

Vol.296, No.5569, pp. 848-852, ISSN 1095-9203

an Expanded View of Climate Change. *Journal of Geophysical Research,* Vol. 98, No.D7, pp. 12839-12847, ISSN 0148-0227

Solar Activity, Space Weather and the Earth's Climate 71

Schwartz, S. E. (2008). Reply to comments by G. Foster et al., R. Knutti et al., and N. Scafetta

Shindell, D.; Rind, D.; Balachandran, N.; Lean & P. Lonergan. (1999). Solar Cycle Variability, Ozone and Climate. *Science*, Vol.284, No, pp. 305-308, ISSN 1095-9203 Shumilov, O.I.; Kasatkina, E.A.; Henriksen, K. & Vashenyuk, E.V.(1996). Enhancement of

Shvedov, P.N. (1892). Tree as archive of droughts. *Meteorological bulletin*, No.5 (in Russian). Solanki, S.; Usoskin, I.G.; Kromer, B.; Schüssler, M. & Beer, J. (2004). Unusual Activity of the

Starkov, G.V. & Roldugin, V.K. (1995). Relationship Between Atmospheric Transparency

Stozhkov, Y. I. (2003). The Role of Cosmic Rays in the Atmospheric Processes. *Journal of*

Stuiver, M.; Reimer, P. J.; Bard, E.; Beck, J.W.; Burr, G.S.; Hughen, K.A.; Kromer, B.;

Solanki, S. (2003). Sunspots: An Overview. *The Astronomy and Astrophysics Review*, Vol.11,

Svensmark, H.; Pedersen, J.O.;.Marsh, N. M.; Enghoff, ,M.B. & Uggerhøj U.I. (2007).

Svensmark, H.; Bondo, T. & Svensmark, J. (2009). Cosmic Ray Decreases Affect Atmospheric

Tinsley, B. A., Deen, G.W. (1991). Apparent Tropospheric Response to MeV-GeV Particle

Tinsley, B.A. (2000), Influence of solar wind on the global electric circuit, and inferred effects

Tinsley, B.A. (2008). The Global Atmospheric Electric Circuit and its Effects on Cloud Microphysics. *Reports on Progress in Physics*, Vol. 71, 066801, ISSN 0034-4885 Todd, M. & Kniveton, D.R. (2004). Short-Term Variability in Satellite-Derived Cloud Cover

press).

pp. 1119-1123, ISSN 0992-7689

559-562, ISSN 0016-7932

pp.153–286, ISSN 0935-4956

396, ISSN 1364-5021

0148-0227

ISSN 0954-3899

8222

431, No.7012, pp. 1084 - 1087, ISSN 0028-0836

doi:10.1029/2009GL038429, ISSN 0094-8276

*Science Review,* Vol.94, pp.231-258, ISSN 0038-6308

*Physics*, Vol. 66, No.13-14, pp. 1205-1211, ISSN 1364-6826

on Heat capacity, time constant, and sensitivity of Earth's climate system. *Journal of Geophysical Research*, Vol.113, D15105, doi:10.1029/2008JD009872, ISSN 0148-0227 Seo, J.-W.; Eckstein, D.; Jalkanen, R. & Schmitt, U. (2011). Climatic Control of Intra- and

Inter-Annual Wood-Formation Dynamics of Scots Pine in Northern Finland. *Environmental and Experimental Botany*, doi: 10.1016/ienvexpbot.2Q 11.01.003, (in

Stratospheric Aerosol after Solar Proton Event. *Annales Geophysicae*, Vol. 4, No.11,

Sun During Recent Decades Compared to the Previous 11,000 years. *Nature*, Vol.

Variations and Geomagnetic Activity. *Geomagnetism and Aeronomy*, Vol. 34, No. 4, p.

*Physics G: Nuclear Particle Physics*, Vol.29, pp.913–923 PII: S0954-3899(03)54503-0,

McCormac, G.; van der Plicht, M. & Spurk, M. (1998). INTCAL98 Radiocarbon Age Calibration, 24 000-0 cal BP. *Radiocarbon*, Vol. 40, No.3, pp. 1041-1083, ISSN 0033-

Experimental Evidence for the Role of Ions in Particle Nucleation Under Atmospheric Conditions. Proceedings of the Royal Society A . Vol. 463, pp. 385–

Aerosols and Clouds. *Geophysical Research Letters,* Vol.36, L15101,

Flux Variations: a Connection via Electrofreezing of Supercooled Water in High-Level Clouds. *Journal of Geophysical Research*, Vol. 96, No.12, pp. 22283-22296, ISSN

on cloud microphysics, temperature, and dynamics in the troposphere. *Space*

and Galactic Cosmic Rays: an Update. *Journal of Atmospheric and Solar-Terrestrial*

Ogurtsov, M.G.; Jungner, H.; Kocharov, G.E.; Lindholm M. & Eronen, M. (2004). Nitrate

Ogurtsov, M.G. & Lindholm, M. (2006). Uncertainties in Assessing Global Warming During

Ogurtsov, M. G. (2007). Secular Variation in Aerosol Transparency of the Atmosphere as the

Ogurtsov, M.G.; Jungner, H.; Helama, S.; Lindholm, M. & Oinonen, M. (2011).

Ogurtsov, M.G. (2011). Relationship Between the Aerosol Concentration in the Stratosphere

Palle, E.; Buttler, C.J. & O'Brien, K. (2004). The Possible Connection Between Ionization in

Palmer, A.S.; van Ommen, T.D.; Curran, A.J. & Morgan, V. (2001). Ice-Core Evidence for a

Pensa, M.; Sepp, M. & Jalkanen, R. (2006). Connections Between Climatic Variables and the

Pudovkin, M.I. (1996). Effect of the Solar Activity on the Lower Atmosphere and Weather.

Pudovkin, M.I. & Veretenenko, S.V. (1992). Effect of Geomagnetic Disturbances on the

Roden, J.S.; Lin, G. & Ehleringer, J.R. (2000). A Mechanistic Model for Interpretation of

Roldugin, V.K.& Vashenyuk, E.V. (1994). Atmospheric Transparency Variations Caused by

Roldugin, V.C. & Tinsley, B.A.(2004). Atmospheric Transparency Changes Associated with

*Solar-Terrestrial Physics*, Vol.66, No.13-14, pp. 1143–1149, ISSN 1364-6826 Schove, D.J (1983). *Sunspot Cycles*. Hutchinson Ross Publications, ISBN 0-87033-424-X,

*Solar-Terrestrial Physics*, Vol. 66, No, pp. 1779–1790, ISSN 1364-6826

*Soros Educational Journal*, No.10, pp. 106-114 (in Russian)

*Geomagnetism and Aeronomy*, Vol. 47, No. 1, pp. 118-128, ISSN 0016-7932 Ogurtsov, M.G. (2010). Long-Term Solar Cycles According to Data on the Cosmogenic

*Environment*, Vol.17, No.5, pp. 685-706, ISSN 0958-305X

Vol. 50, No. 4, pp. 475–481, ISSN 0016-7932

pp.17-27, ISSN 1468-0459

pp. 1953-1956, ISSN 0148-0227

pp. 114 –115, ISSN 0016-7932

Stroudsburg Pennsylvania

*Acta*, Vol.64, pp.21-35, ISSN 0016-7037

0938

7932

Concentration in Greenland Ice: an Indicator of Changes in Fluxes of Solar and Galactic High-Energy Particles. *Solar Physics*, Vol.222, pp.177-190, ISSN 0038-0938 Ogurtsov, M.G. (2005). On the Possibility of Forecasting the Sun's Activity Using

Radiocarbon Solar Proxy. *Solar Physics* , Vol.231, No.1-2, pp.167-176, ISSN 0038-

the 20th Century: Disagreement Between Key Data Sources. *Energy and*

Possible Link Between Long-Term Variations in Solar Activity and Climate.

Beryllium Concentration in Ice of Central Greenland. *Geomagnetism and Aeronomy*,

Paleoclimatological Evidence for Unprecedented Recent Temperature Rise at the Extratropical Part of the Northern Hemisphere. *Geografiska Annaler*, Vol. 93, No.1,

and Ionization According to the Data on Conductivity and Nitrate Content of Greenland Ice. *Geomagnetism and Aeronomy*, Vol.51, No.2, pp. 267-274, ISSN 0016-

the Atmosphere by Cosmic Rays and Low Level Clouds. *Journal of Atmospheric and*

Small-Source of Atmospheric Nitrate. *Journal of Geophysical Research,*. Vol. 28, No.10,

Growth and Needle Dynamics of Scots Pine (Pinus sylvestris L.) in Estonia and Lapland. *International Journal of Biometeorology*, Vol.50, pp.205–214, ISSN 0020-7128

Intensity of Direct Solar Radiation Flux. *Geomagnetism and Aeronomy*, Vol.32, No.1 ,

Hydrogen and Oxygen Ratios in Tree-Ring of Cellulose. *Geochimica et Cosmochimica*

Cosmic Rays. *Geomagnetism and Aeronomy*, Vol.34, No.2, p. 251-253, ISSN 0016-7932

Solar Wind-Induced Atmospheric Electricity Variations. *Journal of Atmospheric and*

**Part 3**

**Climate and ENSO**

**Part 3**

**Climate and ENSO**

72 Climate Variability – Some Aspects, Challenges and Prospects

Tuovinen, M.; Mc Carroll, D.; Grudd, H.; Jalkanen, R. & Los, S. (2009). Spatial and Temporal

Usoskin, I.G.; Mursula, K.; Solanki, S.; Schüssler, M. & Alanko, K. (2004). Reconstruction of

Usoskin, I.G.; Solanki, S.K. & Taricco, C. (2006). Long-Term Solar Activity Reconstructions:

Usoskin, I.G.; Solanki, S.; Kovaltsov, G.A.; Beer, J. & Kromer, B. (2006). Solar Proton Events

Vasiliev, S.S. & Dergachev, V.A. (2002). The ~2400-year cycle in atmospheric radiocarbon

Veretenenko, S. V. & Pudovkin M. I. (1993). Effects of Cosmic Ray Variations in the

Veretenenko, S. V.; Dergachev, V. A. & Dmitriev, P. B. (2007). Effect of Solar Activity and

Vitinsky, Y.I.; Kopecky, M. & Kuklin, G.V. (1986). *Statistics of Sunspot Activity*, Nauka,

Young, G. H. F.; McCarroll, D; Loader, N. J. & Kirchhefer, A. (2010). A 500-year Record of

Yu, F. & Turco, R. P. (2000). Ultrafine Aerosol Formation via Ion-Mediated Nucleation. *Geophysical Research Letters,* Vol.27, No, pp. 883-886, ISSN 0094-8276 Yu, F. & Turco, R. P. (2001). From Molecular Clusters to Nanoparticles: The Role of Ambient

Yu, F. (2006). From Molecular Clusters to Nanoparticles: Second-Generation Ion-Mediated

Zeller, E.J. & Parker, B.C. (1981). Nitrate Ions in Antarctic Firn as a Marker of Solar Activity. *Geophysical Research Letters*, Vol. 8, No.8, pp. 895-898, ISSN 0094-8276

Wild, G. (1882). *On the Air temperature in Russian Empire*. No. 2, St.Petersburg, (in Russian) Willson, R.C. & Mordvinov, A.V. (2003). Secular solar irradiance trend during solar cycles

and Maximum Density. *Boreas*, Vol.38, No.1, pp. 1-12, ISSN 1502-3885 Urey, H.C. (1948). Oxygen Isotopes in Nature and in the Laboratory. *Science*, Vol.108,

No.2813. pp. 602-603., ISSN 1095-0203

Vol.413, pp.745–751, ISSN 0004-6361

Vol. 20, pp. 115-120, ISBN 0992-7689

pp. 35-40, ISSN 0016-7932 (in Russian).

Moscow, USSR (in Russian)

*Holocene*, Vol.20, No.3, pp. 315-324, ISSN

Vol.106, No, pp. 4797-4814, ISSN 0148-0227

3092, ISSN 1680-7367

L25-28, ISSN 0004-6361

ISSN 0094-8276

1062-8738

8276

Stability of the Climatic Signal in Northern Fennoscandian Pine Tree-Ring Width

Solar Activity for the Last Millennium Using 10Be Data. *Astronomy and Astrophysics*,

Direct Test by Cosmogenic 44Ti in Meteorites. *Astronomy and Astrophysics*, Vol. 457,

in Cosmogenic Isotope Data. *Geophysical Research Letters*, Vol.33, No. 8, L08107,

concentration: bispectrum of 14C data over the last 8000 years. *Annales Geophysicae*.

Circulation of the Lower Atmosphere. *Geomagnetism and Aeronomy*, Vol. 33, No.6,

Cosmic-Ray Variations on the Position of the Arctic Front in the North Atlantic. *Bulletin of the Russian Academy of Sciences: Physics*, Vol.71, No.7, pp. 1010-1013, ISSN

21-23. *Geophysical Research Letters*, Vol. 30, doi:10.1029/2002GL016038, ISSN 0094-

Summer Near-Ground Solar Radiation from Tree-Ring Stable Carbon Isotopes. *The*

Ionization in Tropospheric Aerosol Formation. *Journal of Geophysical Research*,

Nucleation Model. *Atmospheric Chemistry and Physics Discussions*, Vol. 6, pp. 3049–

**1. Introduction**

forecasting.

systems of various operational centers.

El Niño–Southern Oscillation (ENSO) is one of the most influential fluctuations of the coupled atmosphere-ocean climate system in the Seasonal-to-Interannual (SI) time scale for global and local communities. Ocean data assimilation systems are generally adopted for monitoring ENSO because the variation of the heat content in the ocean interior of the equatorial Pacific is considered a good precursor of El Niños, and essential for understanding the ENSO process. They are also utilized for the initialization of Coupled Atmosphere-Ocean General Circulation Models (CGCMs), along with atmosphere data assimilation systems in the SI forecasting

**Assimilating Ocean Observation**

Yosuke Fujii1, Masafumi Kamachi1, Toshiyuki Nakaegawa1,

Tamaki Yasuda1, Goro Yamanaka1, Takahiro Toyoda1,

**Data for ENSO Monitoring**

Kentaro Ando2 and Satoshi Matsumoto3

*2Japan Agency for Marine-Earth Science and Technology*

**and Forecasting**

*1Japan Meteorological Agency/ Meteorological Research Institute*

*3Japan Meteorological Agency*

*Japan*

**4**

In this paper, we discuss the capacity of current operational ocean data assimilation systems adopted for ENSO monitoring and SI forecasting based on studies using the system of the Japan Meteorological Agency (JMA). It then demonstrates the benefits of assimilating ocean observation data through those systems in SI forecasts using the JMA seasonal and ENSO forecasting system. It also introduces the recent effort of JMA/Meteorological Research Institute (MRI) to resolve "coupled shock", which is one of the most crucial issues concerning the initialization with uncoupled ocean and atmosphere data assimilation systems in SI

This chapter is organized as follows. Section 2 summarizes the process of developing ocean data assimilation systems for up-to-date ENSO monitoring and SI forecasting. In particular, it describes efforts to improve the Temperature-Salinity (T-S) balance in the assimilation results over the past decade. We then introduce the ocean data assimilation system used in the JMA seasonal and ENSO forecasting system as a state-of-the-art system in Section 3. Section 4 demonstrates the importance of assimilating ocean observation data through the ocean data assimilation system for ENSO and seasonal forecasting. Section 5 introduces the recent effort

to resolve the coupled shock at JMA/MRI. This chapter is summarized in Section 6.

## **Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting**

Yosuke Fujii1, Masafumi Kamachi1, Toshiyuki Nakaegawa1, Tamaki Yasuda1, Goro Yamanaka1, Takahiro Toyoda1, Kentaro Ando2 and Satoshi Matsumoto3 *1Japan Meteorological Agency/ Meteorological Research Institute 2Japan Agency for Marine-Earth Science and Technology 3Japan Meteorological Agency Japan*

#### **1. Introduction**

El Niño–Southern Oscillation (ENSO) is one of the most influential fluctuations of the coupled atmosphere-ocean climate system in the Seasonal-to-Interannual (SI) time scale for global and local communities. Ocean data assimilation systems are generally adopted for monitoring ENSO because the variation of the heat content in the ocean interior of the equatorial Pacific is considered a good precursor of El Niños, and essential for understanding the ENSO process. They are also utilized for the initialization of Coupled Atmosphere-Ocean General Circulation Models (CGCMs), along with atmosphere data assimilation systems in the SI forecasting systems of various operational centers.

In this paper, we discuss the capacity of current operational ocean data assimilation systems adopted for ENSO monitoring and SI forecasting based on studies using the system of the Japan Meteorological Agency (JMA). It then demonstrates the benefits of assimilating ocean observation data through those systems in SI forecasts using the JMA seasonal and ENSO forecasting system. It also introduces the recent effort of JMA/Meteorological Research Institute (MRI) to resolve "coupled shock", which is one of the most crucial issues concerning the initialization with uncoupled ocean and atmosphere data assimilation systems in SI forecasting.

This chapter is organized as follows. Section 2 summarizes the process of developing ocean data assimilation systems for up-to-date ENSO monitoring and SI forecasting. In particular, it describes efforts to improve the Temperature-Salinity (T-S) balance in the assimilation results over the past decade. We then introduce the ocean data assimilation system used in the JMA seasonal and ENSO forecasting system as a state-of-the-art system in Section 3. Section 4 demonstrates the importance of assimilating ocean observation data through the ocean data assimilation system for ENSO and seasonal forecasting. Section 5 introduces the recent effort to resolve the coupled shock at JMA/MRI. This chapter is summarized in Section 6.

In addition, the number of Argo floats rapidly increased after 2000, in order to meet the requirements of the Global Ocean Data Assimilation Experiment (GODAE) project (Clark et al., 2009). This second material change in the observing system urged to develop schemes for assimilating observed salinity profiles effectively in ocean data assimilation systems. In 2000, the name of the TAO array was also changed to TAO/Triangle Trans-Ocean Buoy Network (TRITON) array to designate the upgrading of ATLAS buoys west of 160◦E to TRITON buoys that contain more enriched equipment (Kuroda, 2002). However, many in situ observation platforms still exist, including the TAO/TRITON array, which provide little salinity data. In order to use those observation data effectively along with the data of simultaneous observations of temperature and salinity by Argo floats and other sophisticated platforms, it is necessary to develop a method to guarantee consistency between temperature and salinity fields, even when only a small amount of salinity observation data exist. Thus, for the past decade, various schemes estimating salinity fields mainly from temperature and SSH through the T-S balance relationship have been developed based on OI or 3DVAR (summarized in Fujii

Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting 77

For example, salinity fields are estimated using regression coefficients of their anomaly with the SSH anomaly, as well as temperature fields by Ezer & Mellor (1994); Kamachi et al. (2001). Some studies (e.g., Carton et al., 2000; Huang et al., 2008; Yan et al., 2004) uses climatological T-S relations typically calculated from the World Ocean Atlas (e.g., Locarnini et al., 2010). Vertical shifts of water masses in background (model) T-S profiles are applied in Haines et al. (2006); Ricci et al. (2005). Coupled T-S Empirical Orthogonal Function (EOF) modal decomposition is also employed to reconstruct vertical temperature and salinity profiles in

Thus, most current operational ocean data assimilation systems adopt OI or 3DVAR and have the capacity to assimilate observed salinity profiles imposing a multivariate (mainly T-S) balance relationship. These systems are called "second-generation" systems in Balmaseda et al. (2009; 2011), while systems that can assimilate temperature data alone are called "first-generation" systems. Balmaseda et al. (2011) demonstrated an example of improved ENSO forecasting from the first generation to the second generation. In the following section,

It should be noted that data assimilation systems adopting more sophisticated schemes (e.g., ensemble Kalman filter or the adjoint method) have also been developed or started to use in operation (e.g., Keppenne et al., 2005; Weaver et al., 2003). These systems have the potential

This section introduces MOVE/MRI.COM-G (Usui et al., 2006, hereafter, MOVE-G) as a typical second-generation ocean data assimilation system, and discusses the capacity improvement of second-generation systems based on assimilation runs of MOVE-G. This

MOVE-G is composed of an Ocean General Circulation Model (OGCM) based on the MRI Community Ocean Model (MRI.COM; Tsujino et al., 2010), and the Multivariate Ocean Variational Estimation System (MOVE), an analysis scheme based on Fujii & Kamachi (2003). The OGCM has a nearly global domain extending from 75◦S to 75◦N. The grid spacing is 1◦ in the zonal direction, and changes from 0.3◦ (within 5.7◦S - 5.7◦N) to 1◦ (poleward of 16◦S and 16◦N) in the meridional direction. It has 50 levels in the vertical direction. The first seven levels are 1, 3, 6, 10, 16, 22, and 30 m in depth, and the interval between 30 and 200 m depth is

Dobricic et al. (2005); Fujii & Kamachi (2003); Maes et al. (2000).

we also confirm the advantage of second-generation systems.

**3. Capacity of the second-generation system at JMA**

system is also employed in the studies of Sections 4 and 5.

to improve the accuracy of monitoring and forecasting skills further.

et al., 2010).

#### **2. First and second generations of ocean data assimilation systems**

There have been two innovative efforts in the development of ocean data assimilation systems for ENSO monitoring and SI forecasting, and both of them occurred following material changes of the ocean observing system.

The first effort followed the deployment of the Tropical Atmospheric Ocean (TAO) array (Hayes et al., 1991; McPhaden et al., 1998) under the Tropical Ocean and Global Atmosphere (TOGA) program (e.g., Ji et al., 1995). This change initially realized the operational monitoring of the ocean interior state in the equatorial Pacific using an ocean data assimilation system.

However, since major oceanic data observed by Autonomous Temperature Line Acquisition System (ATLAS) buoys (mooring buoys that form the TAO array) were temperature profiles, ocean data assimilation systems developed in this early stage adopted the Optimal Interpolation (OI) or Three-Dimensional Variational Method (3DVAR) assimilating only temperature profiles. Salinity profiles were abandoned in those systems, although their number was not large. It should also be noted that these data assimilation systems directly modified only the model temperature fields; the model salinity field was modified only through adjustment by model physics. Development of schemes assimilating Sea Surface Height (SSH) data started after the launch of the TOPEX/Poseidon satellite, but it still focused on modifying the model temperature field for the first several years (e.g., Ji et al., 2000).

Some studies, however, pointed out that appropriate treatment of salinity is essential for controlling model fields realistically, for the following two reasons. One is the effect of salinity variations on SSH and pressure fields through contribution to density variations. This issue was addressed by Cooper (1988); Ji et al. (2000); Maes (1998). In particular, Ji et al. (2000) demonstrated that the use of observed SSH in addition to in situ temperature data can increase analysis errors in the systems analyzing temperature alone without considering the salinity anomaly, because the spurious temperature anomaly is estimated in order to compensate the contribution of salinity to SSH.

The other reason is the possibility of destroying density stratification, which was first addressed by Woodgate (1997). Temperature and salinity decrease with depth in many areas of the tropical and subtropical oceans. The upward shift of water mass induces cold and low salinity anomalies in those areas. When assimilating temperature alone, the cold anomaly is reproduced but the low salinity anomaly is missed, resulting in the simulated greater density exceeding the actual density. This artificial density tends to destabilize the stratification and to induce spurious vertical mixing. This effect severely diminishes the salinity maximums in the subsurface layers in the tropical oceans (e.g., Troccoli et al., 2002).

Many studies have also stressed the importance of salinity variability in the equatorial Pacific. The surface salinity front existing in the western equatorial Pacific is considered a good indicator of the eastern edge of the warm water pool, and its position is directly connected to the advective-reflective oscillator theory (e.g., Picaut et al., 1997). Roemmich et al. (1994) suggested that the salinity front affects the current fields in the near-surface layer.

Formation of the barrier layer (the isothermal layer in which salinity is stratified; Lukas & Lindström, 1991) is another important feature associated with salinity there. This barrier layer can affect surface currents by thinning the mixed layer and concentrating the effect of wind stress (e.g., Vialard et al., 2002). It is also considered to induce a temperature rise in the mixed layer because it prevents warm water at the surface from mixing with cold water in the thermocline. This tendency is confirmed in the equatorial Pacific (e.g., Ando & McPhaden, 1997; Fujii et al., 2012; Maes et al., 2006). Some studies (e.g., Maes & Belamari, 2011) have further used CGCMs to confirm the impact of the barrier layer at the onset of El Niños.

2 Will-be-set-by-IN-TECH

There have been two innovative efforts in the development of ocean data assimilation systems for ENSO monitoring and SI forecasting, and both of them occurred following material

The first effort followed the deployment of the Tropical Atmospheric Ocean (TAO) array (Hayes et al., 1991; McPhaden et al., 1998) under the Tropical Ocean and Global Atmosphere (TOGA) program (e.g., Ji et al., 1995). This change initially realized the operational monitoring of the ocean interior state in the equatorial Pacific using an ocean data assimilation system. However, since major oceanic data observed by Autonomous Temperature Line Acquisition System (ATLAS) buoys (mooring buoys that form the TAO array) were temperature profiles, ocean data assimilation systems developed in this early stage adopted the Optimal Interpolation (OI) or Three-Dimensional Variational Method (3DVAR) assimilating only temperature profiles. Salinity profiles were abandoned in those systems, although their number was not large. It should also be noted that these data assimilation systems directly modified only the model temperature fields; the model salinity field was modified only through adjustment by model physics. Development of schemes assimilating Sea Surface Height (SSH) data started after the launch of the TOPEX/Poseidon satellite, but it still focused on modifying the model temperature field for the first several years (e.g., Ji et al., 2000). Some studies, however, pointed out that appropriate treatment of salinity is essential for controlling model fields realistically, for the following two reasons. One is the effect of salinity variations on SSH and pressure fields through contribution to density variations. This issue was addressed by Cooper (1988); Ji et al. (2000); Maes (1998). In particular, Ji et al. (2000) demonstrated that the use of observed SSH in addition to in situ temperature data can increase analysis errors in the systems analyzing temperature alone without considering the salinity anomaly, because the spurious temperature anomaly is estimated in order to compensate the

The other reason is the possibility of destroying density stratification, which was first addressed by Woodgate (1997). Temperature and salinity decrease with depth in many areas of the tropical and subtropical oceans. The upward shift of water mass induces cold and low salinity anomalies in those areas. When assimilating temperature alone, the cold anomaly is reproduced but the low salinity anomaly is missed, resulting in the simulated greater density exceeding the actual density. This artificial density tends to destabilize the stratification and to induce spurious vertical mixing. This effect severely diminishes the salinity maximums in

Many studies have also stressed the importance of salinity variability in the equatorial Pacific. The surface salinity front existing in the western equatorial Pacific is considered a good indicator of the eastern edge of the warm water pool, and its position is directly connected to the advective-reflective oscillator theory (e.g., Picaut et al., 1997). Roemmich et al. (1994)

Formation of the barrier layer (the isothermal layer in which salinity is stratified; Lukas & Lindström, 1991) is another important feature associated with salinity there. This barrier layer can affect surface currents by thinning the mixed layer and concentrating the effect of wind stress (e.g., Vialard et al., 2002). It is also considered to induce a temperature rise in the mixed layer because it prevents warm water at the surface from mixing with cold water in the thermocline. This tendency is confirmed in the equatorial Pacific (e.g., Ando & McPhaden, 1997; Fujii et al., 2012; Maes et al., 2006). Some studies (e.g., Maes & Belamari, 2011) have further used CGCMs to confirm the impact of the barrier layer at the onset of El Niños.

suggested that the salinity front affects the current fields in the near-surface layer.

the subsurface layers in the tropical oceans (e.g., Troccoli et al., 2002).

**2. First and second generations of ocean data assimilation systems**

changes of the ocean observing system.

contribution of salinity to SSH.

In addition, the number of Argo floats rapidly increased after 2000, in order to meet the requirements of the Global Ocean Data Assimilation Experiment (GODAE) project (Clark et al., 2009). This second material change in the observing system urged to develop schemes for assimilating observed salinity profiles effectively in ocean data assimilation systems. In 2000, the name of the TAO array was also changed to TAO/Triangle Trans-Ocean Buoy Network (TRITON) array to designate the upgrading of ATLAS buoys west of 160◦E to TRITON buoys that contain more enriched equipment (Kuroda, 2002). However, many in situ observation platforms still exist, including the TAO/TRITON array, which provide little salinity data.

In order to use those observation data effectively along with the data of simultaneous observations of temperature and salinity by Argo floats and other sophisticated platforms, it is necessary to develop a method to guarantee consistency between temperature and salinity fields, even when only a small amount of salinity observation data exist. Thus, for the past decade, various schemes estimating salinity fields mainly from temperature and SSH through the T-S balance relationship have been developed based on OI or 3DVAR (summarized in Fujii et al., 2010).

For example, salinity fields are estimated using regression coefficients of their anomaly with the SSH anomaly, as well as temperature fields by Ezer & Mellor (1994); Kamachi et al. (2001). Some studies (e.g., Carton et al., 2000; Huang et al., 2008; Yan et al., 2004) uses climatological T-S relations typically calculated from the World Ocean Atlas (e.g., Locarnini et al., 2010). Vertical shifts of water masses in background (model) T-S profiles are applied in Haines et al. (2006); Ricci et al. (2005). Coupled T-S Empirical Orthogonal Function (EOF) modal decomposition is also employed to reconstruct vertical temperature and salinity profiles in Dobricic et al. (2005); Fujii & Kamachi (2003); Maes et al. (2000).

Thus, most current operational ocean data assimilation systems adopt OI or 3DVAR and have the capacity to assimilate observed salinity profiles imposing a multivariate (mainly T-S) balance relationship. These systems are called "second-generation" systems in Balmaseda et al. (2009; 2011), while systems that can assimilate temperature data alone are called "first-generation" systems. Balmaseda et al. (2011) demonstrated an example of improved ENSO forecasting from the first generation to the second generation. In the following section, we also confirm the advantage of second-generation systems.

It should be noted that data assimilation systems adopting more sophisticated schemes (e.g., ensemble Kalman filter or the adjoint method) have also been developed or started to use in operation (e.g., Keppenne et al., 2005; Weaver et al., 2003). These systems have the potential to improve the accuracy of monitoring and forecasting skills further.

#### **3. Capacity of the second-generation system at JMA**

This section introduces MOVE/MRI.COM-G (Usui et al., 2006, hereafter, MOVE-G) as a typical second-generation ocean data assimilation system, and discusses the capacity improvement of second-generation systems based on assimilation runs of MOVE-G. This system is also employed in the studies of Sections 4 and 5.

MOVE-G is composed of an Ocean General Circulation Model (OGCM) based on the MRI Community Ocean Model (MRI.COM; Tsujino et al., 2010), and the Multivariate Ocean Variational Estimation System (MOVE), an analysis scheme based on Fujii & Kamachi (2003). The OGCM has a nearly global domain extending from 75◦S to 75◦N. The grid spacing is 1◦ in the zonal direction, and changes from 0.3◦ (within 5.7◦S - 5.7◦N) to 1◦ (poleward of 16◦S and 16◦N) in the meridional direction. It has 50 levels in the vertical direction. The first seven levels are 1, 3, 6, 10, 16, 22, and 30 m in depth, and the interval between 30 and 200 m depth is

data). The term *Jadd* is the additional nonlinear constraint for avoiding density inversion (Fujii

Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting 79

quality control procedure introduced in Fujii et al. (2005). The cost function is non-quadratic for **w**, and the calculation of **g** includes inversion of the non-diagonal matrix **B***m*. In MOVE-G, we adopt the preconditioned quasi-Newton method introduced in Fujii (2005) to minimize

The 3DVAR analysis explained above is performed once every assimilation cycle using all available observations in the term of the cycle, and the result is reflected in the model fields by Incremental Analysis Updates (IAU: Bloom et al., 1996). The first guess for the analysis is given as a weighted mean of the climatology and the model-prediction for the middle time of the cycle from the assimilation result at the end of the previous cycle. The difference between the analysis and the model-prediction (i.e., analysis increment) is applied to correct the temperature and salinity fields in the model. Current fields are adjusted to the corrected temperature and salinity fields through the model dynamics, and thus establish the

An online model-bias estimation using the one-step bias-correction algorithm (Balmaseda et al., 2007) can be applied with IAU in MOVE-G. The bias estimates are subtracted from the model-prediction fields before calculating the first guess, and are updated by taking a

In addition, the SSH change due to the variation of the total fresh water mass in the global ocean, which is not taken into account in the OGCM, is estimated in the assimilation runs in Section 4. In the estimation, this globally constant value is regarded as a control variable of 3DVAR. This value is added to SDH calculated from the temporal analysis fields before subtracting the observed SSH. The term of the background constraint for the SSH change is also added to the cost function (3). The estimation in the analysis in the previous assimilation cycle is adopted as the first guess. We set a small value for the prescribed error variance of the

In situ temperature and salinity profiles, satellite SSH anomaly data, and observation-based gridded Sea Surface Temperature (SST) data are assimilated into the model in MOVE-G. The temperature and salinity profiles employed in the assimilation runs in this study are collected from WOD01, GTSPP, and the data of the TAO/TRITON array. Profiles of Argo floats are included in GTSPP. The SSH data is the along-track data from TOPEX/Poseidon, Jason-1, ERS-1/2, and ENVISAT, extracted from Ssalto/Duacs delayed-time multimission altimeter products (CLS, 2004). Centennial in-situ Observation-Based Estimates of the variability of SST and marine meteorological variables (COBE-SST; Ishii et al., 2005), or the gridded SST data compiled in JMA, are also assimilated in the assimilation runs in Sections 4 and 5. In the previous section, we indicated that state-of-the-art (second-generation) operational ocean data assimilation systems adopt schemes in which consistency between temperature and salinity is assumed. MOVE-G also improves the accuracy of the salinity fields by establishing an adequate relationship between temperature and salinity through the coupled T-S EOF modes. In particular, variation of salinity coupled with that of temperature can be

weighted mean of its original and analysis increment in every assimilation cycle.

∗**R***h*

*h* is the adjoint code of the nonlinear operator H*h*. We also apply the variational

−1 {H*h*(**x**) − **y***h*}

+ ∇*Jadd* , (4)

**H***T***R**−1 (**Hx** − **y**) + **H***h*

this non-quadratic function without directly implementing the inversion.

et al., 2005).

where **H**∗

**g** = ∑ *m* ∑ *l* **B**−1

The gradient of the cost function is written as

geostrophic balance in most areas.

first guess in order to vary the value slowly.

*m* **w***m*,*l* + **G***T*

10 m. A generalized enstrophy-preserving scheme and a scheme that involves the concept of diagonally upward/downward mass momentum fluxes along the sloping bottom (Ishizaki & Motoi, 1999) are applied for momentum advection. Isopycnal diffusion (Redi, 1982), isopycnal thickness diffusion (Gent & McWilliams, 1990), and the vertical mixing scheme of Noh & Kim (1999) are also adopted.

The analysis scheme MOVE adopts the 3DVAR method using vertical coupled T-S EOF modal decomposition for the background error covariance matrix. In this scheme, correction of temperature and salinity profiles to their first guess values, Δ**x***p*, is represented by a linear combination of the T-S EOF modes as follows:

$$
\Delta \mathbf{x}\_{l'} = \sum\_{l} w\_{l} \lambda\_{l} \mathbf{S} \mathbf{u}\_{l'} \tag{1}
$$

where *wl* is the amplitude of the *l*th EOF mode, *λl* is the singular value for the *l*th mode, **S** is the diagonal matrix composed of the standard deviations of temperature and salinity from the first guess, and **u***l* is the vector representing the *l*th mode. It should be noted that the vector Δ**x***p* contains the corrections of temperature and salinity at all model levels as its elements.

In MOVE-G, the model domain is partitioned into 40 horizontal subdomains, and the T-S EOF modes are calculated for each subdomain from historical profile data in the World Ocean Database 2001 (WOD01; Conkright et al., 2002) and the Global Temperature-Salinity Profile Program (GTSPP) database (Hamilton, 1994). The subdomains overlap in boundary areas. Correction of temperature and salinity profiles in the overlapping areas is calculated as a weighted sum,

$$\mathbf{x}\_p = \mathbf{x}\_p^f + \sum\_m a\_m \Delta \mathbf{x}\_{p,m} \tag{2}$$

where **x***p* is the analysis of the profiles, **x** *f p* is the first guess of the profiles, *m* is the counter of the subdomains, Δ**x***p*,*m* is the correction based on the EOF modes of the *m*th subdomain, and *am* is the weight of the *m*th subdomain that satisfies ∑*m a*2 *m* = 1 for avoiding the loss or gain of total variances by the area partition (Fukumori, 2002).

The mode amplitudes, *wl* in (1), are determined to minimize the cost function. The cost function, *J*(**w**), is defined as

$$J(\mathbf{w}) = \frac{1}{2} \sum\_{m} \sum\_{l} \mathbf{w}\_{m,l}^{T} \mathbf{B}\_{m}^{-1} \mathbf{w}\_{m,l} + \frac{1}{2} \left( \mathbf{H} \mathbf{x} - \mathbf{y} \right)^{T} \mathbf{R}^{-1} \left( \left( \mathbf{H} \mathbf{x} - \mathbf{y} \right) \right)$$

$$+ \frac{1}{2} \left[ \mathcal{H}\_{\hbar}(\mathbf{x}) - \mathbf{y}\_{\hbar} \right]^{T} \mathbf{R}\_{\hbar}^{-1} \left[ \mathcal{H}\_{\hbar}(\mathbf{x}) - \mathbf{y}\_{\hbar} \right] + I\_{\text{add}} \,\tag{3}$$

where **w** is the vector containing the amplitudes at all horizontal grid points for all subdomains, and **w***m*,*l* is the partial vector of **w** containing the amplitudes of the *l*th mode in the *m*th subdomain. The matrix **B***m* represents the horizontal correlation of background (first guess) errors for the *m*th subdomain modeled by the Gaussian function. The vector **y** is composed of temperature and salinity observations, and **y***h* is satellite SSH data. Vector **x** = **x** *f* + **Gw** is the state vector of temperature and salinity analysis fields composed of **x***p*, where **x** *f* is the first guess and **G** denotes the transformation represented by (1) and (2). Matrix **H** represents spatial interpolation for acquiring the values equivalent to the temperature and salinity observation, and H*h* is the nonlinear operator that includes calculating the Sea surface Dynamic Height (SDH) from gridded temperature and salinity data and interpolation. Matrix **R** (**R***h*) is the error covariance matrix for the temperature and salinity profiles (satellite SSH data). The term *Jadd* is the additional nonlinear constraint for avoiding density inversion (Fujii et al., 2005).

The gradient of the cost function is written as

4 Will-be-set-by-IN-TECH

10 m. A generalized enstrophy-preserving scheme and a scheme that involves the concept of diagonally upward/downward mass momentum fluxes along the sloping bottom (Ishizaki & Motoi, 1999) are applied for momentum advection. Isopycnal diffusion (Redi, 1982), isopycnal thickness diffusion (Gent & McWilliams, 1990), and the vertical mixing scheme of Noh & Kim

The analysis scheme MOVE adopts the 3DVAR method using vertical coupled T-S EOF modal decomposition for the background error covariance matrix. In this scheme, correction of temperature and salinity profiles to their first guess values, Δ**x***p*, is represented by a linear

where *wl* is the amplitude of the *l*th EOF mode, *λl* is the singular value for the *l*th mode, **S** is the diagonal matrix composed of the standard deviations of temperature and salinity from the first guess, and **u***l* is the vector representing the *l*th mode. It should be noted that the vector Δ**x***p* contains the corrections of temperature and salinity at all model levels as its elements. In MOVE-G, the model domain is partitioned into 40 horizontal subdomains, and the T-S EOF modes are calculated for each subdomain from historical profile data in the World Ocean Database 2001 (WOD01; Conkright et al., 2002) and the Global Temperature-Salinity Profile Program (GTSPP) database (Hamilton, 1994). The subdomains overlap in boundary areas. Correction of temperature and salinity profiles in the overlapping areas is calculated as a

*wlλl***Su***l* , (1)

*am*Δ**x***p*,*m* , (2)

*m* = 1 for avoiding the loss or gain

*p* is the first guess of the profiles, *m* is the counter of

*T* **R**−1 ((**Hx** − **y**))

*h* [H*h*(**x**) − **y***h*] + *Jadd* , (3)

Δ**x***p* = ∑ *l*

**x***p* = **x** *f*

*p* + ∑ *m*

the subdomains, Δ**x***p*,*m* is the correction based on the EOF modes of the *m*th subdomain, and

The mode amplitudes, *wl* in (1), are determined to minimize the cost function. The cost

1

2 (**Hx** − **y**)

*m* **w***m*,*l* +

*T* **R**−1

where **w** is the vector containing the amplitudes at all horizontal grid points for all subdomains, and **w***m*,*l* is the partial vector of **w** containing the amplitudes of the *l*th mode in the *m*th subdomain. The matrix **B***m* represents the horizontal correlation of background (first guess) errors for the *m*th subdomain modeled by the Gaussian function. The vector **y** is composed of temperature and salinity observations, and **y***h* is satellite SSH data. Vector **x** = **x** *f* + **Gw** is the state vector of temperature and salinity analysis fields composed of **x***p*, where **x** *f* is the first guess and **G** denotes the transformation represented by (1) and (2). Matrix **H** represents spatial interpolation for acquiring the values equivalent to the temperature and salinity observation, and H*h* is the nonlinear operator that includes calculating the Sea surface Dynamic Height (SDH) from gridded temperature and salinity data and interpolation. Matrix **R** (**R***h*) is the error covariance matrix for the temperature and salinity profiles (satellite SSH

(1999) are also adopted.

weighted sum,

combination of the T-S EOF modes as follows:

where **x***p* is the analysis of the profiles, **x** *f*

*J*(**w**) = 1

function, *J*(**w**), is defined as

*am* is the weight of the *m*th subdomain that satisfies ∑*m a*2

of total variances by the area partition (Fukumori, 2002).

2 ∑ *m* ∑ *l* **w***T m*,*l***B**−1

2 [H*h*(**x**) − **y***h*]

+ 1

$$\mathbf{g} = \sum\_{m} \sum\_{l} \mathbf{B}\_{m}^{-1} \mathbf{w}\_{m,l} + \mathbf{G}^{T} \left[ \mathbf{H}^{T} \mathbf{R}^{-1} \left( \mathbf{H} \mathbf{x} - \mathbf{y} \right) + \mathbf{H}\_{h}{}^{\*} \mathbf{R}\_{h}{}^{-1} \left\{ \mathcal{H}\_{h} (\mathbf{x}) - \mathbf{y}\_{h} \right\} \right] + \nabla f\_{\text{add}} \tag{4}$$

where **H**∗ *h* is the adjoint code of the nonlinear operator H*h*. We also apply the variational quality control procedure introduced in Fujii et al. (2005). The cost function is non-quadratic for **w**, and the calculation of **g** includes inversion of the non-diagonal matrix **B***m*. In MOVE-G, we adopt the preconditioned quasi-Newton method introduced in Fujii (2005) to minimize this non-quadratic function without directly implementing the inversion.

The 3DVAR analysis explained above is performed once every assimilation cycle using all available observations in the term of the cycle, and the result is reflected in the model fields by Incremental Analysis Updates (IAU: Bloom et al., 1996). The first guess for the analysis is given as a weighted mean of the climatology and the model-prediction for the middle time of the cycle from the assimilation result at the end of the previous cycle. The difference between the analysis and the model-prediction (i.e., analysis increment) is applied to correct the temperature and salinity fields in the model. Current fields are adjusted to the corrected temperature and salinity fields through the model dynamics, and thus establish the geostrophic balance in most areas.

An online model-bias estimation using the one-step bias-correction algorithm (Balmaseda et al., 2007) can be applied with IAU in MOVE-G. The bias estimates are subtracted from the model-prediction fields before calculating the first guess, and are updated by taking a weighted mean of its original and analysis increment in every assimilation cycle.

In addition, the SSH change due to the variation of the total fresh water mass in the global ocean, which is not taken into account in the OGCM, is estimated in the assimilation runs in Section 4. In the estimation, this globally constant value is regarded as a control variable of 3DVAR. This value is added to SDH calculated from the temporal analysis fields before subtracting the observed SSH. The term of the background constraint for the SSH change is also added to the cost function (3). The estimation in the analysis in the previous assimilation cycle is adopted as the first guess. We set a small value for the prescribed error variance of the first guess in order to vary the value slowly.

In situ temperature and salinity profiles, satellite SSH anomaly data, and observation-based gridded Sea Surface Temperature (SST) data are assimilated into the model in MOVE-G. The temperature and salinity profiles employed in the assimilation runs in this study are collected from WOD01, GTSPP, and the data of the TAO/TRITON array. Profiles of Argo floats are included in GTSPP. The SSH data is the along-track data from TOPEX/Poseidon, Jason-1, ERS-1/2, and ENVISAT, extracted from Ssalto/Duacs delayed-time multimission altimeter products (CLS, 2004). Centennial in-situ Observation-Based Estimates of the variability of SST and marine meteorological variables (COBE-SST; Ishii et al., 2005), or the gridded SST data compiled in JMA, are also assimilated in the assimilation runs in Sections 4 and 5.

In the previous section, we indicated that state-of-the-art (second-generation) operational ocean data assimilation systems adopt schemes in which consistency between temperature and salinity is assumed. MOVE-G also improves the accuracy of the salinity fields by establishing an adequate relationship between temperature and salinity through the coupled T-S EOF modes. In particular, variation of salinity coupled with that of temperature can be

(a) MOVE-G 1GE (b) MOVE-G 2GE-T (c) MOVE-G VAL (d) TRITON BUOY

Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting 81

2001 2003 2005 2007

2001 2003 2005 2007

0-156E

5N-156E

2001 2003 2005 2007

5S-156E

2001 2003 2005 2007

0-156E

5N-156E

2001 2003 2005 2007

5S-156E

2001 2003 2005 2007

2001 2003 2005 2007

0-156E

5N-156E

2001 2003 2005 2007

5S-156E

2001 2003 2005 2007

mixing due to modification of only the model temperature field.

reveals that spurious mixing actually causes a cold bias.

suppresses spurious mixing and reduces the cold bias.

imposing the T-S balance relationship.

33.6 33.8 34.0 34.2 34.4 34.6 34.8 35.0 35.2 35.4 35.6 35.8

Figure 2(a) indicates that MOVE-G 1GE has a colder temperature field than MOVE-G VAL in the entire equatorial Pacific. This colder temperature is also induced by spurious vertical

We further calculate statistics for the accuracy of temperature fields in the equatorial Pacific for MOVE-G 1GE and MOVE-G VAL. The reference for the statistics is the data of the profiling floats in 2◦S-2◦N, 130◦E-80◦W that are withheld in the assimilation runs. The result (Table 1)

Table 1 also indicates that imposing a T-S balance relationship in the second-generation system resolves this problem. The difference between the mean temperature field of MOVE-G 2GE-T and that of MOVE-G VAL (Fig. 2(b)) also implies that imposing the T-S relationship effectively

Smaller Root Mean Square Differences (RMSDs) and larger Anomaly Correlation Coefficients (ACCs) suggest that not only the mean state but also the variability of the temperature field in the equatorial Pacific is improved in MOVE-G VAL. The accuracy of temperature and salinity fields in the equatorial Pacific is thus improved in second-generation systems, as a result of

Fig. 1. Comparison of salinity in (a) MOVE-G 1GE, (b) MOVE-G 2GE-T, and (c) MOVE-G VAL, with (d) salinity profiles observed by TRITON buoys at 5◦N-156◦E (top), 0◦-156◦E

5S-156E

2001 2003 2005 2007

(middle), and 5◦S-156◦E (bottom).

2001 2003 2005 2007

0-156E 2001 2003 2005 2007

5N-156E

300

0

300

0

300

200

100

Depth

200

100

Depth 200

100

Depth 0

estimated through the coupled T-S EOF modes, even if little salinity data is available (Fujii & Kamachi, 2003).

Here, we compare the temperature and salinity fields in three assimilation runs in order to demonstrate the difference between the first- and second-generation ocean data assimilation systems. One is the assimilation run named MOVE-G VAL in Fujii et al. (2012). The atmospheric reanalysis dataset produced by the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP-R1; Kalnay et al., 1996) is employed as the external forcing in MOVE-G VAL. Online model-bias estimation is applied in this assimilation run. The length of the assimilation cycle is set to one month. The gridded SST data are not assimilated.

In addition, several profiles of temperature and salinity are excluded from the assimilated data in order to use them as independent reference data. The excluded profiles are those observed by TRITON buoys positioned at 5◦N-156◦E, 0◦-156◦E, and 5◦S-156◦E; the profiles of Argo floats whose last digit of the World Meteorology Organization (WMO) ID is "4"; and the profiles whose position and date are similar to those of one of the above excluded profiles (within 0.1◦ in longitude and latitude, and on the same date).

In another assimilation run, MOVE-G 1GE, only temperature observations are assimilated, and salinity increments estimated from the temperature data through T-S EOFs are not applied to correct the model salinity field. Thus, the model salinity field is just adjusted to the corrected temperature field through the model physics in MOVE-G 1GE. This run is equivalent to those of the first-generation systems, while MOVE-G VAL can be considered an assimilation run of the second-generation system. Observation data other than temperature are not assimilated in the other run, MOVE-G 2GE-T, either; however, the salinity increments calculated through T-S EOFs are applied to correct the model salinity fields there.

The settings of the assimilation system for MOVE-G 1GE and MOVE-G 2GE-T, other than those described above, are the same as those for MOVE-G VAL. The observation data withheld in MOVE-G VAL are also withheld in MOVE-G 1GE and MOVE-G 2GE-T. We analyze the results of these assimilation runs in the period of 1993–2008.

Validation of salinity using the data observed by TRIRON buoys deployed along 156◦E (Fig. 1) clearly indicates that using T-S EOFs improves the salinity field even if salinity observations are not assimilated. The subsurface salinity maximum is apparently diminished at 0◦ and 5◦S in MOVE-G 1GE. The variations of near-surface fresh water at these positions are not estimated satisfactorily in this run. The subsurface salinity maximum is smoothed out vertically at 5◦N. These errors stem from the spurious vertical mixing induced by the breakdown of the T-S relation due to modifying the temperature field alone (Troccoli et al., 2002).

The subsurface low salinity biases at 0◦ and 5◦S are removed, and the variations of the surface fresh water are fairly estimated in MOVE-G 2GE-T due to the T-S EOFs. The subsurface salinity at 5◦N is also improved, although the contrast of the subsurface salinity between 0◦ and 5◦N is slightly underestimated.

Assimilating salinity and SSH improves the salinity field further. The subsurface salinity contrast between 0◦ and 5◦N is improved, and appearance of water whose salinity exceeds 35.4 at 0◦ is properly estimated in MOVE-G VAL. It should be noted that the observation data of these TRIRON buoys are not assimilated as well in MOVE-G VAL, as in the other two assimilation runs.

6 Will-be-set-by-IN-TECH

estimated through the coupled T-S EOF modes, even if little salinity data is available (Fujii &

Here, we compare the temperature and salinity fields in three assimilation runs in order to demonstrate the difference between the first- and second-generation ocean data assimilation systems. One is the assimilation run named MOVE-G VAL in Fujii et al. (2012). The atmospheric reanalysis dataset produced by the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP-R1; Kalnay et al., 1996) is employed as the external forcing in MOVE-G VAL. Online model-bias estimation is applied in this assimilation run. The length of the assimilation cycle is set to one month. The gridded SST

In addition, several profiles of temperature and salinity are excluded from the assimilated data in order to use them as independent reference data. The excluded profiles are those observed by TRITON buoys positioned at 5◦N-156◦E, 0◦-156◦E, and 5◦S-156◦E; the profiles of Argo floats whose last digit of the World Meteorology Organization (WMO) ID is "4"; and the profiles whose position and date are similar to those of one of the above excluded profiles

In another assimilation run, MOVE-G 1GE, only temperature observations are assimilated, and salinity increments estimated from the temperature data through T-S EOFs are not applied to correct the model salinity field. Thus, the model salinity field is just adjusted to the corrected temperature field through the model physics in MOVE-G 1GE. This run is equivalent to those of the first-generation systems, while MOVE-G VAL can be considered an assimilation run of the second-generation system. Observation data other than temperature are not assimilated in the other run, MOVE-G 2GE-T, either; however, the salinity increments calculated through

The settings of the assimilation system for MOVE-G 1GE and MOVE-G 2GE-T, other than those described above, are the same as those for MOVE-G VAL. The observation data withheld in MOVE-G VAL are also withheld in MOVE-G 1GE and MOVE-G 2GE-T. We analyze the

Validation of salinity using the data observed by TRIRON buoys deployed along 156◦E (Fig. 1) clearly indicates that using T-S EOFs improves the salinity field even if salinity observations are not assimilated. The subsurface salinity maximum is apparently diminished at 0◦ and 5◦S in MOVE-G 1GE. The variations of near-surface fresh water at these positions are not estimated satisfactorily in this run. The subsurface salinity maximum is smoothed out vertically at 5◦N. These errors stem from the spurious vertical mixing induced by the breakdown of the T-S relation due to modifying the temperature field alone (Troccoli et al.,

The subsurface low salinity biases at 0◦ and 5◦S are removed, and the variations of the surface fresh water are fairly estimated in MOVE-G 2GE-T due to the T-S EOFs. The subsurface salinity at 5◦N is also improved, although the contrast of the subsurface salinity between 0◦

Assimilating salinity and SSH improves the salinity field further. The subsurface salinity contrast between 0◦ and 5◦N is improved, and appearance of water whose salinity exceeds 35.4 at 0◦ is properly estimated in MOVE-G VAL. It should be noted that the observation data of these TRIRON buoys are not assimilated as well in MOVE-G VAL, as in the other two

(within 0.1◦ in longitude and latitude, and on the same date).

T-S EOFs are applied to correct the model salinity fields there.

results of these assimilation runs in the period of 1993–2008.

Kamachi, 2003).

data are not assimilated.

2002).

assimilation runs.

and 5◦N is slightly underestimated.

33.6 33.8 34.0 34.2 34.4 34.6 34.8 35.0 35.2 35.4 35.6 35.8

Fig. 1. Comparison of salinity in (a) MOVE-G 1GE, (b) MOVE-G 2GE-T, and (c) MOVE-G VAL, with (d) salinity profiles observed by TRITON buoys at 5◦N-156◦E (top), 0◦-156◦E (middle), and 5◦S-156◦E (bottom).

Figure 2(a) indicates that MOVE-G 1GE has a colder temperature field than MOVE-G VAL in the entire equatorial Pacific. This colder temperature is also induced by spurious vertical mixing due to modification of only the model temperature field.

We further calculate statistics for the accuracy of temperature fields in the equatorial Pacific for MOVE-G 1GE and MOVE-G VAL. The reference for the statistics is the data of the profiling floats in 2◦S-2◦N, 130◦E-80◦W that are withheld in the assimilation runs. The result (Table 1) reveals that spurious mixing actually causes a cold bias.

Table 1 also indicates that imposing a T-S balance relationship in the second-generation system resolves this problem. The difference between the mean temperature field of MOVE-G 2GE-T and that of MOVE-G VAL (Fig. 2(b)) also implies that imposing the T-S relationship effectively suppresses spurious mixing and reduces the cold bias.

Smaller Root Mean Square Differences (RMSDs) and larger Anomaly Correlation Coefficients (ACCs) suggest that not only the mean state but also the variability of the temperature field in the equatorial Pacific is improved in MOVE-G VAL. The accuracy of temperature and salinity fields in the equatorial Pacific is thus improved in second-generation systems, as a result of imposing the T-S balance relationship.

Possibly, oceanic temperature and salinity information from the TAO/TRITON array and profiling floats is mostly redundant. If so, part of the cost for the observing platforms is abandoned. Therefore, showing the complementary effects of the array and the floats is vital for sustaining these observation platforms. In particular, the impact of the observing platforms on SI forecasting is important information for administrators of the observing system, because SI forecasting is one of the most influential products of ocean observation

Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting 83

The complementary impacts of TAO/TRITON array and profiling floats have already been demonstrated by an Observing System Experiment (OSE) using the seasonal forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF) (Balmaseda & Anderson, 2009). The results of OSEs, however, depend greatly on the forecasting system. Therefore, we also perform OSEs continuously in order to examine the impact of assimilating oceanic temperature and salinity data of the TAO/TRITON array and profiling floats through MOVE-G in the JMA seasonal and ENSO forecasting system. It should be noted that this study does not evaluate the atmospheric data and oceanic current data of the TAO/TRITON array. Our previous results are briefly introduced in Balmaseda et al. (2009). In this section,

The OSE configuration is as follows. First, we perform three ocean data assimilation runs (ALL, XTT, and XAF) from 2000 to 2008 using MOVE-G. All available data in the regular observation dataset are assimilated in ALL. Data of the TAO/TRITON array are withheld in

The three assimilation runs use the same setting except for the assimilated data. The period of the assimilation cycle is 10 days, the same as in JMA's operational system. The Japanese 25-year Reanalysis (JRA-25; Onogi et al., 2007) is used for estimating the atmospheric forcing. The original period of JRA-25 is 1979 to 2004, but it is extended to the present time by adding the product of the JMA Climate Data Assimilation System (JCDAS). The online model-bias estimation is not applied here. However, we applied the estimation of the SSH change due to the variation of the total fresh water mass in the global ocean. COBE-SST is assimilated in all

Thirteen-month 11-member ensemble forecasts are then performed with JMA/MRI-CGCM, the CGCM adopted in the forecasting system (Takaya et al., 2010; Yasuda et al., 2007), from 31 January, 26 April, 30 July, and 28 October during the 5 years of 2004–2008. We thus have 20 forecasts. Analysis fields of ALL, XTT, and XAF are used as the initial conditions of the ocean component in these forecasts. The atmospheric fields of JRA-25 are used for the initial condition of the atmospheric component. The flux correction procedure used in the JMA

In order to generate ensemble members, we put small perturbations on the observation data of the gridded SST in the 10-day assimilation cycle just before the start of a forecast. Although we applied perturbations in the same shape as the regression map of SST to the NINO4 index (see Table 2), we found in a preliminary study that forecast results are not sensitive to the

We calculate the ensemble mean of horizontal monthly mean fields for several atmospheric and oceanic parameters from all the forecast results and convert them to data in a 2.5◦-interval grid, although only the results for SST, Sea Level Pressure (SLP), Velocity Potential on the 200 hPa surface (VP200), and Outgoing Longwave Radiation (OLR) are presented here. Forecast biases are estimated by averaging the deviations of forecast values from their references separately for each lead time; each forecast month; and each of ALL, XTT, and XAF. The

shape of the perturbation if the scale is as small as we applied in this study.

data.

we introduce our recent OSE results.

the assimilation runs.

XTT, and data of the profiling floats are withheld in XAF.

operation is applied in all forecast calculations.

Fig. 2. Mean temperatures (contour) of (a) MOVE-G 1GE and (b) MOVE-G 2GE-T in 1993–2008 and their differences from the mean of MOVE-G VAL (shading) in the Pacific equatorial section. Units in ◦C.

Table 1. Statistics associated with the accuracy of temperatures at the fixed depths for MOVE-G 1GE (1GE) and MOVE-G VAL (VAL) in the equatorial Pacific. BIAS and RMSD: Averaged difference and RMSD between 1GE/VAL and reference values. ACC: Correlation coefficient between anomalies of 1GE/VAL and reference values. Anomalies are calculated as the deviation from the World Ocean Atlas 2009 (Locarnini et al., 2010). Values equivalent to the reference for the assimilation runs are calculated by spatial and temporal interpolations from the monthly mean data.

#### **4. Impact of ocean observation data on ENSO forecasting**

In this section, we demonstrate the benefits of assimilating ocean observation data through the ocean data assimilation system in ENSO and seasonal forecasting. The impact of oceanic temperature and salinity observed by the TAO/TRITON array and profiling floats (most deployed as Argo floats) on the SI forecasting is examined in order to show the benefits. Temperature profiles of the TAO array were clearly the most influential data on ENSO forecasting before 2000. However, the recent increase of Argo floats is likely to reduce the influence of the TAO/TRITON array, since both observe the subsurface temperature field in the tropical Pacific.

8 Will-be-set-by-IN-TECH

(b) (MOVE-G 2GE-T) - (MOVE-G VAL)

150E 180 150W 120W 90W

(a) (MOVE-G 1GE) - (MOVE-G VAL)

150E 180 150W 120W 90W

100

0

200

300

Depth

400

500

equatorial section. Units in ◦C.

interpolations from the monthly mean data.

the tropical Pacific.

Bias (◦C) RMSD (◦C) ACC Depth 1GE VAL 1GE VAL 1GE VAL 10m -0.134 0.029 0.572 0.487 0.795 0.828 50m -0.120 0.079 0.620 0.553 0.860 0.880 100m -0.471 0.030 2.386 1.121 0.561 0.645 150m -0.584 0.120 1.893 1.536 0.522 0.651 200m -0.350 0.129 1.391 1.202 0.447 0.553 300m -0.259 -0.116 0.557 0.410 0.247 0.548

Fig. 2. Mean temperatures (contour) of (a) MOVE-G 1GE and (b) MOVE-G 2GE-T in 1993–2008 and their differences from the mean of MOVE-G VAL (shading) in the Pacific

Table 1. Statistics associated with the accuracy of temperatures at the fixed depths for MOVE-G 1GE (1GE) and MOVE-G VAL (VAL) in the equatorial Pacific. BIAS and RMSD: Averaged difference and RMSD between 1GE/VAL and reference values. ACC: Correlation coefficient between anomalies of 1GE/VAL and reference values. Anomalies are calculated as the deviation from the World Ocean Atlas 2009 (Locarnini et al., 2010). Values equivalent

In this section, we demonstrate the benefits of assimilating ocean observation data through the ocean data assimilation system in ENSO and seasonal forecasting. The impact of oceanic temperature and salinity observed by the TAO/TRITON array and profiling floats (most deployed as Argo floats) on the SI forecasting is examined in order to show the benefits. Temperature profiles of the TAO array were clearly the most influential data on ENSO forecasting before 2000. However, the recent increase of Argo floats is likely to reduce the influence of the TAO/TRITON array, since both observe the subsurface temperature field in

to the reference for the assimilation runs are calculated by spatial and temporal

**4. Impact of ocean observation data on ENSO forecasting**

Possibly, oceanic temperature and salinity information from the TAO/TRITON array and profiling floats is mostly redundant. If so, part of the cost for the observing platforms is abandoned. Therefore, showing the complementary effects of the array and the floats is vital for sustaining these observation platforms. In particular, the impact of the observing platforms on SI forecasting is important information for administrators of the observing system, because SI forecasting is one of the most influential products of ocean observation data.