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Keywords:

  • surface latent heat flux;
  • heat flux-SST relation;
  • CFS

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Mean and Interannual Variability
  5. 3. Latent Heat Flux-SST Correlation
  6. 4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean
  7. 5. Summary
  8. Acknowledgments
  9. References

[1] The present study evaluates surface latent heat flux (LHF) and its relationship with sea surface temperature (SST) in the NCEP Climate Forecast System (CFS) simulations and retrospective forecasts. Mean LHF in the CFS is higher than satellite estimates in the Tropics due to a dry bias in surface air humidity. The SST forced Global Forecast System simulation produces larger interannual variability of LHF compared to the CFS coupled simulation. The LHF-SST correlation in the CFS simulation and retrospective forecasts shows large differences from observations in the eastern equatorial Indian Ocean and western-central equatorial Pacific due to an excessive SST dependence in the sea-air humidity difference in relation to a low bias in surface air humidity. In the eastern equatorial Indian Ocean, the CFS simulation disagrees with observations regarding the role of LHF in the development of SST anomalies during boreal summer.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Mean and Interannual Variability
  5. 3. Latent Heat Flux-SST Correlation
  6. 4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean
  7. 5. Summary
  8. Acknowledgments
  9. References

[2] Surface heat fluxes are important quantities in atmosphere-ocean interactions and play important roles in climate variability. Currently, model simulations and forecasts are widely used in studying climate variations. Inevitably, the models have limitations in various aspects, and it is necessary to know the sources of these limitations. For example, in a coupled model, do the atmosphere and ocean interact in a manner similar to reality? The nature of this interaction can be diagnosed by studying the latent heat flux (LHF)-sea surface temperature (SST) relationship [Frankignoul and Hasselmann, 1977; Barsugli and Battisti, 1998; Wu et al., 2006]. Thus, it is of interest to evaluate these relationships in model simulations and forecasts against available observations.

[3] The NCEP Climate Forecast System (CFS) is a fully coupled ocean-land-atmosphere dynamical seasonal prediction system. It has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of statistical methods [Saha et al., 2006]. After the CFS became operational in August 2004, its use in climate studies has increased rapidly. The CFS retrospective forecast data lend themselves to many studies that are not just related to seasonal forecasts. Thus, it is important to evaluate the performance of the CFS simulation and CFS forecasts.

[4] The present study compares mean and interannual variability of LHF and the LHF-SST correlation in a 50-year CFS simulation and in 24-year CFS retrospective forecasts [Saha et al., 2006] against proxies from observational datasets, including the Goddard Satellite-based Surface Turbulence Fluxes version 2 (GSSTF2) for the period 1988–2000 [Chou et al., 2003] and the NOAA optimum interpolation (OI) version 2 monthly mean SST starting from November 1981 [Reynolds et al., 2002]. Validation of GSSTF2 daily LHF against collocated measurements of field experiments reveals a rather small negative bias of ∼−2.6 Wm−2 in the tropical oceans [Chou et al., 2003]. Note that previous intercomparison studies indicate discrepancies among different satellite-derived LHF products. A 30-year SST forced simulation of the atmospheric model of the CFS, i.e., the Global Forecast System (GFS), is also analyzed to give information about how different the coupled and forced simulations are regarding the LHF variability and the LHF-SST relationship. The SST forcing for the forced simulation is from the CFS simulation. Recently, an objectively analyzed air-sea fluxes (OAFlux) dataset for the global oceans is produced by a synthesis of satellite retrievals, ship reports, and atmospheric model reanalysis products using a variational objective analysis [Yu et al., 2004]. This OAFlux dataset for the period 1981–2002 is included in the present study for cross-validation.

2. Mean and Interannual Variability

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Mean and Interannual Variability
  5. 3. Latent Heat Flux-SST Correlation
  6. 4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean
  7. 5. Summary
  8. Acknowledgments
  9. References

[5] In GSSTF2 (Figure 1a), large mean LHF is seen in trade wind belts and in the western boundary warm current regions of Kuroshio and Gulf Stream due to high surface winds coupled with large sea-air humidity differences [Chou et al., 2003; Feng and Li, 2006]. The LHF is also large in the Arabian Sea and the Bay of Bengal region in relation to the monsoon activity. Small LHF is seen in the eastern equatorial Pacific and Atlantic due to weak winds and upwelling-induced cold SSTs, and in high latitudes due to poleward decrease of SSTs [Chou et al., 2003; Feng and Li, 2006]. The LHF is also small in the equatorial eastern Indian Ocean-western Pacific warm pool region, mainly due to weak surface winds.

image

Figure 1. (a) Mean LHF (Wm−2) from GSSTF2, (b) CFS simulation minus GSSTF2 differences of mean LHF (Wm−2), (c) mean surface wind speed (m/s), and (d) mean sea-air humidity difference (g/kg). The contour interval is 20 Wm−2 in Figure 1a, 10 Wm−2 in Figure 1b, 0.5 m/s in Figure 1c, and 0.5 g/kg in Figure 1d with zero contours suppressed.

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[6] Mean LHF from OAFlux (not shown) is larger in the equatorial Indian Ocean-western Pacific, eastern equatorial Pacific, north of equatorial Atlantic, west coast of the North Pacific and North Atlantic, and smaller in trade wind belts and mid-latitudes compared to GSSTF2. The distribution of the mean LHF difference between OAFlux and GSSTF2 is similar to that between the NCEP reanalysis and GSSTF2 [Feng and Li, 2006, Figure 1c].

[7] The CFS simulation produces much larger LHF in the equatorial Indian Ocean-western Pacific, eastern equatorial Pacific, eastern tropical North Pacific, equatorial Atlantic, north of equatorial Atlantic, and in the western boundary current regions of Kuroshio and Gulf Stream (Figure 1b). The difference of mean LHF in these regions exceeds 30 Wm−2. In trade wind belts, the CFS simulation produces smaller LHF, especially in the South Pacific where the difference reaches about 20–30 Wm−2.

[8] Higher LHF in the warm pool, cold tongue, and warm current regions is due to larger sea-air humidity difference (Figure 1d). This, in turn, is attributed to lower surface air humidity (not shown). Higher LHF in the Bay of Bengal and northern Arabian Sea is due to higher wind speed (Figure 1c). The wind speed in trade wind belts is weaker in the CFS, leading to smaller LHF in the subtropical South Indian and Pacific Oceans. In the mid-ocean part of the Pacific Inter-tropical Convergence Zone and western tropical North Atlantic, the effects of weaker trade winds are nearly cancelled by the effects of larger sea-air humidity difference.

[9] Large LHF variability tends to occur in regions of high mean LHF because high mean wind speed and large sea-air humidity difference not only leads to high mean LHF, but also favors large LHF variability. In GSSTF2 (Figure 2a), the variability is large over subtropical regions and western boundary warm current regions. The variability is small in eastern tropical Atlantic, eastern equatorial Pacific, and in high latitudes. The variability is relatively low in the equatorial eastern Indian Ocean and western Pacific. The variability of OAFlux LHF (not shown) is smaller than that of GSSTF2 in most regions. The largest difference is seen in western-central tropical Pacific, western tropical Atlantic, and north Indian Ocean where the standard deviation of OAFlux LHF is only about 60% of that of GSSTF2.

image

Figure 2. (a) Standard deviation of monthly mean LHF anomalies (Wm−2) from GSSTF2, (b) the CFS coupled simulation, and (c) the GFS forced simulation. The contour interval is 4 Wm−2.

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[10] Compared to GSSTF2, the CFS simulation (Figure 2b) displays larger LHF variability in the equatorial Indian Ocean-western Pacific, coastal southeast China, tropical eastern North Pacific, and tropical eastern North Atlantic. This is related to larger mean sea-air humidity difference in the CFS (Figure 1d). The larger variability in high latitudes may be due to higher mean wind speed (Figure 1c) and larger wind speed variability in the CFS. The variability in trade wind belts is smaller in the CFS compared to GSSTF2, which is related to weaker mean wind speed (Figure 1c) and smaller variability of sea-air humidity difference in the CFS.

[11] Compared to the CFS coupled simulation, the SST forced GFS simulation (Figure 2c) displays larger variability globally. The most pronounced variability increase is seen in the tropical Indian Ocean-western Pacific, eastern tropical North Pacific and Atlantic. The increase of variability in these regions reaches 40%. This increase is due to the lack of atmospheric negative feedback in the forced simulation, which increases the persistence of atmospheric anomalies and leads to excessively large seasonal mean rainfall and surface LHF anomalies [Wu and Kirtman, 2005]. This is most pronounced in regions of warm SST and high mean rainfall where the atmospheric internal dynamics is active and contributes to SST variations.

[12] The individual CFS retrospective forecast displays a spatial distribution of LHF variability similar to the CFS coupled simulation (not shown). The difference in the magnitude of the LHF variability is within 20% in most regions. An increase over 20% is seen the eastern tropical Indian Ocean, the western North Pacific and Atlantic.

[13] The CFS forecast ensemble shows significantly reduced LHF variability, presumably due to ensemble averaging (not shown). The ensemble mean LHF variability is less than 60% of individual member in most of the regions. This suggests that the LHF has a large component of high frequency variations driven by atmospheric internal dynamics. The effects are smallest in the eastern equatorial Pacific, equatorial Atlantic, and mid-latitude western North Atlantic where the ocean forcing of the atmosphere dominates. Compared to GSSTF2, the LHF variability in the CFS ensemble forecast is much smaller in most regions.

3. Latent Heat Flux-SST Correlation

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Mean and Interannual Variability
  5. 3. Latent Heat Flux-SST Correlation
  6. 4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean
  7. 5. Summary
  8. Acknowledgments
  9. References

[14] The local LHF-SST correlation is helpful for identifying the local air-sea relationship [Barsugli and Battisti, 1998; Wu et al., 2006]. Thus, comparison of the local correlation between CFS simulations and retrospective forecasts against observations provides information about whether the CFS simulations and retrospective forecasts can capture the atmosphere-ocean interactions.

[15] In observations (Figure 3a), positive LHF-SST correlation is seen in the eastern equatorial Pacific and Atlantic, western North Pacific, western North Atlantic, tropical North Atlantic, southwest coast of Australia, and south of Africa. In the equatorial central-western Pacific and most of the tropical Indian Ocean, the correlation is negative. The LHF-SST tendency correlation is negative in mid-latitudes, equatorial central-western Pacific, north Indian Ocean, and coastal Sumatra [Wu et al., 2006, Figure 6c]. The above distribution of correlation indicates the dominance of oceanic forcing of the atmosphere in the eastern equatorial Pacific and Atlantic, and the dominance of atmospheric forcing of the ocean in mid-latitudes and the contribution of atmospheric forcing to SST variations in the eastern Indian Ocean-western Pacific. The OAFlux produces positive LHF-SST correlation in the equatorial western-central Pacific and tropical Indian Ocean (not shown), which is similar to the NCEP reanalysis [Wu et al., 2006, Figure 13], but is opposite to GSSTF2.

image

Figure 3. (a) Point-wise and simultaneous LHF-SST correlation derived from GSSTF2 and OI version 2 SST, (b) the CFS coupled simulation, (c) the GFS forced simulation, (d) an individual member of CFS forecasts of 1-month lead, and (e) CFS ensemble forecasts of 1-month lead. The contour interval is 0.1 with zero contours suppressed.

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[16] In the CFS coupled simulation (Figure 3b), the LHF-SST correlation displays both similarities to and differences from observations. The simulation captures the positive correlation in the eastern equatorial Pacific and Atlantic, and in the western North Pacific and North Atlantic, but misses the negative correlation in the equatorial western-central Pacific. In particular, the simulation produces a positive correlation in the coastal Sumatra-Java region, which is in sharp contrast with observations. This positive correlation suggests the dominance of oceanic forcing of the atmosphere, as in the eastern equatorial Pacific and Atlantic. This disagrees with observations. The LHF-SST relationship in this region will be discussed in the next section.

[17] The GFS forced simulation (Figure 3c) displays the dominance of positive LHF-SST correlation. This contrasts with the coupled simulation and indicates that the forced simulation produces spurious oceanic forcing of the atmosphere, consistent with previous studies [Wu et al., 2006].

[18] The CFS individual forecast (Figure 3d) displays LHF-SST correlation similar to the CFS simulation. There are, however, some regional differences. In the eastern tropical Indian Ocean, the positive LHF-SST correlation is limited in spatial coverage compared to the coupled simulation. When the forecast lead time increases, the positive correlation in the eastern Indian Ocean becomes more similar to the coupled simulation (not shown).

[19] In the CFS ensemble forecasts (Figure 3e), the LHF-SST correlation displays large differences from the individual forecast; the ensemble forecast has a much larger and broader positive correlation. This difference occurs because the ensemble averaging removes the high-frequency LHF variations that are weakly correlated with SST variations. The remaining low frequency LHF variations are largely induced by SST variations and thus have a positive correlation with SST. With the increase of forecast lead time, the spatial coverage of positive LHF-SST correlation is reduced in particular in mid-latitudes (not shown).

[20] The discrepancy in the LHF-SST correlation is mainly due to excessive dependence of sea-air humidity difference on SST in the CFS simulation and forecast. This is confirmed by comparing the correlation between the sea-air humidity difference and SST. In the CFS coupled simulation and retrospective forecasts, the correlation between sea-air humidity difference and SST is very high (correlation coefficient >0.7) (not shown). The corresponding correlation based on observations is below 0.5 except for eastern tropical Pacific and Atlantic. The discrepancy in the surface wind speed-SST correlation is relatively small.

4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Mean and Interannual Variability
  5. 3. Latent Heat Flux-SST Correlation
  6. 4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean
  7. 5. Summary
  8. Acknowledgments
  9. References

[21] Atmosphere-ocean interactions include various feedbacks. One important feedback is a positive wind-evaporation feedback on SST. The wind-evaporation feedback has been identified in several modes of climate variability, for example, the Indian Ocean dipole/zonal mode [Saji et al., 1999; Webster et al., 1999]. In this mode, the development of the eastern pole during boreal summer involves a positive wind-evaporation feedback [Wang et al., 2003; Wu and Kirtman, 2007]. Can the CFS simulation or retrospective forecasts capture the positive wind-evaporation feedback in the above region?

[22] To address this question, we have calculated monthly simultaneous correlation with respect to SST anomalies for the region of 0°–10°S, 90°–105°E (Figure 4). The discussions will focus on the boreal summer which is the time for the development of the dipole mode in observations. During this time, the SST tendency is positive corresponding to warm SST anomalies, which indicates the intensification of warm SST anomalies in this region. This feature is captured by the CFS simulation and CFS retrospective forecasts although the positive SST tendency appears about 1–2 months later in the CFS forecasts. In observations, the LHF is reduced due to a decrease in wind speeds. Thus, the wind-evaporation has a positive feedback on SST. In the CFS simulation, however, LHF anomalies are positive, i.e., surface evaporation has a damping effect on the existing warm SST anomalies, which is opposite to observations. This occurs because of the large positive correlation between sea-air humidity difference and SST. In the 1-mon lead CFS forecasts, LHF anomalies are weak because of the cancellation of sea-air humidity difference effects on the wind speed effects. When the forecast lead time increases to 7 months, LHF anomalies become positive as in the CFS simulation.

image

Figure 4. Area average of point-wise and simultaneous correlation (scale at left) of LHF (thick solid), surface wind speed (thin solid), sea-air humidity difference (thin dashed), and SST tendency (thick dashed) with respect to SST and ratio (dotted; scale at right) of standard deviation of surface air humidity over that of sea humidity averaged over the region of 5°S–5°N and 170°–90°W and derived from (a) GSSTF2 and OI version 2 SST, (b) the CFS coupled simulation, and CFS ensemble forecasts of (c) 1-month lead and (d) 7-month lead.

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[23] A common feature of the CFS simulation and retrospective forecasts is that the sea-air humidity difference follows closely the SST variation, whereas in observations the sea-air humidity difference is not as closely related to SST. This discrepancy occurs because the CFS has a dry bias in the eastern equatorial Indian Ocean that leads to smaller variability in the surface air humidity compared to that of sea humidity (Figures 4b–4d). As such, the sea-air humidity difference anomalies follow the sea humidity (or SST) anomalies. In observations, the variability of the air humidity is larger than that of the sea humidity (Figure 4a).

5. Summary

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Mean and Interannual Variability
  5. 3. Latent Heat Flux-SST Correlation
  6. 4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean
  7. 5. Summary
  8. Acknowledgments
  9. References

[24] The CFS mean LHF is higher than satellite estimates in the tropical Indo-western Pacific, tropical Atlantic, eastern tropical Pacific, and Kuroshio and Gulf Stream regions. This discrepancy is due to larger sea-air humidity difference. In the South Indian and Pacific Ocean trade wind belts, the CFS mean LHF is lower than satellite estimate due to weaker winds. The forced simulation produces a larger variability of LHF compared to the satellite estimation due to the lack of atmospheric negative feedback. The CFS ensemble forecasts have much smaller variability of LHF due to reduced high frequency variability.

[25] The CFS simulations and retrospective forecasts display large discrepancy from observations in the local LHF-SST correlation in the eastern equatorial Indian Ocean and western-central equatorial Pacific. This discrepancy is due to an excessively large contribution of sea-air humidity difference to the LHF-SST correlation. The ensemble averaging in retrospective forecasts significantly increases the LHF-SST correlation in mid-latitudes.

[26] The CFS simulation fails to capture the LHF-SST relationship in the eastern equatorial Indian Ocean. In observations, the wind-evaporation feedback contributes to the development of SST anomalies in the eastern pole of the Indian Ocean Dipole/Zonal Mode during boreal summer. In the CFS simulation, surface LHF acts as a damping term due to an excessive SST dependence in the sea-air humidity difference anomalies. While the short-lead CFS retrospective forecasts appear better than the CFS simulation in this aspect, this problem shows up when the forecast lead time increases.

[27] The discrepancies between the CFS and observations in the eastern Indian Ocean-western Pacific are related to a dry bias in the CFS. The dry bias leads to lower variability in the surface air humidity because the interaction between convection and circulation depends on the mean state. This effect is more pronounced in warm SST (large rainfall) regions where the convection-circulation interaction is strong and the atmosphere contributes to SST changes. This could involve the influence of both intraseasonal and interannual variations. As a result, the CFS underestimates the atmospheric forcing of SST, and overestimates the SST forcing of the atmosphere in the above regions. This suggests the importance of improving the simulation of mean moisture fields. The specific reasons for the dry bias remain to be uncovered.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Mean and Interannual Variability
  5. 3. Latent Heat Flux-SST Correlation
  6. 4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean
  7. 5. Summary
  8. Acknowledgments
  9. References

[28] This research was supported by grants from the NSF ATM-0332910, the NOAA NA04OAR4310034 and NA05OAR4311135, and the NASA NNG04GG46G.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Mean and Interannual Variability
  5. 3. Latent Heat Flux-SST Correlation
  6. 4. Latent Heat Flux-SST Relationship in the Eastern Equatorial Indian Ocean
  7. 5. Summary
  8. Acknowledgments
  9. References